AI agents are the future of enterprise operations, but many organizations are still figuring out how to deploy them responsibly. Choosing the right agent framework is a critical first step.
Sydney Scott
Editorial Strategist, AI
Workday
More than 80% of organizations now use AI agents, and adoption is accelerating fast. Agents are already reshaping the way business gets done, delivering autonomous workflows that drive efficiency and enable teams to focus on higher-value work.
As agents become more embedded into enterprise operations, a shift is happening toward fully agentic AI systems. Enterprises with high AI maturity know it’s not an easy shift to build an autonomous support system that still needs to be managed by human experts, but it ensures that AI acts responsibly and that outcomes are as intended.
That shift means that choosing the right AI agent frameworks has never been more critical. The strengths of an AI agent framework—from scalability and trust mechanisms to domain specificity and enterprise readiness—directly influence how effectively organizations can deploy agents at scale and unlock meaningful value.
More than 80% of enterprise organizations already use AI agents.
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AI agent frameworks are structured software environments that give agents the capabilities they need to operate intelligently and autonomously. A strong framework equips agents to interpret context, take purposeful actions, coordinate with other agents, and interact with the internal and external tools and data sources that power real-world applications.
At a basic level, an AI agent framework answers: How does an agent know what to do, how to do it, and how to do it safely?
By supplying standardized mechanisms for reasoning, memory, action-taking, and oversight, frameworks ensure that agentic systems function with consistency and strategic intent. Most agent frameworks offer a common set of foundational capabilities, including:
Below is a real-world example from Microsoft's AutoGen framework. The diagram shows how a framework organizes multiple types of agents and defines how they interact.
At the center is the base “ConversableAgent,” which provides the core capabilities all agents share. From that foundation, AutoGen creates specialized agents: an AssistantAgent that operates autonomously, a UserProxyAgent that always brings a human into the loop, and a GroupChatManager that coordinates multiple agents working together.
Robust frameworks like this matter because they provide the foundational infrastructure agentic systems rely on. Without them, teams would need to engineer core mechanics themselves—such as how agents plan and sequence actions, store and recall context, and connect securely to data and tools—and ensure their behavior is monitored and controlled.
These capabilities are technically complex and expensive to build from scratch. Frameworks consolidate that complexity into a stable foundation, allowing teams to focus on designing high-value agent behaviors rather than reinventing underlying components.
As a result, organizations gain faster agent development cycles, more consistent agent performance, and greater confidence that their agentic systems can scale safely across enterprise environments.
Knowing how different AI agent frameworks are designed helps match agent use cases to the right foundation and architecture.
Organizations evaluating AI agent frameworks are looking for platforms that can support reliable agents and complex multi-step tasks without sacrificing on governance or flexibility. Leading frameworks take different approaches—some focus on graph-based control flow, others on data-centric retrieval, and some on multi-agent coordination or orchestration.
Understanding each framework’s design and the real-world examples of AI agents they power helps teams match their agent use cases to the right foundation and architecture. Here are five of the top AI agent frameworks available to businesses in 2026.
LangChain is an open-source framework for building AI applications with large language models, and LangGraph extends it with a graph-based runtime for long-running, stateful workflows and agents.
Key capabilities:
Best suited for: Teams that want fine-grained control over agent workflows, especially multi-step or multi-agent applications that benefit from explicit graph structure and state management.
AutoGen is an open-source programming framework from Microsoft for building agents and multi-agent applications, with a strong focus on conversational and collaborative interactions.
Key capabilities:
Best suited for: Applications that center on conversational agents, collaborative problem-solving, or scenarios where humans and agents need to work together within the same workflow.
Semantic Kernel is Microsoft’s lightweight, open-source SDK for building AI agents and integrating large language models into .NET, Python, and Java applications.
Key capabilities:
Best suited for: Engineering teams that want to embed agent capabilities directly into existing applications, especially in Microsoft-centric environments, while keeping a clear separation between orchestration logic and business code.
LlamaIndex is an open-source framework that began as a data layer for LLM applications and has evolved into a developer-focused framework for context-aware AI agents and workflows.
Key capabilities:
Best suited for: Knowledge-intensive applications—such as research assistants, internal copilots, and domain-specific agents—that must reliably interact with complex or proprietary data sources.
CrewAI is an open-source Python framework for building and orchestrating multi-agent "crews,” or groups of specialized agents that collaborate to complete tasks.
Key capabilities:
Best suited for: Teams that want to structure work as collaborative, role-based multi-agent processes—such as content pipelines, research workflows, or operational automations—without building their own orchestration layer from scratch.
Intentional agent framework selection creates a foundation that can support more advanced AI capabilities over time.
As AI agents become core to enterprise operations, the frameworks behind them determine how effectively they can operate with clarity, context, and control. The right foundation shapes everything from how agents reason and collaborate to how they manage state, access data, and adhere to governance requirements.
Strong frameworks give teams the confidence to scale agentic systems into critical workflows, ensure reliable outcomes, and maintain oversight as autonomy increases.
Key considerations when selecting a framework for your business:
With an intentional approach to framework selection and oversight, organizations can create an agentic foundation that grows with their business and supports more advanced and responsible AI capabilities over time.
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