Summary

Creative Solutions

• Information specific to the U.S. and market and enterprise AI agents was not available in reputable public sources. As an alternative, we provided the market size for the global AI agent market, covering the entire sector rather than just enterprise agents, given that North America and enterprise solutions represent a significant portion of the market.
• Similarly, the information specific to the UK and EU markets was not available. We provided a qualitative finding about the growth in those markets.
• Innovations were provided in two sections: one within the key players section, highlighting product launches and partnerships, and another offering a broader perspective on market trends.
• While we couldn't find detailed industry-specific adoption data due to the early stage of the technology, we included available insights on specific sectors and overall adoption trends. The information regarding key player capabilities is based on company claims.

Complete Findings

The AI Agent Market

• The global AI agents market is poised for significant expansion in the coming years. Estimates for the market size in 2023-2024 range between $3.86 billion and $5.1 billion. By 2030, projections place the market size between $47.1 billion and $50.31 billion, reflecting a compound annual growth rate (CAGR) of 44.8% to 45.1% over the 2024 to 2030 period.

 

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• Currently, North America holds over 40% of the global AI agents market, driven by the US' leadership in technology and early adoption of AI across various industries. Major companies like Google, Microsoft, and IBM are driving advancements in AI agents, supported by a strong startup ecosystem that focuses on AI innovation in sectors such as finance and education.
• In Europe, the AI agents market is expected to grow significantly over the next decade, driven by the UK and Germany. This growth is supported by government initiatives, including the European Union's AI strategy, which encourages research, innovation, and the ethical application of AI agents in various industries.
• The enterprise segment accounted for the largest market revenue share in 2023, but the industrial segment is anticipated to exhibit the highest CAGR over the forecast period.
• The machine learning segment currently leads the market, accounting for over 30% of global revenue. The deep learning segment is anticipated to exhibit the highest CAGR through 2030.
• Based on deployment type, cloud dominates the market, accounting for 53% of the total market share, driven by its flexibility, lower costs, and scalability compared to on-premise solutions.

• Ready-to-deploy agents hold the highest market share due to their lower costs, ongoing support, and minimal setup time compared to custom solutions, making them particularly appealing to small- and medium-sized companies. Many solutions offer intuitive interfaces requiring little technical knowledge, making them accessible to a broader range of users.
• The build-your-own agents segment is expected to show the highest CAGR, as they can be integrated with legacy systems, ensuring smoother workflows. Industries like healthcare and BFSI tend to favor build-your-own agents to maintain strict control over data and security protocols, enabling them to implement customized data handling and privacy measures.
• The single-agent systems segment currently holds the largest market share due to their ease of implementation and lower development costs compared to multi-agent systems. These solutions allow businesses to enhance efficiency quickly without extensive customization. However, multi-agent systems are expected to experience substantial growth in the next five years, driven by their ability to handle complex problem-solving, real-time decision-making, and improved collaboration across industries, particularly in applications like emergency response, where coordination among agents is essential.

 

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Market Growth Drivers

• The demand for automation, rising investments, a thriving startup ecosystem—particularly in the U.S.—and companies' willingness to adopt new solutions are key factors driving growth in the AI agent market.

• A key driver for the adoption of AI agents is the rising demand for automation solutions that enhance efficiency, scalability, and decision-making across various sectors. Businesses are increasingly under pressure to streamline operations while reducing costs, and AI agents present a viable solution by automating repetitive tasks, analyzing large datasets, and delivering real-time insights.
•  Seventy-one percent of organizations expect AI agents to drive automation, with most also believing agents will offload repetitive tasks from human operators, allowing employees to focus on more value-added activities.

 

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• In industries like investment banking and healthcare, AI agents are employed for data analysis and pattern detection, helping professionals make timely, accurate decisions. This automation not only boosts operational efficiency but also allows companies to scale their services without needing to increase their workforce, making AI agents essential for sustaining growth and competitiveness.

•  Unlike earlier software focused on low-level, sequential tasks. modern cognitive architectures enable dynamic, end-to-end automation. While platforms like UiPath and Zapier proved the demand for rules-based automation, scaling these systems exposed several limitations: the need for significant manual work in setup and maintenance, the fragility of UI automations, limited API integrations, and challenges in processing unstructured data. Traditional RPA and iPaaS solutions are constrained by deterministic architectures, even when incorporating LLMs.
• AI agents differ fundamentally from traditional RPA bots and earlier generation AI tools by serving as adaptive decision engines within application workflows. As AI agents mature and their capabilities grow, they will be increasingly capable of dynamic decision-making, complex reasoning, multi-step processes, and exception handling, unlike the static, hard-coded logic of RPA bots or retrieval-augmented generation (RAG) systems.
• For instance, in an invoice reconciliation workflow, an RPA bot may struggle with mismatches or edge cases, often escalating issues to human intervention. In contrast, AI agents can adapt to new data formats, execute multi-step investigations (e.g., scanning emails for price changes), and handle complex considerations like currency exchange and transaction timing. They are also robust against exceptions, leveraging contextual understanding to resolve issues autonomously, thus truly reducing reliance on human involvement.

• Capgemini found that organizations are "eager to adopt AI agents, with a strong majority (82%) intending to integrate them within 1–3 years." Security and trust concerns remain key barriers to widespread adoption of AI agents, but businesses of all sizes increasingly recognize the need to engage with the booming AI agent market to stay competitive and capitalize on advancements in automation, efficiency, and decision-making capabilities.
• Another recent survey found that 72% of decision-makers are open to engaging with startups for AI agent solutions, favoring them for their innovative and tailored offerings, as well as their speed and agility, which often surpass what incumbents can provide.

 

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• Recent reports indicate that AI agents are experiencing a "ChatGPT moment" as investors shift their focus beyond chatbots. Grace Isford, a partner at Lux Capital, noted a "dramatic increase" in investor interest in startups developing AI agents. Accel's latest Euroscape report also identifies enterprise AI agents as a major upcoming trend. Michael Treskow, a partner at London-based VC firm Eight Roads, highlights that investments are shifting towards AI agents that go beyond answering questions and are now performing tasks based on available information.
• AI agent startups are indeed attracting substantial investments, albeit not on the scale of billion-dollar financings seen with AI model companies, but valuations are still rising. Some recent examples:
• Adept, led by former OpenAI and Google employees, raised $350 million in 2023, with a valuation exceeding $1 billion. The company announced in June that it will focus exclusively on solutions that enable enterprise agentic AI.
• Cognition AI, the company developing the "first fully autonomous AI software engineer," raised $175 million in April 2024, led by Founders Fund, pushing the company's valuation to $2 billion.
• French startup H raised $220 million in a seed round backed by Amazon, Samsung, UiPath, and Eric Schmidt in May. Artisan AI, a Y Combinator-backed startup positioning AI agents as "AI employees for enterprise," secured $7.3 million in funding and has already onboarded over 100 companies.
• CrewAI has secured $18 million in funding, including backing from boldstart ventures and Insight Partners. The funding will be used to support the development and launch of its new Enterprise Cloud platform, which enables organizations to build and deploy AI agents tailored to their unique workloads. The platform builds on CrewAI’s existing open-source framework, which already powers over 10 million AI agents per month.
• DeepOpinion, a startup "specializing in enterprise agentic process automation," raised €11 million in a Series A funding round In September.

•  “Every major company is now doing [AI] agents,” states Ruslan Salakhutdinov, a computer scientist at Carnegie Mellon University who is working on AI agents. According to AI expert Andrew Ng, “AI agent workflows are going to drive tremendous progress in AI this year, probably more so than the next generation of foundational models. This is a significant trend, and I urge everyone working in AI to pay attention to it.” Research suggests that they are.
• Microsoft CEO Satya Nadella mentioned during an earnings call earlier this year that the company aims to provide an AI agent capable of handling more tasks on behalf of users, though he acknowledged that there is still "a lot of execution ahead." Similarly, executives from Meta and Google have highlighted their efforts to enhance the productivity of AI agents, signaling a broader industry push towards more capable and autonomous AI systems.

Innovations

• AI agents still face numerous challenges and limitations, such as memory, data quality and accuracy. However, recent innovations expanded the applicability of LLMs to more complex workflows, driving the AI agent landscape. Improvements in model accuracy, memory structures (both short and long-term), logic frameworks, and answer evaluation mechanisms have significantly enhanced their capabilities. LLMs now have the ability to self-evaluate and correct errors to some degree, and when combined with human oversight, they can deliver higher accuracy and productivity.
• Moreover, generative AI applications are transitioning from custom-built solutions to more standardized frameworks. This shift toward "packaged software" is improving implementation speed and cost efficiency, making it easier to deploy these technologies across a wider range of real-world use cases, including enterprise long-tail tasks.
• One of the most notable trends is the increased autonomy of AI agents. These agents are starting to become capable of making complex decisions and taking independent actions without the need for human input. This is particularly evident in industries like autonomous vehicles, robotics, and finance, where AI agents handle tasks such as navigation, operational management, and trading strategies, showcasing their ability to function without constant oversight.
• Enhanced NLP is another significant advancement. Improvements in NLP have allowed AI agents to better understand and generate human language, making virtual assistants, chatbots, and customer service agents more effective at handling complex interactions and responding to user queries with higher accuracy and nuance. For example, financial services company Klarna reported that its AI agent, powered by OpenAI, took over two-thirds of customer chats within its first month of use. During this period, the AI agent performed the equivalent work of 700 full-time agents.

