AI Agent Framework
A library or SDK that provides the scaffolding — loops, memory, tool binding — for building AI agents.
What is AI Agent Framework?
AI agent frameworks (LangChain, CrewAI, AutoGen, Pydantic AI, etc.) accelerate prototyping but do not solve governance, routing, or deployment. Enterprise teams typically use a framework inside a platform that adds those missing layers. See AI Agent Frameworks for a comparison, and VDF AI Networks for the platform approach.
What is an example of AI Agent Framework?
A claims team can use an agent framework to build a workflow that reads a claim, retrieves policy language, calls a fraud-scoring service, and drafts a recommendation. The enterprise platform still has to authorize each data source and tool, record the trace, and require approval before payment.
How is AI Agent Framework different from related concepts?
An AI agent framework is a developer toolkit. An AI agent platform is the managed environment used to deploy, secure, govern, observe, and operate agents across teams.
What should enterprises evaluate for AI Agent Framework?
- Check support for deterministic workflows, retries, state persistence, and human approval—not only autonomous loops.
- Confirm that model, vector-store, and tool integrations can be replaced without rewriting the whole agent.
- Test whether traces, prompts, tool arguments, and evaluation data can be exported into your own monitoring stack.
Read the full guide: AI Agent Framework — in-depth article →
Related terms
Putting AI Agent Framework to work?
VDF AI runs governed AI agents on your own infrastructure — on-premises, sovereign cloud, or air-gapped. Book a working session to map the architecture.
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