The past year has given us an explosion of frameworks for building AI agents. Evaluating them, however, still often comes down to running isolated prompts and comparing outputs.
That isn't how agents behave in production.
They call tools. They interact with APIs. They maintain context across multiple turns. They make decisions that depend on previous actions. And they're expected to follow organizational policies while doing all of that.
That's the problem we're working on at Humanbound.
Humanbound is an open source testing engine for AI agents that evaluates real behavior instead of isolated prompts. You can test live endpoints, simulate multi-turn conversations, exercise tool use, and score results against your own policies.
One idea I particularly like is closing the loop between evaluation and enforcement. When a test exposes an undesirable behavior, that failing test can become a deployable guardrail rule instead of remaining just another item in a report.
We're still early, which means community feedback matters far more than polished demos.
If you're building an AI agent, I'd love for you to point Humanbound at it.
Run the quickstart. Try your own scenarios. Break things. Tell us what doesn't work. The edge cases are where evaluation frameworks get interesting.
If local deployment matters to you, Humanbound also supports fully local, air-gapped execution with Ollama.
Documentation: https://docs.humanbound.ai
GitHub: https://github.com/humanbound/humanbound
I'm looking forward to hearing what works, what doesn't, and what you'd like to see next.
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