 

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• Finally, human-AI collaboration is gaining momentum, as AI agents are increasingly designed to work alongside humans to enhance productivity and decision-making. This collaboration is particularly impactful in fields like healthcare, where AI agents support doctors in diagnosing diseases, recommending treatments, and analyzing patient data, leading to better outcomes and more informed decisions. For example, ServiceNow AI agents are designed to work with each other and humans to "proactively and predictly" resolve tasks.

Function-Specific Agents

• A report from May, which analyzed 94 AI agent companies, found that these tools generally fall into three main subsectors. The first category includes agents that focus on general tasks, such as improving workplace productivity. The second category consists of function-specific agents, which are designed to perform specialized jobs, like acting as sales development representatives. Lastly, the third category is industry-specific agents, which aim to automate various tasks within a specific profession.
• According to the report, function-specific agents are the ones expected to win over the market first. According to the report, these AI agents are essentially trained or instructed to handle tasks that are as clearly defined as a job description. This clarity in task definition is seen as a key factor driving excitement and progress in the field, as it allows these agents to function effectively in specific roles. One example of a function-specific AI agent gaining traction is 11x, a London-based startup that develops "digital workers"—AI agents designed to perform the duties of SDRs, effectively automating a key part of the sales process.
• 11x.ai, which completed a Series A funding round of $24 million and a Series B round of $50 million in 2024, is now valued at $350 million. The company currently offers two AI bots, referred to as "automated digital workers." The first, Alice, is an AI Sales Development Representative (SDR) designed to manage sales lead generation, conduct research, and handle customer outreach. Recently, 11x.ai introduced Jordan, an AI phone sales representative capable of speaking over 30 languages. Jordan can handle both inbound and outbound conversations with potential buyers, further expanding the company's AI-driven sales automation capabilities.

Multi-Agent Architectures

• While AI agents have traditionally been supportive tools, they are now evolving into independent entities capable of executing complex tasks with minimal human oversight. Multi-agent systems are expected to further enhance these capabilities by enabling AI agents to collaborate and solve tasks collectively, improving efficiency and productivity for enterprises.
• The technology is still in its infancy, but according to Cognizant, Multi-agent AI is “set to revolutionize enterprise operations.” The future of software will likely be shaped by these agent architectures, leading to the creation of AI agent marketplaces that allow dynamic integration and collaboration between specialized agents across different platforms.
• Multi-agent systems excel in complex tasks by utilizing specialized sub-agents, which enhance both accuracy and modularity. Although still in early development, multi-agent architectures are gaining traction in enterprise applications like customer service and software development, with both major tech companies and startups working on new frameworks and tools. Startups, including well-funded companies like Ema and Sierra, are building multi-agent architectures for industries like healthcare and customer service. Major tech companies, such as Microsoft, are also releasing multi-agent frameworks, fueling increasing media interest in 2024.

 

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• Despite the potential, widespread adoption faces challenges, including agent reliability and orchestration complexity. However, advancements indicate a future where AI teams, managed by AI systems and guided by humans, will handle increasingly intricate workflows.

Key players

• The AI agent landscape is highly fragmented and evolving rapidly, with a diverse mix of established incumbents and emerging startups driving innovation. Over the last few weeks, numerous companies have made announcements or launched new solutions that are moving the market.

 

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Salesforce

• On September 12, Salesforce announced Agentforce, a platform that integrates autonomous AI agents across its core clouds, including Sales, Service, Marketing, and Commerce. The platform includes new AI agents like Sales Qualification, Service Agents, and Campaign Optimizer, designed to assist with complex workflows such as customer engagement, sales support, and marketing automation.

 

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• At Dreamforce 2024, Salesforce emphasized that Agentforce moves beyond simple generative AI assistants (like Copilot) to fully autonomous agents capable of handling multi-step tasks. Central to this is the Atlas Reasoning Engine, which simulates human planning and decision-making to refine and execute user queries. Data Cloud serves as the platform’s core, unifying Salesforce applications and customer data.
• Agentforce offers native integrations with MuleSoft, Salesforce Flow, and Apex to further enhance agent functionality. Salesforce partners, including AWS, Google, and IBM, are already building agents for the platform. Agentforce is expected to compete with other AI solutions in the market, such as ServiceNow's Xanadu release and Google's AI agents, with some components launching as early as February 2025.

Microsoft

• Melius Research’s Ben Reitzes has identified Microsoft as an early driver in the development and adoption of AI agents. According to Reitzes, Microsoft is positioned to drive advancements in this space, leveraging its existing capabilities and investments in AI technology to accelerate the integration of AI agents across various applications.
• Microsoft reported that 60% of Fortune 500 companies are using its Copilot technology. Examples include Lumen Technologies, projecting annual savings of $50 million through AI-driven improvements for its sales teams, and Honeywell, which achieved productivity gains equivalent to adding 187 full-time employees​.
• On October 21, Microsoft introduced ten new autonomous agents in Dynamics 365, including agents for Sales Qualification, Order Processing, Supplier Communications, and Financial Reconciliation. The launch also featured Copilot Studio Preview, which allows companies to create custom AI agents without coding, backed by robust security and governance features.
• The company is positioning its autonomous agents as “apps for an AI-driven world,” distinguishing them from traditional chatbots by their ability to operate with minimal human intervention. The agents are designed to perform a variety of tasks, including handling client queries, identifying sales leads, and managing inventory.
• Internal tests showed significant improvements in several business functions through the use of Copilot and AI agents. A sales team achieved a 9.4% increase in revenue per seller and 20% more closed deals. Customer service saw a 12% faster case resolution, while the marketing team improved the Azure.com conversion rate by 21.5% using a custom AI agent. In Human Resources, an AI-powered self-service agent increased the accuracy of responses by 42%, demonstrating enhanced efficiency across departments​
• In a demo, McKinsey, which had early access to the tools, created an AI agent capable of managing client inquiries by checking interaction history, identifying the appropriate consultant for the task, and scheduling follow-up meetings.
• The new launches position Microsoft to compete directly with Salesforce's Agentforce. According to CNBC, "Microsoft is doubling down on AI agents at a time when competition is intensifying up in the red-hot artificial intelligence space, after Salesforce launched its own 'agentic' AI system last month."

Google

• In April 2024, Google Cloud introduced Vertex AI Agent Builder, a platform designed for creating and deploying no-code conversational AI agents. This tool is particularly focused on customer engagement, allowing businesses to build task-specific AI agents that can execute complex workflows across multiple systems. The agents are capable of natural voice interactions and grounding responses in enterprise data, enhancing their relevance and accuracy.
• Google Cloud’s Vertex AI Agent Builder introduces key features like a no-code console for building AI agents with natural language, integration with Gemini models, and the ability to ground outputs in enterprise data and Google Search. It also supports complex workflows through multi-agent linking and adheres to stringent compliance and security standards such as HIPAA and ISO 27000-series
• In September. the company launched a series of updates, including the new Gemini 1.5 Flash and 1.5 Pro models with a 2 million context window. These models feature grounding through Google Search, premade "Gems" in Google Workspace, and a range of AI agents focused on customer engagement and conversational tasks.
• At the Gemini at Work event, Google Cloud outlined how its agentic AI push integrates across its platform. The goal is to allow enterprises to leverage Gemini’s models, create custom AI agents, and deploy them with enterprise data grounding on optimized infrastructure. This unified stack facilitates seamless customer engagement solutions across web, mobile apps, call centers, and point-of-sale systems, using both chat and voice interaction.
• On October 21, Honeywell announced a partnership with Google to integrate Google’s AI-powered agents into its systems, aiming to automate tasks for engineers and assist technicians with maintenance issues. Honeywell emphasized that while generative AI is already in use within the industrial sector, this collaboration will elevate its application beyond current "gen AI point solutions" by linking Google AI with the Honeywell Forge IoT platform. “We’re moving from automation to autonomy,” said Suresh Venkatarayalu, Honeywell’s CTO and president of Honeywell Connected Enterprise, in a Google blog post. “Our goal is to provide AI agents that offer real-time support to workers, both on factory floors and in the field.”

