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ISG Research is happy to share insights gleaned from our latest Buyers Guide, an assessment of how well software providers’ offerings meet buyers’ requirements. The AI Agents: ISG Research Buyers Guide is the distillation of a year of market and product research by ISG Research.
The shift toward artificial intelligence (AI)-driven software in enterprises and workforce 
processes has generated a rising demand for more intelligent applications and systems across enterprises. However, integrating and utilizing decades of accumulated knowledge and data presents a significant challenge due to the complexity of legacy systems and existing business practices. Embedding intelligence through AI necessitates software components—known as agents—that can operate autonomously without constant human interaction or oversight. With the emergence of AI platforms that integrate agentic design, generative AI (GenAI) and conversational interfaces for worker engagement, the foundation has been established for the rise and adoption of AI agents.
ISG Research defines AI agents as autonomous software entities that perceive their environment, make context-aware decisions and take actions based on perception and reasoning. While many AI agents rely on simple rule-based logic and are primarily reactive, agentic AI expands on more advanced platforms that enable self-directed behavior. These agents can operate across multiple processes and systems, focusing on specific goals without requiring constant oversight. In many instances, AI agents can run independently or coordinate across different software providers and enterprise environments. Although workers can initiate or interact with these agents, they are designed to function autonomously and engage only as needed to achieve desired outcomes.
AI agents have evolved from simple rule-based programs into intelligent, autonomous components capable of reasoning, decision-making and acting within complex business and digital environments. Initially characterized by reactive behavior, they progressed to multi-agent systems, leveraging machine learning (ML) to operate more independently and
interactively with both workers and machines. Today, with the power of large language models (LLMs) and GenAI, software-based AI agents can perform a wide range of tasks across departments and teams, from retrieving knowledge to orchestrating actions across enterprise systems. Consequently, AI agents are emerging as a foundational layer in modern software architecture, driving new levels of productivity, adaptability and intelligence in enterprise applications. By 2028, software providers will blend agentic AI with collaboration and communications software to provide digital assistants that increase productivity by guiding actions.
Enterprises need AI agents that can reliably support a wide range of activities and tasks, understand context, operate independently and integrate seamlessly with existing systems. These AI agents should function autonomously across workflows, engage with humans as necessary and adapt based on situational context and knowledge so that it can take appropriate actions while efficiently utilizing context and data. Key requirements include operating in a secure and governed manner, with clear limits on their functionality. Most importantly, AI agents must deliver tangible business value by improving efficiency, reducing manual effort and continuously learning in order to enhance performance over time. Ultimately, AI agents save time and resources, reducing costs associated with tasks that would otherwise require manual intervention from workers.
To succeed with AI agents, enterprises must have both the necessary skills and resources to support agentic AI platforms and autonomous capabilities that can be invoked by applications or through conversational AI software. Thorough ingestion and preparation of data from existing applications is critical, as decades of customized systems and "dirty cores" can pose significant challenges in terms of resources and time. The AI agent software must also integrate and interoperate with other applications and systems to achieve intended goals, unless they operate exclusively within the environment and data of the software providers. Additionally, there is a growing need to address privacy and security concerns associated with AI operating autonomously, as well as ensuring compliance with digital sovereignty regulations across different countries and regions.
The rapidly evolving GenAI market is increasingly focusing on agentic AI as enterprises aim to incorporate generative capabilities into business processes and workflow automation. While LLMs excel at generating content such as text, images and videos, they are not inherently designed to generate actions. To address this limitation, foundation models need to be trained on specific actions and outcomes to foster goal-oriented behavior. This has given rise to large action models (LAMs) in some agentic AI systems, while in others, LLMs are being enhanced with training data that includes actions and results. Consequently, modeling and evaluation tools must evolve to support agents and their associated behaviors. Effective AI agents need to perceive their environment, reason about goals and determine appropriate actions—operating across both pre-built and custom applications. Unlike traditional GenAI, which is prompt-driven, AI agents must also be capable of executing tasks autonomously.
