Introduction to AI Agent Manager

Building an AI agent has never been easier. Running a fleet of them inside an enterprise has never been harder. The gap opens the moment there is more than one agent and more than one team building them, because no single person can account for what's running, which models it calls, or which tools it can reach. Shadow agents reach production APIs with no oversight. Prototypes ship without guardrails. Agents authenticate with a developer's personal API key, leaving no record of what they did or on whose behalf. These aren't framework problems, which is why they tend to go unaddressed until they become incidents.

This session introduces WSO2 Agent Manager, an open-source, framework-agnostic control plane for securely running, managing, and governing three things together: the agents themselves, the LLMs they call, and the tools they can reach. We'll look at why Agent Management Platforms are emerging as a distinct category in the enterprise AI stack, sitting alongside your API management and identity layers rather than inside your agent framework. From there we turn to the questions that arrive once an agent is working: can you enforce policy across every agent, prove what each one said and what it cost, know whether it's any good, and let the next team reuse it without forking your work? Alongside those sits one more: who is this agent, and what is it allowed to reach? Given its own credentials, its own role, and scoped access to tools, an agent operates under permissions the platform issues and enforces, so that more agents doesn't mean more blast radius.

Whether you're building your first agent or already managing dozens across teams, you'll leave with a practical framework for taking AI agents from prototype to production safely.