IBM

• The IBM’s WatsonX.ai platform serves as an enterprise-grade AI studio aimed at building and deploying agentic services. It offers a comprehensive developer toolkit that supports the full AI development lifecycle. The platform provides flexible options, catering to both pro-code solutions for experienced developers and no-code platforms for business users, enabling a wide range of users to leverage its capabilities.
• On September 17, IBM and Salesforce announced a partnership to develop pre-built AI agents that integrate IBM WatsonX with Salesforce’s Agentforce platform. These AI agents are specifically designed to automate processes in regulated industries, focusing on enterprise data workflows. By embedding WatsonX into Salesforce’s ecosystem, this collaboration extends its capabilities to areas like customer relationship management (CRM) and other data-intensive operations.
• On October 21, IBM introduced Granite 3.0, an enterprise AI model. In the announcement, the company stated that it is planning to release new tools to help developers and deploy agentic frameworks and said that it is focused on developing AI agent technologies which are capable of greater autonomy, sophisticated reasoning and multi-step problem solving.
• “The initial release of the Granite 3.0 8B model features support for key agentic capabilities, such as advanced reasoning and a highly-structured chat template and prompting style for implementing tool use workflows. IBM also plans to introduce a new AI agent chat feature to IBM watsonx Orchestrate, which uses agentic capabilities to orchestrate AI Assistants, skills, and automations that help users increase productivity across their teams. IBM plans to continue building agent capabilities across its portfolio in 2025, including pre-built agents for specific domains and use-cases.”
• IBM’s expansion of its AI-powered platform, IBM Consulting Advantage, now includes specialized offerings for Cloud Transformation and Management, and Business Operations. These new modules integrate domain-specific AI agents, applications, and methods, enabling consultants to accelerate cloud and AI transformations, such as code modernization and quality engineering, and streamline operations in areas like finance, HR, and procurement.

Amazon

• Amazon and AWS are advancing their AI agent development with offerings designed for both retail and enterprise use cases. The company, though trailing behind Google and Microsoft in AI development, is increasing its investments in AI agents to close the gap. Amazon Bedrock Agents are among the key developments, enabling execution of multistep tasks across company systems. The agents incorporate RAG, which improves their ability to retrieve and generate relevant data, making them more efficient for complex queries. Additional features include code interpretation, memory retention across interactions, and chain-of-thought reasoning, which provides transparency in decision-making.
• In June, Amazon hired executives and top talent from the AI agent startup Adept and licensed its AI technology to bolster its AI agent portfolio. By leveraging Adept’s technology, Amazon seeks to enhance AI-driven automation, particularly in helping users navigate and operate enterprise software more efficiently.

 

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• In August, reports indicated that Adept's investors would receive their money back, despite no change in the company’s ownership.
• Amazon is also developing AI shopping agents designed to recommend products to customers and even add items to their carts automatically. “It’s on our roadmap. We’re working on it, prototyping it, and when we think it’s good enough, we’ll release it in whatever form makes sense,” said Trishul Chilimbi, VP and distinguished scientist at Amazon.

Amelia

• Developed by IPsoft, Amelia offers advanced conversational AI and automation solutions for enterprise applications. Unlike broader AI systems, Amelia's AI agents are designed with a deep contextual understanding, enabling them to manage complex, multi-turn conversations. By combining natural language processing, machine learning, and process automation, Amelia’s AI agents can simulate human-like interactions with precise responses.
• Amelia’s AI agents are widely deployed across industries such as finance, healthcare, and telecommunications, where they assist in tasks like loan processing, handling customer inquiries, patient management, and executing administrative functions. Amelia’s ability to integrate with existing IT systems and offer highly customized AI solutions for businesses positions it as a leading player in the AI agent market.
• Amelia has been recognized as a leader in the Everest Group's Conversational AI PEAK Matrix for three consecutive years, holding the highest position across both the Vision & Capability and Market Impact axes. Recently awarded the XCelent Advanced Technology 2024 Award for retail banking solutions, Amelia stands out for its advanced capabilities in the financial sector. It is one of only two vendors acknowledged as a top provider across all major industries, with the largest client base among vendors in the banking sector and a strong retention rate with tier 1 banks.

Oracle

• In September, Oracle announced at CloudWorld the launch of over 50 role-based AI agents within their Fusion Cloud Applications Suite, designed to automate business processes and enhance productivity across multiple departments.
• The AI agents are embedded throughout Oracle's ecosystem, providing personalized insights, recommendations, and automated task completion capabilities. These agents support various business functions including finance, supply chain, HR, sales, marketing, and service, enabling organizations to reimagine their workflow processes.
• "This is the use case that we've really been wishing for," said Mark Beccue, an analyst at TechTarget's Enterprise Strategy Group. "These are very pragmatic and practical ideas. Something helps me get something done."

Anthropic

• Anthropic, the developer behind the AI model Claude, is expanding into the AI agent market. On October 22, the company introduced AI agents tailored for specific business functions, built on large AI models and customizable for various tasks. Anthropic’s latest innovation, the "Computer Use" capability, enables its technology to interpret computer screens, click buttons, enter text, navigate websites, and execute tasks across any software, including real-time internet browsing.
• According to Jared Kaplan, Anthropic’s chief science officer, this tool allows AI to "use computers in basically the same way that we do," capable of completing complex tasks involving "tens or even hundreds of steps." Amazon had early access to this tool, and early customers and beta testers included companies like Asana, Canva, and Notion. Kaplan also noted that the company has been developing the tool since early this year, with plans to make it available to both consumers and enterprise clients in the coming months or early next year.

Ema

•  Ema offers a universal AI employee solution that can assume multiple roles across various business functions. The platform features a Generative Workflow Engine for building specialized AI employees, with capabilities in customer support, data analysis, and employee assistance. The startup, which emerged from stealth in March, has raised $61 million from Accel and Section 32.
• It offers enterprise no-code AI agents, referred to as “universal AI employees,” designed to operate across multiple business functions. "Our goal at Ema is to automate most of the mundane tasks human employees perform today, freeing them up for more valuable work in the enterprise. Ema is built as a universal AI employee, capable of taking on any role in the organization—from customer support and employee experience to sales, marketing, legal, and compliance," said Surojit Chatterjee, CEO and co-founder, in an interview with VentureBeat.
• The company offers a no-code platform with pre-built AI employee templates. Users select an AI agent for a specific use case, then fine-tune and deploy it through a guided process. These AI employees (or "Ema personas") make decisions, create plans, and orchestrate workflows while collaborating with humans. To ensure accuracy, the platform uses EmaFusion, a 2T+ parameter model combining over 100 public LLMs and custom domain-specific models, optimizing performance and cost.

Sierra AI

•  Sierra's AI agents are designed to deliver natural, empathetic conversations across text and voice channels, ensuring a consistent brand tone and personality. The agents seamlessly integrate with a variety of business systems, including CRM case management, order management, subscription services, and customer service platforms, enabling efficient automation across multiple functions.

• Built on the AgentOS platform, Sierra’s technical architecture leverages multi-LLM processing for complex tasks, long-term memory capabilities, and API integration with enterprise systems. The platform also includes built-in supervisory AI systems that monitor and manage agent behavior, ensuring the accuracy and reliability of the AI agents' performance.
• On October 9, Sierra announced that its AI agents had gained voice capabilities, enabling them to handle customer calls. In the announcement, the company highlighted the multitasking abilities of its agents, stating, “Behind the scenes, Sierra agents have multitasking superpowers. While your agent communicates updates and chit-chats, it can quickly retrieve relevant information, securely access internal systems, and take appropriate action. In the context of a return, for example, a Sierra agent can pull up a customer’s order while it checks their address. Then, in a fraction of a second, it can locate the three nearest return centers, calculate walking directions for each, and tell the customer which has the shortest walk.”
• Founded in 2023 by OpenAI's chairman Bret Taylor, the company reached unicorn status in February 2024 following a $110 million venture round led by Sequoia Capital and Benchmark. On October 9, Bloomberg reported that the company was in discussions to secure additional funding, potentially valuing it at over $4 billion. 

AI Agent Adoption

• A July 2024 report from Capgemini reveals that while only 10% of companies are currently using AI agents, 82% plan to integrate them within the next 1-3 years. This surge in adoption is driven by the anticipated benefits of enhanced automation and efficiency, particularly in industries such as customer support and financial services.

 

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• Adoption may be currently low, as it is a new technology, but AI agents are expected to reshape business dynamics. According to Bill Gate, "Agents are not only going to change how everyone interacts with computers. They are also going to upend the software industry, bringing about the biggest revolution in computing since we went from typing commands to tapping on icons."
• The pharmaceutical and healthcare sector currently leads in AI agent adoption, with 23% of organizations already using the technology. However, in the next year, a substantial portion of high-tech (77%) and retail (66%) organizations are expected to adopt AI agents, demonstrating growing acceptance and integration of AI across diverse industries.

• AI agents offer organizations versatility, facilitating deployment across various functions. Approximately 75% of businesses plan to implement this technology for tasks such as code generation and continuous code improvement, underscoring its practical applications in streamlining and enhancing development processes.

 

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• As noted in the survey, AI agent adoption is still limited, but Capgemini, Deloitte and SHRM provides industry-specific projections for future growth:
• In pharma/healthcare, AI agents are expected to manage healthcare services, including appointment scheduling, patient monitoring, and personalized care, improving service delivery and reducing errors.
• In financial services, AI agents will continuously monitor account activity, detecting anomalies in real time to reduce fraud. Deloitte estimated the potential for different types of “agent modes” in which “humans and AI interact to implement the operating improvements that can deliver financial impact.”
 