AI agents research is designed to assist enterprises in navigating the new era of agentic and conversational AI, which is driving innovation in intelligent business operations across the workforce and business processes. This comprehensive research evaluates the full spectrum of AI agent offerings and can be used in conjunction with studies on collaborative AI platforms, suites and conversational AI. Unlike separate evaluations that focus on individual categories, this research specifically examines how these technologies integrate to create a unified AI ecosystem—spanning conversations to agents—that enhances productivity and employee engagement.
To prepare for AI agent software, enterprises should first ensure that their data infrastructure is well-governed, high-quality and seamlessly integrated across systems. This data needs to be accessible to LLMs and capable of supporting large-scale, action-oriented systems, often referred to as foundational models. Organizations should identify a portfolio of high-impact use cases where autonomy and intelligent decision-making can create value, such as in customer service, sales and marketing, finance, supply chain management and IT operations. Establishing strong AI governance frameworks for security, transparency and accountability is essential, as is equipping teams to collaborate effectively with AI agents. Finally, enterprises should adopt flexible platforms and methodologies that support stable operations while allowing for continuous experimentation to evolve AI agent capabilities over time.
The ISG Buyers Guide™ for AI Agents evaluates software providers and products in key areas: platform support, intelligence and workflow, analytic and insights, AI overall, communication administration and specific AI support with agentic AI.
This research evaluates the following software providers that offer products that address key elements of AI Agents: Appian, Automation Anywhere, AWS, C3.ai, Fractal, Google, Gupshup, IBM, Microsoft, Newgen, Oracle, Pega, Salesforce, SAP, ServiceNow, SS&C Blue Prism, UiPath, Verint, Zendesk and Zenvia.
This research-based index evaluates the full business and information technology value of AI agents software offerings. We encourage you to learn more about our Buyers Guide and its effectiveness as a provider selection and RFI/RFP tool.
We urge organizations to do a thorough job of evaluating AI agents offerings in this Buyers Guide as both the results of our in-depth analysis of these software providers and as an evaluation methodology. The Buyers Guide can be used to evaluate existing suppliers, plus provides evaluation criteria for new projects. Using it can shorten the cycle time for an RFP and the definition of an RFI.
The Buyers Guide for AI Agents in 2025 finds Oracle first on the list, followed by ServiceNow and Salesforce.
Software providers that rated in the top three of any category ﹘ including the product and customer experience dimensions ﹘ earn the designation of Leader.
The Leaders in Product Experience are:
- Oracle.
- ServiceNow.
- Salesforce.
The Leaders in Customer Experience are:
- ServiceNow.
- Verint.
- Oracle.
The Leaders across any of the seven categories are:
- Oracle, which has achieved this rating in seven of the seven categories.
- ServiceNow in six categories.
- Salesforce and Verint in two categories.
- AWS, IBM, Microsoft and UiPath in one category.
The overall performance chart provides a visual representation of how providers rate across product and customer experience. Software providers with products scoring higher in a weighted rating of the five product experience categories place farther to the right. The combination of ratings for the two customer experience categories determines their placement on the vertical axis. As a result, providers that place closer to the upper-right are “exemplary” and rated higher than those closer to the lower-left and identified as providers of “merit.” Software providers that excelled at customer experience over product experience have an “assurance” rating, and those excelling instead in product experience have an “innovative” rating.
Note that close provider scores should not be taken to imply that the packages evaluated are functionally identical or equally well-suited for use by every enterprise or process. Although there is a high degree of commonality in how organizations handle AI agents, there are many idiosyncrasies and differences that can make one provider’s offering a better fit than another.
ISG Research has made every effort to encompass in this Buyers Guide the overall product and customer experience from our AI agents blueprint, which we believe reflects what a well-crafted RFP should contain. Even so, there may be additional areas that affect which software provider and products best fit an enterprise’s particular requirements. Therefore, while this research is complete as it stands, utilizing it in your own organizational context is critical to ensure that products deliver the highest level of support for your projects.
You can find more details on our community as well as on our expertise in the research for this Buyers Guide.
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