An agent framework is a software library that developers use to build AI agents. It supplies the loop that calls a model, the plumbing to connect tools and memory, and patterns for chaining steps or splitting work between agents. It helps you build an agent quickly. It does not, by itself, decide what that agent is allowed to do in your business.
What does an agent framework give a developer?
The repetitive parts. Most agents need the same scaffolding: send a request to a model, read back a tool call, run the tool, feed the result back, repeat until done. A framework packages that loop. It usually adds adapters for common data stores, a way to define tools, a memory module, and support for one agent handing work to another.
That saves weeks of engineering. It also means many agents share the same shape, which makes them easier to read and maintain.
Why is a framework not enough once agents act?
A framework's job ends at "the agent can call this tool". Whether the agent should call it, for this customer, at this amount, on this evidence, is a separate question. In most frameworks the answer lives in the code the developer writes around each tool, or in the prompt. That puts authority in many places, owned by whoever last edited them.
In a regulated business that is the gap that matters. A supervisor will ask who approved this agent to move money, on what evidence, and what stopped it from going further. A framework has no built-in answer, because it was never meant to.
How is a framework used in practice?
Teams pick a framework, define tools as functions, write a system prompt, and test the agent against sample tasks. In a governed setup, the tool definitions do not hold the rules. Each tool call is sent to a control layer that checks the action against the agent's role, certificate, sources and limits, and returns a verdict before anything executes. The framework drives the reasoning; the control layer decides.
For example, an engineering team at an insurer uses a framework to build a claims triage agent. The framework handles document reading and the loop. When the agent proposes to mark a claim "fast-track", the call goes to the governance layer, which allows it for claims under the insurer's limit and holds the rest for a handler.
How is an agent framework different from an agentic operating system?
A framework is a build tool; an agentic operating system is the runtime authority. The framework gives you agents. The operating system decides what each agent may do, proves it, enforces it at the moment of action and records the verdict, across every agent regardless of which framework built it. The full comparison is in agent framework or agentic operating system?
Is an agent framework the same as an AI governance framework?
No, though the words collide. Governance frameworks are sets of principles and practices for managing AI risk, such as the NIST AI Risk Management Framework or Singapore's Model AI Governance Framework from IMDA. They tell an organisation what good governance looks like. An agent framework is code. You can use one to help meet the other, but neither replaces it.
Where Pryme fits
Pryme is not an agent framework. It sits between models and the business as a governed agentic operating system, and agents built with any framework can route their actions through it. The framework keeps building the agent; Pryme returns the verdict on each action the agent proposes. See the governed agentic operating system, explained.
Questions people ask
Do you need a framework to build an AI agent?
No. A small agent can be written directly against a model provider's interface. Frameworks pay off as the number of tools, agents and steps grows.
Can you switch frameworks later?
Usually, with some rewriting. Keeping authority out of framework code makes switching far less risky, because the rules do not move with the rewrite.