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• In customer service, they will handle natural language interactions, providing personalized assistance and allowing human agents to focus on more complex tasks. This seems to be the most widespread adoption currently.
• In human resources, AI agents will manage administrative tasks like onboarding, payroll, and benefits, freeing staff to work on strategic initiatives. For example, ServiceNow integrates AI into its digital workflow technology with its latest release, Xanadu, which includes NowAssist for employees, an AI-driven manager hub, and a redesigned HR agent workspace, integrating AI across the employee and manager experience.
• For IT service desks, they will resolve repetitive issues, improving productivity and reducing response times. In IT software development, AI agents are anticipated to autonomously develop software, from analysis to monitoring, ensuring quality assurance and shifting the role of engineers toward collaboration with AI.
• Furthermore, despite the early-stage development of AI agent technology, some organizations are already leveraging its potential:
•  LG has integrated AI agents with robotic, AI, and multi-modal technologies to enable mobility, active learning, and complex conversation engagement. These agents can autonomously manage smart-home devices, patrol homes, monitor pets, and improve domestic security and energy efficiency.
•  Torq has implemented a cybersecurity AI agent into its security hyper-automation platform, automating the triaging of contextual alerts, incident investigation, and response. This technology allows security teams to focus on more pressing issues, reducing staff burnout. Torq aims for the AI agent to independently resolve 90% of tier-1 and tier-2 security tickets in the future.

Adoption by Function

• A survey found that IT operations are the top priority for AI agent investment, with 23% of respondents planning to focus on this area in the next one to two years. Key areas of emphasis include enhanced system monitoring, predictive analytics, and automated maintenance, all aimed at reducing downtime and improving operational efficiency. HR was the second most prioritized function, followed by cybersecurity, project management, accounting, marketing management, and customer complaint resolutions.

 

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•  According to Accel, AI agents are enabling the automation of increasingly broad and complex business challenges, as they allow AI models to orchestrate workflows with minimal or no human involvement, managing tasks such as document queries, handling, engineering workflows, and security processes autonomously.
• The next generation of frontier action models will be specifically designed for task automation and decision-making, further enhancing their capacity to independently tackle complex business problems. In addition, some functions may be better suited for agents than others, which could affect adoption in the future.

 

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• Still, organizations trust in AI agents for specific tasks. A majority (63%) indicated they would trust AI agents to analyze and synthesize data, while half would trust them to compose work-related emails. Additionally, 60% of organizations believe that within the next 3–5 years, AI agents will be responsible for generating most of the coding within their operations.

 

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Research Strategy

Summary

Complete Findings

Software Development

1. Code Generation and Assistance

•  Examples of existing solutions: AI coding assistants like GitHub Copilot, Tabnine, Replit AI, ChatGPT, Tabby, Cody, Blackbox AI, and Codeium, among others, help developers suggest code snippets or improvements, complete code based on context, auto-generate code, and debug code to save time and enhance productivity.
•  Integration for efficiency: These tools integrate seamlessly into development environments (e.g., IDEs like VS Code), offering real-time code suggestions, autocomplete, bug detection, and security checks. They also support multi-language development, refactoring, and natural language queries, streamlining workflows and improving coding accuracy.
•  Future evolution: AI coding assistants will increasingly automate routine development tasks, boosting productivity, code quality, and coding standardization across teams. By 2027, these tools will advance to manage technical debt, modernize legacy code, and enhance user experiences. In the long term, AI will autonomously develop and optimize software, transforming software engineering into “Software 2.0,” where new skills and strategic oversight will be essential for managing AI-driven development.

2. Automated Code Review

•  Examples of existing solutions: AI agents, such as GitHub Copilot, Bito AI, CodeRabbit, Codium AI, and What the Diff, are leveraging AI for automated code reviews, offering features such as real-time suggestions, pull request summaries, static analysis, and chat-based feedback.
•  Integration for efficiency: AI tools streamline workflows by automating error detection and summarizing code changes. Integrated seamlessly and directly with developer platforms and integrated development environment (IDE) platforms, such as Visual Studio Code, GitHub, and GitLab, these solutions help teams detect bugs early, improve security, and maintain code quality while saving time for human code reviewers.
•  Future evolution: These agents will likely become more context-aware, offering deeper domain-specific insights and more reliable decision-making processes to improve the relevance and accuracy of feedback and complement human reviews. Future solutions may also likely integrate hybrid models, combining human expertise with AI to mitigate errors and promote knowledge sharing through collaborative reviews, boosting code quality and team development.

3. Software Testing and Quality Assurance

•  Examples of existing solutions: AI agents, such as Applitools, Testim.io, Mabl, Testsigma, and Katalon Studio leverage AI to automate testing processes, reduce manual effort, and ensure better test maintenance and performance.
•  Integration for efficiency: These AI-powered testing tools integrate into continuous integration and continuous deployment (CI/CD) pipelines, DevOps toolchains, test frameworks, or development environments, to automate testing across the development lifecycle, using AI for generating test cases, detecting changes, and employing self-healing scripts to reduce downtime. They also connect with platforms, such as repositories and ticketing systems, to streamline workflows, provide continuous feedback, and support better decision-making through real-time analytics.

•  Future evolution: Future AI agents in software testing will collaborate with human testers and other specialized agents to enhance efficiency, sharing insights across areas like performance, UI, and security. They will also assist in exploratory testing by suggesting scenarios based on past issues or new features. Personalized testing strategies will emerge, focusing on user experience by analyzing interaction patterns and prioritizing critical pain points to improve release quality.

Human Resources (HR)

 

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1. Recruitment and Candidate Screening

•  Examples of existing solutions: AI recruitment tools like Vervoe, HireVue, Jobvite, Manatal, and SmartRecruiters help automate various stages of the hiring process, including candidate screening, skill assessments, and interview scheduling.

•  Integration for efficiency: These tools integrate into applicant tracking systems (ATS), HR management systems, recruitment workflows, and other recruitment software to streamline tasks, such as resume screening, candidate engagement, and performance analytics. Through these integrations, AI-driven assessments, chatbots, and predictive analytics enhance decision-making, improve hiring speed, and reduce bias in candidate selection.
•  Future evolution: AI recruitment tools will evolve by leveraging predictive analytics to identify future skill demands, helping companies engage candidates proactively. Virtual assistants and chatbots will enhance candidate interactions through personalized communication and faster responses. Additionally, AI will streamline workflows from job descriptions to profile summaries, providing data-driven insights to support better hiring decisions.

2. Employee Onboarding

•  Examples of existing solutions: AI onboarding tools like BambooHR, Workday, Rippling, and Greenhouse Onboarding automate various aspects of the onboarding process, from paperwork management to task assignments.
•  Integration for efficiency: These tools automate administrative and repetitive tasks such as document handling, compliance tracking, and personalized workflows. They can integrate with HR systems (human resources information system — HRIS) to provide real-time insights, improve compliance, and offer tailored onboarding experiences, thereby enhancing productivity and ensuring a seamless new hire process. Virtual assistants provide real-time support, improving engagement and reducing turnover from day one.
•  Future evolution: The future of onboarding AI will feature enhanced personalization and efficiency through advanced AI tools, immersive experiences with VR/AR, and predictive analytics to optimize outcomes and improve new hire success.

3. Performance Management and Employee Development

•  Examples of existing solutions: AI-powered tools like Lattice, BetterWorks, Reflektive, and 15Five are AI-driven platforms supporting employee performance management with goal setting, feedback, and predictive analytics.
•  Integration for efficiency: These tools integrate with HR systems, collaboration tools, communication tools, and HRIS tools to automate feedback, real-time performance tracking, and personalized development plans, reducing administrative burden and enhancing decision-making. Predictive analytics help managers proactively address issues, while continuous evaluation improves goal alignment and employee engagement.
•  Future evolution: The future of AI in performance management will focus on hyper-personalized insights, predictive analytics, and immersive learning through AR/VR. AI tools will evolve to address emotional intelligence, team dynamics, and ethical considerations while supporting continuous learning ecosystems. These advancements aim to create human-centric workplaces, enhancing employee engagement, productivity, and well-being.

Finance

 

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1. Fraud Detection and Risk Management

•  Examples of existing solutions: AI agents such as Effectiv, Feedzai, SEON, Sift, LeewayHertz, and Data Visor use AI to detect and prevent financial fraud with real-time monitoring, machine learning models, and predictive analytics of transactions and user behavior.
•  Integration for efficiency: These AI tools integrate into banking systems, payment systems, ecommerce platforms, and customer onboarding processes, automating KYC, transaction monitoring, and fraud detection to reduce false positives and improve risk management. They enhance decision-making by providing real-time insights, customizable workflows, and proactive responses to emerging threats.
•  Future evolution: Future fraud detection AI agents will leverage advanced machine learning, behavioral analytics, and multichannel data integration to detect complex fraud patterns more accurately. They will incorporate explainable AI for transparency, automate responses to suspicious activity, and use federated learning to train on decentralized data while preserving privacy. These tools will shift toward proactive fraud prevention, using predictive analytics to anticipate threats and enhance security.

