An AI agentic framework is a software toolkit that makes it easier to build, deploy, and coordinate AI agents.

‍

Main takeaways

• Frameworks like LangChain, CrewAI, and Microsoft AutoGen handle foundational agent infrastructure—reasoning, tool access, memory, and multi-agent orchestration.
• The same capabilities that make frameworks powerful also accelerate the creation of agents that can access sensitive business systems.
• Security teams often have no visibility into which agentic frameworks are deployed in their environment or what permissions the agents built on them hold.
• As developers ship agents faster using these frameworks, governance needs to keep pace.

What is an AI agentic framework?

Building an AI agent from scratch means solving a long list of foundational engineering problems: how does the agent plan multi-step tasks? How does it retain context between steps? How does it access tools, handle failures, and coordinate with other agents? Agentic frameworks address all of this. Rather than engineering those capabilities from the ground up, developers use frameworks to access ready-made components—and focus on what they actually want the agent to do.

‍

Think of a framework as the scaffolding that lets a development team focus on what they want an agent to do—rather than how to make it work.

‍

Common agentic frameworks in enterprise use today include LangChain, CrewAI, Microsoft AutoGen, and LlamaIndex. Each offers a different set of abstractions, but all share the same core purpose: accelerating the development of agents that can reason, act, and integrate with external systems.

‍

What frameworks enable—and expose

Agentic frameworks dramatically reduce the time it takes to build agents that connect to business-critical tools. An engineer can wire an agent into Slack, Google Drive, Salesforce, or a proprietary database in a matter of hours.

‍

That speed is the point. It's also the risk.

‍

When agents are built and deployed quickly—especially by teams outside of security—the permissions those agents hold rarely receive the same scrutiny as a new employee's access request. The agent may have read access to shared drives, write access to CRM records, or the ability to send messages on behalf of users. Without oversight, those access grants accumulate invisibly.

‍

Key risks introduced by agentic framework deployments include:

‍

• Ungoverned tool integrations—Framework-built agents connect to SaaS apps and APIs that may not be logged or monitored by IT.
• Permission sprawl—Agents built for narrow tasks often receive broad access by default, and those permissions are rarely reviewed.
• Supply chain exposure—Frameworks themselves pull in third-party libraries and plugins. Vulnerabilities in those dependencies can affect every agent built on the framework.
• Shadow AI development—Developers or analysts building agents with open-source frameworks may never surface those deployments to security teams at all.

Governance in an agentic world

The emergence of agentic frameworks requires security teams to expand their thinking beyond human identities. Every agent is a non-human identity with its own access profile. Every framework deployment is a potential new access pathway into the organization's SaaS and data environment.

‍

Visibility into which frameworks are in use, which agents have been deployed, and what those agents can access is the starting point for effective governance.

‍

Learn how Nudge Security discovers AI tools and agent integrations across your SaaS estate →