AI agent governance, explained.
Guides on governing what AI agents are allowed to do — action authorization, guardrails, evals, and how runtime governance differs from the observability tools you already use.
What are AI evals, and why does agentic AI need them continuously?
Deterministic software is proven once at a release gate and watched for uptime. An LLM agent's input space isn't enumerable — why evaluation has to become a continual, 100%-of-traffic layer with an SME in the loop.
Comparison · 9 minIn-process runtime observability vs. proxy & network-based AI controls
How in-process controls differ from GRC platforms (Credo.ai, Trustible, Monitaur) and network gateways (Lasso Security) — what each layer can see, and why enterprise identity rules often only work in-process.
Guide · 8 minWhat is AI agent governance?
A precise definition, why it matters now that agents take actions, and the four pillars: action authorization, guardrails, evaluation, and audit.
Comparison · 7 minRuntime governance vs. LLM observability: do you need both?
Observability tells you what your agent did; governance decides what it's allowed to do. The difference, a side-by-side, and why most teams need both.
See it in action
Watch the same agent, on a real open-source model, run with and without Parapet — one wipes a database, the other is denied in-process.