2. Loan Underwriting and Origination

•  Examples of existing solutions: AI agents, such as Turnkey Lender, Zest AI, LoanPro, HyperVerge, LeewayHertz, and ZBrain.ai use AI to automate and streamline loan origination and underwriting processes.
•  Integration for efficiency: These tools leverage AI for automated credit scoring, assessing creditworthiness, risk assessment, and decision-making, integrating with existing financial systems to accelerate loan approvals and enhance accuracy. AI models also analyze both traditional and alternative data sources, improving customer experience while optimizing portfolio management through predictive analytics. They integrate with financial systems to reduce manual work and enhance decision-making with real-time insights.
•  Future evolution: AI underwriting will advance with predictive analytics, alternative data sources, ethical AI practices, and integration with blockchain and IoT, driving more accurate, efficient, and transparent lending operations.

3. Portfolio Management

 

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•  Examples of existing solutions: AI tools like Bloomberg Terminal, QuantConnect, Sentieo, and Kensho enhance portfolio management with analytics, market research, and algorithmic trading. Robo-advisors such as Betterment and Wealthfront automate investment strategies with minimal human input.
•  Integration for efficiency: These tools integrate into financial workflows by providing real-time data, predictive analytics, and personalized recommendations, helping portfolio managers optimize investment strategies and mitigate risks efficiently. These agents optimize portfolios through automated rebalancing, monitor risks in real-time, and provide personalized financial services. By integrating with existing infrastructure, AI agents streamline operations, improve customer engagement, and offer timely insights through automated reporting.
•  Future evolution: AI in portfolio management will drive hyper-personalized financial services, using generative AI for data analysis to tailor products to individual customer needs, enhancing satisfaction and loyalty. Investment management will leverage AI algorithms to analyze vast data sources, expanding robo-advisor services for holistic financial planning. Additionally, AI will play a critical role in fraud prevention by identifying suspicious patterns in real time to safeguard financial assets.

Customer Support

1. Intelligent Chatbots for 24/7 Support

•  Examples of existing solutions: Tools like ProProfs Chat, HubSpot, Kommunicate, Zendesk, and Intercom provide AI-powered customer support with features, such as 24/7 live chat, ticketing systems, and personalized customer engagement.
•  Integration for efficiency: These chatbots integrate seamlessly into websites, CRM systems, and social media platforms, automating repetitive queries, improving response times, and enhancing customer engagement. This reduces the workload on human agents, boosts productivity, and ensures a consistent, high-quality customer experience.
•  Future evolution: Future customer service chatbots will offer hyper-personalized interactions, voice-activated support, and seamless omnichannel integration. They will incorporate empathetic AI to respond to customer emotions and use predictive analytics for proactive solutions. With self-learning capabilities, these chatbots will continuously improve, delivering increasingly sophisticated and responsive support experiences.

2. Automated Ticket Routing

•  Examples of existing solutions: Help Scout, SysAid, Intercom, Freshdesk, and Zendesk offer AI-powered chatbots and ticketing systems that streamline customer support through automation and smart responses.
•  Integration for efficiency: These tools enhance productivity by automating ticket sorting, routing, and drafting responses while offering writing assistance and chatbots for self-service. They integrate into workflows to reduce agent workload, improve response times, and increase customer satisfaction with personalized interactions.
•  Future evolution: The future of AI in customer support will see advancements in conversational AI and natural language processing, proactive support via IoT integration, and immersive experiences through AR and VR. Increased system integration and intuitiveness will drive more cost-effective and seamless customer interactions.

3. Sentiment Analysis

•  Examples of existing solutions: SentiSum, MonkeyLearn, SupportLogic, Brandwatch, Idiomatic, and CallRail provide sentiment analysis tools tailored for customer support, social media tracking, and call analytics.
•  Integration for efficiency: These AI tools centralize customer feedback from multiple channels, offer real-time sentiment insights, automate ticket handling, and enhance decision-making with predictive analytics. By integrating with platforms like Zendesk and Freshdesk, they streamline workflows and improve customer satisfaction.
•  Future evolution: The future of sentiment analysis in customer support will focus on real-time issue anticipation, personalized interactions, and seamless chatbot integration. Companies will also use sentiment insights to improve products, enhance agent training, and boost customer satisfaction and revenue​.

Sales and Marketing

1. Sales Data Analytics and Forecasting

•  Examples of existing solutions: Tools like Avoma, Clari, BoostUp.AI, Gong.io, and Mediafly provide AI-powered sales forecasting solutions, focusing on predictive analytics, pipeline management, and revenue intelligence.
•  Integration for efficiency: These tools automate data collection from conversations, emails, and CRMs, providing real-time forecasting, deal insights, and risk analysis. They enhance decision-making by enabling accurate forecasts, tracking pipeline health, and aligning sales efforts to meet revenue goals efficiently.
•  Future evolution: Future AI sales forecasting will emphasize deeper integration with CRM systems for a holistic view of sales data and trend analysis. It will focus on delivering contextually relevant insights tailored to specific sales scenarios and customer needs. Additionally, AI will automate manual tasks like data analysis and reporting, freeing up sales teams to focus on strategic activities and personalized customer interactions, driving stronger relationships and revenue growth.

2. Lead Generation and Qualification

•  Examples of existing solutions: Tools such as Lyne AI, Smartwriter AI, Instantly AI, Customers.ai, Albert, and Seamless.AI automate lead generation, content creation, and outreach through personalized outreach, prospect targeting, and email campaigns.
•  Integration for efficiency: These AI tools automate data scraping, lead qualification, outreach personalization, and campaign tracking, helping businesses identify high-quality prospects faster. They integrate with CRM systems and marketing platforms to optimize workflows, enabling sales teams to focus on conversions, improving customer engagement, and enabling more targeted outreach strategies. These AI tools automate processes like data collection, lead scoring, personalized messaging, and campaign tracking.
•  Future evolution: The future of AI-powered lead generation will focus on AI agents that automate repetitive tasks, engage prospects in real-time, and personalize communication across multiple channels. These tools will enhance lead qualification, integrate seamlessly with CRM systems, and optimize outreach through predictive analytics, helping businesses improve conversion rates and operational efficiency.

Operations

1. Robotic/Intelligent Process Automation (RPA/IPA)

•  Examples of existing solutions: Platforms like UiPath and Automation Anywhere provide robotic process automation (RPA) tools that employ AI agents to automate repetitive tasks across various business functions. Celonis’s AI-driven process mining tools automate complex workflows across multiple systems, from procurement to supply chain operations.
•  Integration for efficiency: AI agents in Robotic Process Automation (RPA) integrate into business operations by automating repetitive tasks, streamlining workflows, and improving data accuracy. By combining RPA with data science, organizations enhance decision-making through predictive analytics, cognitive automation, and adaptive learning, ensuring optimized processes and continuous improvement. These tools also seamlessly integrate with existing business systems to streamline workflows and improve operational outcomes.
•  Future evolution: Future RPA trends include advanced AI integration for handling unstructured data and complex decisions, widespread adoption of hyper-automation for optimized operations, and subscription-based RPA-as-a-service models to reduce entry barriers for businesses.

2. Supply Chain Optimization

•  Examples of existing solutions: Tools like ThroughPut AI, Logility, Blue Yonder, and IBM Watson optimize supply chains through AI-driven demand forecasting, logistics planning, and inventory management.
•  Integration for efficiency: These AI solutions integrate with business systems to enhance decision-making by providing real-time insights, predictive analytics, and automated workflows. They streamline logistics by automating warehouse management, tracking shipments in real-time, and optimizing supply routes. They enhance decision-making through predictive analytics, helping businesses respond proactively to disruptions, reduce costs, and improve operational efficiency.
•  Future evolution: The future of AI in supply chains will see more advanced systems improving efficiency and resilience, with generative AI helping design optimized networks. AI will also play a key role in risk management by predicting disruptions and recommending mitigation strategies. However, human intuition and ethical oversight will remain essential alongside these technologies.

Research and Development (R&D)

1. Generative and Product Design AI

•  Examples of existing solutions: Tools like Adobe Sensei, NVIDIA GauGAN, Autodesk Fusion 360, and Spacemaker utilize generative AI in fields such as graphic design, product development, and urban planning. Autodesk Fusion 360 uses AI generative design algorithms to create optimized designs based on specified parameters. Siemens NX integrates AI for generative design, enabling faster product development cycles.
•  Integration for efficiency: Generative AI boosts productivity by automating iterations, optimizing designs based on constraints, and providing rapid prototypes. It supports designers across industries—fashion, architecture, and gaming—by generating innovative outputs quickly, allowing them to focus on creativity and strategic decision-making. This not only speeds up the design phase but also leads to innovative solutions that human designers may not consider, thereby enhancing creativity and efficiency in product development.
•  Future evolution: The future of generative AI in design will emphasize human-AI collaboration, with designers guiding AI tools to enhance creativity and innovation. As AI models become more advanced, they will produce personalized, culturally nuanced designs. Ethical frameworks will evolve to address concerns like data privacy and sustainability, while democratizing access to design tools, and empowering non-experts to create professional-quality work. This evolution will unlock new creative possibilities and design movements.

Drug Discovery

•  Examples of existing solutions: Atomwise, Exscientia, Insilico Medicine, Iktos, and Cradle are some AI agents that are used to streamline and accelerate drug discovery through predictive analytics and molecule design by analyzing biological data and predicting molecular interactions for drug discovery.
•  Integration for efficiency: These platforms leverage AI to analyze large datasets, predict drug efficacy, and automate processes like molecule synthesis and clinical trial design. They integrate with R&D workflows and drug development pipelines to scan large biological databases, simulate drug interactions, and identify promising compounds. This enhances decision-making, reduces costs, and accelerates time-to-market for new drugs.
•  Future evolution: The future of drug discovery with AI will focus on accelerating development through real-time analysis, precision R&D, and patient-centric drug design. Regulatory flexibility will encourage further AI adoption, while cybersecurity and advanced modeling will be essential to ensure safe integration. As AI reduces costs and optimizes clinical trials, pharmaceutical companies will increasingly leverage it to streamline drug approval and market supply.

Legal Compliance

1. Compliance Monitoring

•  Examples of existing solutions: Aisera enables the creation of tailored training materials aligned with specific compliance requirements, proactively alerting users about non-compliance issues. Compliance Officer AI Agents provide continuous monitoring of compliance policies within organizations, helping teams stay ahead of regulatory changes. Additionally, tools such as AuditBoard, Kount, Compliance.ai, and Centraleyes are other examples of compliance AI tools that "monitor risk profiles and regulatory changes, enabling organizations to address potential issues preemptively."
•  Integration for efficiency: AI integrates with compliance systems to automate regulation analysis, risk identification, and real-time reporting. By streamlining data collection, assessments, and checks, they reduce manual effort and keep workflows updated with regulatory changes. This allows compliance teams to focus on strategic tasks, improving efficiency and decision-making.
•  Future evolution: Future compliance AI agents will leverage advanced NLP for real-time regulatory analysis, explainable AI (XAI) for transparent decision-making, and federated learning to enhance data privacy. They will offer hyper-personalized compliance training and augment human teams by automating routine tasks. Adoption across industries like finance, healthcare, and manufacturing is expected to grow, reflecting AI's versatility in ensuring industry-specific compliance.

2. Automated Auditing and Legal Document Analysis

•  Example of existing solutions: Tools like LegalRobot, Luminance, and Casetext automate legal document analysis, contract review, and compliance monitoring, enhancing efficiency in law practices. AI-driven auditing platforms, like BRYTER, automate the review of legal documents, contracts, and company operations to ensure compliance with internal and external regulations.
•  Integration for efficiency: AI agents streamline workflows by automating document analysis, compliance checks, and risk assessments. They integrate with existing legal systems to reduce manual effort, generate insights, and improve decision-making, allowing legal teams to focus on strategic tasks.
•  Future evolution: The future of AI in automated auditing and legal document analysis will feature more advanced algorithms capable of drafting complex legal documents with minimal human input, significantly reducing preparation time. AI-driven tools will further enhance legal research by offering faster access to relevant precedents through advanced NLP and predictive analytics for case outcomes. These developments will allow legal professionals to focus on higher-level strategic tasks, improving efficiency and decision-making.

Summary

Complete Findings

I. Overview

• AI Agents benefit users with task automation, greater performance, and quality of response.
• Task automation — When AI Agents automate complex tasks that would otherwise have been completed by humans, enterprises can achieve goals "inexpensively, rapidly, and at scale."
• Greater performance — If an AI agent can incorporate knowledge and feedback from "other AI agents specializing in related areas can be useful for information synthesis. This backend collaboration of AI agents and the ability to fill information gaps are unique to agentic frameworks, making them a powerful tool and a meaningful advancement in artificial intelligence."
•  Quality of responses — Unlike traditional AI models, AI Agents provide responses that are accurate, comprehensive, and personalized. Higher quality responses provide for a better customer experience.

• In its 2023 Hype Cycle for Artificial Intelligence, Gartner gave AI Agents a high benefit rating and an emerging maturity rating with a market penetration of 1 to 5% of the target audience.
• Please note that Gartner defined AI agents as " the combined application of AI and simulation technologies to jointly develop AI agents and the simulated environments in which they can be trained, tested, and sometimes deployed."

II. AI-Agent Applications For Small and Medium-Sized Businesses (SMEs).

• AI Agents can be implemented in customer support, inventory management, appointment scheduling, content generation, automation of repetitive tasks, project management, marketing and sales, data analysis, financial management, and legal and compliance tasks for SMEs.
• In customer support, examples of AI agents that can be used by SMEs are conversational AI and virtual AI agents. Conversational AI, virtual AI agents, and appointment scheduling are primarily goal-based AI agents but can also include model-based and learning agents as well.
•  Project management and AI agents used in marketing and sales to generate leads, nurture customer relationships, and drive sales are also primarily goal-based with elements from model-based and learning systems.
• AI agents used in inventory management, data analysis, and financial management are primarily learning agents with elements of model-based systems as well.
• Depending on how they are implemented, AI agents used for content generation, legal and compliance, or the automation of simple tasks can be any one of the various types of AI agents.

III. Types of AI Agents

• There are seven types of AI Agents: simple reflex agents, model-based reflex agents, goal-based agents, utility-based agents, learning agents, hierarchical agents, and multi-agent systems.  

1. Simple Reflex Agent

• A simple reflex agent is an AI agent that utilizes current data under a set of condition-action rules that have been coded into the system when making a decision or taking an action.
• This type of AI Agent acts only on "predefined rules and immediate perception." Pros include ease of design, ease of implementation for specific tasks, and quick response to stimuli not requiring complex processing. Cons include limited flexibility and a lack of context, given that it only works with current data.

 

 

•  Examples of simple reflex agents are thermostats, automated lighting systems, vacuum cleaning robots, elevator control, automatic doors, traffic light control, smoke detectors, and basic spam filters.
• This type of AI agent applies to startups and small and medium enterprises (SMEs) in automated customer support such as customer service chatbots.
• While simple reflex agents have limited scope and functionality, they are building blocks for more advanced agents and may have applications in Game AI to control non-player characters (NPCs). On account of their simplicity and length of use, the adoption rate for this type of AI agent may be high, unfortunately, precise numbers were not sourced.
• Potential future development may occur through combinations with "other AI techniques and incorporating adaptive and learning mechanisms, we can enhance their capabilities and unlock new possibilities in the field of artificial intelligence."

2. Model-Based Reflex Agents

• Model-based reflex agents utilize "current perception and memory to maintain an internal model of the world." The external environment is observed partially from the maintenance of an internal environment that is equipped with sensors that "gather information and consider the history of precepts, enabling more sophisticated decision-making."
• Actions taken by this type of model are dependent on the model, its reflexes, previous precepts, and current state. It is practical for environments where full information is unavailable, and some current state data is required.
• The internal state is updated based on two factors, "how the world evolves independently of the agent, and how does the agent's action affect the world," and operated in four stages" sense, model, reason, and act.
•  Examples of model-based AI agents are advanced driver-assistance systems (ADAS), healthcare monitoring systems, and smart home systems.

 

 

•  Pros of a model-based reflex agent are its quick and efficient decision-making, accurate decision-making is improved from its ability to create an internal model of the world, environmental adaptability, and choices are made based on more information and strategic choices.
•  Cons of model-based reflex agents are the high computational cost of building and maintaining models, real-world complexity may not be captured well, all potential situations that can come cannot be anticipated by the model, frequent updating is required for currency, and interpretation and comprehension may be challenging,
• Model-based agents can be used by startups and SMEs for supply chain management. They can be used for predicting inventory needs, given that the agent can consider "factors such as seasonal demand, shipping times, and production capacity, leading to more efficient stock management."
• Adoption rates are increasing "across various real-world applications, demonstrating their effectiveness in environments that require quick decision-making based on current states and historical data."

3. Goal-Based Agents

•  Goal-based agents operate with an internal model of the work as well as a goal or set of goals. These goals are used to make decisions by considering different scenarios and choosing actions with a higher likelihood of achieving goals.
• Goal-based agents expand on model-based reflex agents "by not only using percept history and internal memory to understand their environment but also considering the impact of their possible actions before performing them. This allows goal-based agents to think beyond the present moment and decide the best actions to take to achieve their objectives."
• There are five steps in the working patterns of a goal-based agent: perception, reasoning, action, evaluation, and goal completion. Key features of goal-based agents are "the ability to think beyond the present moment, use search algorithms, predict future scenarios, and adapt to changing conditions."
• This type of AI agent is sophisticated and makes systems capable of functioning with minimal human intervention possible. These agents enhance the process of decision-making and allow the handling of complex tasks, adaptability to changing conditions, and are "crucial for the development of advanced AI applications." 
•  Goal-based AI agents have been used across a wide range of industries and can be found in computer vision, natural language processing, navigation, and robotics.

 

 

•  Pros of this type of AI Agent are that it is simple both in implementation and understanding, it is efficient for goal achievements, performance evaluation is easy, more advanced AI agents can be achieved when combined with other techniques, best suited for structured, well-defined environments, and can be used for multiple applications.
•  Cons include being limited to specific goals, inability to adapt to changing environments, ineffectiveness for complex tasks with multiple variables, and significant domain knowledge required.
• This type of AI agent is useful for complex planning and decision-making tasks such as "strategic game playing, automated planning in logistics, and resource allocation in project management, where considering future steps towards a goal is essential."

4. Utility-Based Agents

•  Utility-based agents calculate utility using a utility function. A sequence of actions is selected that reach the goal, and maximize utility or reward. Each scenario based on a set of fixed criteria is assigned a utility value, "a metric measuring the usefulness of an action or how “happy” it will make the agent." There is no pre-defined goal in a utility-based agent.
• The criteria used include factors such as "progression toward the goal, time requirements, or computational complexity. The agent then selects the actions that maximize the expected utility. Hence, these agents are useful in cases where multiple scenarios achieve a desired goal and an optimal one must be selected."
• This type of agent is used in scheduling, game-play, economics, resource management, and where decision-making requires the balancing of various factors to determine the best possible outcome. It is increasingly being used in situations where multiple criteria are important such as in personalized recommendations and financial trading.

 

 

• Pros of utility-based agents are the wide range of decision-making problems they can handle, their ability to learn from experience and adjust decision-making scenarios, and the presence of a consistent and objective framework for decision-making.
•  Cons include the requirement for an accurate environmental model, high-cost computation and extensive calculations, not being able to make moral or ethical considerations, and difficulty in understanding and validation for humans.

5. Learning Agents

•  Learning agents differ from other AI agent types in that they can learn. The initial knowledge base is autonomously updated with new experiences which "enhances the agent’s ability to operate in unfamiliar environments."
• Goal or utility-based reasoning is employed in learning agents which are comprised of four main elements: Learning, critique, performance, and problem generator.
• Because approaches in algorithms are adjusted and refined based on data input, this type of agent performs tasks such as image recognition, predictive analytics, and natural language processing can be performed with increasing accuracy.
• Learning agents learn from the "consequences of their actions, which enables them to perform better in dynamically changing environments." This is a cycle of observation, learning, acting based on feedback, and adaptation.

 

 

•  Pros of learning agents are that they can convert ideas into action, follow basic commands in task performance, evolve with time, and consider utility measurements.
•  Cons of learning agents include that they can make biased or incorrect decisions, there are high development and maintenance costs, the computing resource requirement is high, large amounts of data are required, and they lack human-like intuition and creativity.
• Learning AI agents can be used for conversational customer service, as task-oriented dialogue agents (booking flights), and as virtual AI agents by SMEs.
• Potential/future developments for learning agents such as conversational AI are enhanced personalization, increased autonomy, improved interactions between humans and computers, and wider application domains.

6. Hierarchical Agents

•  High-level agents oversee lower-level agents in a structured hierarchy. The higher-level agents supervise and distribute goals into smaller tasks.
• This type of agent is used in applications such as robotics, manufacturing, and transportation as they are good at prioritizing multiple tasks and sub-tasks.
•  Pros of hierarchical agents are resource efficiency, enhanced communication from clear lines of authority and direction, agent-decision-making is improved by hierarchical reinforcement learning when action complexity is reduced and exploration is enhanced, and computational complexity is minimized when the problem is represented more concisely and in a reusable form.
•  Cons include increased complexity, adaptability is limited with a fixed hierarchy in uncertain environments, bottlenecks and delays may occur, reusability across different problem domains is lacking, and training is difficult.

7. Multi-Agent Systems

• Multi-agent systems (MAS) refer to "multiple artificial intelligence (AI) agents working collectively to perform tasks on behalf of a user or another system." While behaving collectively, each agent within a MAS has individual properties.
• A MAS is most applicable when there is a large-scale, complex task to be completed, and can comprise "hundreds, if not thousands, of agents. "
• Problems are solved using agent communication and distributed problem-solving, described by IBM as reinforcement learning. Information can be instantaneous (received from sensors or actions) or episodic (sequences of sensations, actions, and learned policies). This information is shared in real time to prevent repetitive learning.
• Multi-agent systems can be in a centralized network architecture or a decentralized network. MAS can be organized in holonic, hierarchical, coalition, or team structures.
•  Behavior in a MAS, whether multi-robot or multi-software can include flocking or swarming, and use cases include in transportation, healthcare and public health, supply-chain management, and defense systems.
•  Pros of MAS are flexibility, scalability, domain specialization, and greater specialization, and cons of MAS include agent malfunctions, coordination complexity, and unpredictable behavior.
• A potential use case for SMEs for MAS is in project management systems.
• Future developments for MAS include constellations wherein different LLMs and specialized knowledge bases are leveraged within various approaches to accomplish complex tasks. As the use of MAS spreads, ensuring data security will be the role of specialized agents within the MAS Architecture that is tasked with "safeguarding data integrity and access."
• "Imagine an agent with the specific duty of identifying the sources of data, including comprehensive data lineage considerations, and leveraging existing API endpoints, LDAP queries, and various established methods to verify whether a user requesting data is authorized to access it." 
• The market maturity for MAS is low, as these systems as only now beginning to "permeate various sectors." Gartner places the MAS within the initial innovation trigger sector of its hype cycle, with an expected plateau to be reached in 5 to 10 years. 

 

 

Summary

 

 

Creative Solutions

• Precompiled case studies on AI agent implementation within SMEs in the technology sector were not available.
• Therefore, we expanded our search to adjacent industries such as e-commerce, fintech, healthtech, and consumer goods.
• Please refer to the Research Strategy for additional information surrounding the research process.

Complete Findings

1. Sierra AI Agent Implementation at OluKai

•  Founded in 2005, OluKai is a US lifestyle and footwear brand inspired by Hawaiian culture, known for crafting premium leather sandals and expanding into an array of men’s and women’s footwear. As part of its growth strategy, OluKai pivoted towards a direct-to-consumer (DTC) model, complementing its wholesale operations with a strong digital presence. This shift helped position OluKai as an omnichannel brand, offering products through its website, physical stores, and platforms like Amazon.

 

Source

 

• With the rapid growth in the e-commerce sector, OluKai faced increasing volumes of customer inquiries across phone, email, and social media, which made it difficult to maintain response times and deliver personalized, high-quality customer service. The company’s goal was to scale customer support while maintaining the authenticity and personal touch synonymous with its 'Aloha Experience' brand ethos. Previous chatbot solutions had proven unsatisfactory, frustrating customers rather than solving their problems effectively.
• To address these challenges, OluKai turned to Sierra to build their first AI-powered agent. The AI agent was trained to manage repetitive customer inquiries (like order tracking and returns) while preserving the company’s personal touch. Key strategies included integrating AI with human oversight. The AI agents were designed to handle routine tasks, allowing OluKai’s customer service team, renamed "Day Makers," to focus on complex, high-touch interactions that fostered long-term customer relationships.
• Another strategy included implementing flexible AI responses. The AI was programmed to handle nuanced customer scenarios, such as late returns or special requests, by combining OluKai’s policies with personalized exception handling. Melissa Ziegler, VP of Marketing at OluKai, highlights the efficiency of the OluKai AI agent, noting that it provides instant responses around the clock, significantly reducing both first response and average handling times, making it accessible anytime, even in the middle of the night.
• The implementation of Sierra's AI agent at OluKai led to notable improvements in customer service. Customer Satisfaction (CSAT) reached 4.5 out of 5, with ambitions to further improve personalization and raise the score to 4.75. The AI agent also achieved a 65% resolution rate, handling over 70% of service tickets without human intervention. Additionally, it significantly reduced response and resolution times, allowing OluKai to manage higher ticket volumes efficiently while maintaining service quality.
• Melissa Ziegler, VP of Marketing at OluKai, expressed optimism about AI’s ability to maintain their brand’s personal touch. She reflects, “observing the AI respond empathetically to customers, mirroring the approach of [their] human agents, was astounding.” Additionally, John Elkins, Aloha Experience Supervisor, highlighted the team’s improved job satisfaction: “With Sierra, we've reduced ticket volume expectations, allowing our team to concentrate on more complex cases with empathy and care.”

2. Einstein Copilot AI Agent Implementation at Wonolo

•  Wonolo, short for “work now locally,” is an online platform connecting job seekers with local businesses in need of temporary workers. With a focus on operational efficiency, Wonolo turned to AI solutions to improve customer service and streamline communications between its agents and customers.

• Wonolo integrated Salesforce’s Service Cloud, powered by Einstein AI, to augment the performance of its customer service agents. By using Einstein Copilot, a generative AI solution, the company improved response times, automated customer interactions, and enhanced overall service quality. The AI agent was designed to handle tasks such as generating job-related texts and assisting agents in responding quickly and accurately to customer inquiries.
• Wonolo’s strategy for adopting AI agents focused on automating routine tasks, such as using Einstein AI to auto-generate messages with key job information, freeing up agents for more complex tasks. The company integrated AI to leverage real-time data, consolidating customer insights into a single view to enhance agents' efficiency in resolving cases.
• Additionally, AI-driven support tools suggested responses and handled basic customer queries autonomously, minimizing the need for senior staff intervention and optimizing overall operational workflow. The company sought to boost service quality. Wonolo aimed to enhance the consistency of service, ensuring customers received uniform, high-quality support regardless of which agent handled their case.
• Wonolo achieved a 20% reduction in average handling time for new agents and a 12% reduction for experienced agents through AI implementation. This, combined with a 3-point increase in customer satisfaction due to faster response times and better service consistency, also boosted agent confidence, with fewer customers requesting specific agents for follow-ups.
• Wonolo's Senior Salesforce Administrator, Adam Ashworth indicates that, "Einstein has helped [their] agents be more efficient and confident in their work, without losing the human connection [Wonolo] pride itself on as a company." On the other hand, EVP and GM of Service Cloud at Salesforce, Kishan Chetan, commented, "Einstein enhances the human element of candidate matching, making Wonolo even better at what they do best."

3. Ada AI Agent Implementation at ClickUp

• ClickUp, a project management platform with 12 million users, partnered with Ada to enhance their customer support using generative AI agents. Initially, ClickUp implemented a scripted chatbot to streamline customer interactions across their website and help center. However, to scale further and reduce manual workload, they transitioned to Ada’s AI-powered generative agent. This AI agent was integrated with their Zendesk Help Center, enabling it to autonomously generate answers to customer inquiries based on ClickUp’s support content and user profiles.
• "As one of the fastest-growing SaaS" and award-winning startups in the world, ClickUp experienced rapid user growth, which led to challenges in managing customer support efficiently. Their support team struggled with the volume of inquiries, inefficient routing, and inconsistent customer experiences. To resolve 45% of customer inquiries without human intervention and ensure a more consistent and efficient support process, ClickUp turned to AI to automate and optimize its support system.
• ClickUp's AI adoption strategy focused on streamlining customer interactions and improving operational efficiency. By replacing disjointed support forms with Ada’s AI-powered chatbot, the company consolidated multiple channels into a single interface, simplifying the customer experience. The AI agent automated triage and routing processes by gathering relevant customer data and directing inquiries to the appropriate human agents, significantly improving response times.
• After implementing the generative AI agent, ClickUp achieved a 20% improvement in its Automated Resolution Rate (AR), with 40% of customer inquiries resolved digitally without requiring human assistance. This success highlights the efficiency of AI-driven automation in handling a significant portion of customer service interactions.

 

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• Allie Hurley, Head of Global Support, noted that while the scripted chatbot delivered strong results, the implementation of the generative AI agent exceeded expectations, and the company continuously updates its product to meet evolving customer needs. She adds, "With the AI Agent, we no longer have to rely on human intervention to address new customer inquiries that come through when we've updated our product."

4. Ada AI Agent Implementation at Dott

• Dott is a leading shared micromobility operator, based in Amsterdam, Netherlands, that offers eco-friendly scooters and bikes for urban transportation. Their mission is to provide clean, convenient rides, ensuring a seamless experience for both riders and customer service agents. To support this, Dott operates a customer service team of 23 agents handling queries across various channels.
• To enhance its customer service, Dott implemented Dixa’s unified agent workspace alongside Ada’s AI-powered chatbot. This dual integration allowed Dott’s support team to handle all customer interactions from a single interface and automate routine queries, significantly improving their efficiency and customer satisfaction. By streamlining their customer service through AI-driven tools, Dott was able to provide personalized, fast responses while reducing their dependency on external customer service agents.
• Dott was facing significant challenges with manual workflows that slowed response times and increased operational costs. Their agents spent excessive time on repetitive tasks, affecting their ability to respond quickly and effectively to more complex issues. Additionally, Dott’s fragmented internal systems led to inefficiencies, which negatively impacted the rider experience. Dott sought to solve these issues by automating routine tasks, prioritizing urgent inquiries, and unifying agent workflows to improve overall customer satisfaction and service efficiency.
• After implementing AI-driven solutions, the company saw a 20% increase in customer satisfaction (CSAT), along with a 70% reduction in response time. Handling time decreased by 40%, allowing agents to resolve issues more efficiently. Additionally, reliance on external support was cut by 50%, enabling the company to bring Tier 1 and Tier 2 support back in-house, improving both service control and cost efficiency.

 

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• Randy Berridge, Director of Community Operations & Insights at Dott, emphasized that the impact of implementing Ada in their customer service operations has been clear and highly effective. She adds, "The AI Agent's seamless handling of inquiries has not only boosted efficiency but also slashed costs significantly."

Research Strategy

Summary

 

 

Complete Findings

Data Security

• Because AI agents operate with a chain of events and interactions that is vast and complex and which is largely hidden, security concerns arise when users struggle to control and monitor the real-time actions of the AI agents.
•  Data exposure and exfiltration can occur during the agent-driven chain of events, denial of service or wallet scenarios can occur from the unbridled consumption of resources, or agent hijacking can occur.
• Data breaches may also occur from coding errors, as well as from the use of third-party libraries or code introduced along the supply chain. Agents built containing hard-coding credentials are an easy target for attackers.
• Mitigating this risk includes employing "multi-layered security measures" and regular auditing of the system for potential threats."

Data Scarcity

• Data scarcity refers to the lack of data that is necessary "to perform meaningful analysis or train machine learning and AI models. It's a scenario where the amount of available data is less than the amount needed to achieve desired outcomes."
• This impacts AI development when training deep learning models that attempt to mimic "human brain functions, require vast amounts of data to learn and make accurate predictions."
•  Critical aspects such as feature selection, data imbalance, and learning failure patterns are impacted restricting the ability of the model to learn effectively while also skewing understanding.
• Training data for AI is limited in many fields. A leading example of this is in healthcare, where datasets for diagnostic AI are limited on account of the sensitive nature of medical records and privacy concerns.
• According to VentureBeat, to address a lack of training data for embodied AI agents, " researchers from Imperial College London and Google DeepMind have introduced Diffusion Augmented Agents (DAAG), a novel framework that leverages the power of large language models (LLMs), vision language models (VLMs) and diffusion models to enhance the learning efficiency and transfer learning capabilities of embodied agents."

Data Bias

•  Biases inherent in training data can be inherited by AI agents, potentially leading to unfair outcomes when the biases are perpetuated and amplified by the agent.
• If the AI agent is used in applications such as hiring, loans, or law enforcement there is room for potential discriminatory outcomes.
• Poor quality data leads to suboptimal AI performance, the effectiveness of the AI agent is dependent on the quality of data used for training.
• Data cleaning, diversity of data, auditing AI systems, monitoring, and transparency in decision-making are all techniques that are used to minimize bias.

Overfitting and Underfitting

•  Overfitting refers to models that learn the noise and outliers as well as the core information from training data. Essentially, the model is too close to the training dataset and has lost the ability to generalize the models, resulting in a model that "cannot perform accurately when unseen data is introduced."
• Underfitting refers to a model that is very simple and does not incorporate the underlying trends in the data. The relationship between the input and the output is not captured accurately resulting in a "high error rate on both the training set and unseen data."
• This is important during implementation because costs may be underestimated, and striking a balance between the two requires a diverse dataset and expertise.
• Addressing overfitting required the creation of a variety of holdout test sets comprised of examples that cannot be "memorized during training and can only be solved through a proper understanding of the target task." Additional mitigation measures are regularization, pruning, cross-validation partitions, data augmentation, and dropout.
• Underfitting can be addressed by amplifying the learning capacity of the model by introducing feature engineering, adding more training data, and the application of ensemble methods. 

 

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Compatibility

• AI agent systems may be required to interact with existing software and hardware not originally designed to work with them, leading to issues in integration such as incompatible communication protocols and data formats.
• Before implementing an AI agent, checking for compatibility with existing hardware and software, as well as the ease of integration into workflows saves time and resources.
• This process will also uncover data silos and data quality issues that hinder consolidation into high-quality datasets for the training and development of AI.

Additional Findings

Ethical, Technical, Regulatory, and Compliance Challenges

• Ethical and societal challenges arise when biased systems lead to outcomes that perpetuate inequality, or create new discrimination.
•  Technical constraints include infrastructure limitations, a need for significant processing power, and AI-specific hardware.
•  Regulatory and compliance challenges can include navigating AI regulations and requirements for the legal and ethical deployment of AI.