AI-agent failures are often operational rather than model failures: an unexpected tool is called, a request uses broader access than intended, or a workflow proceeds without enough evidence for a human to review it.
A practical safety layer is small and explicit:
- Capture the intended tool-call trace and input context.
- Compare it against a readable policy.
- Produce allow, block, or review signals a human can inspect.
- Keep the decision record with the workflow change.
Nimblique Studio publishes separate building blocks for teams evaluating parts of that workflow:
- MCP Connector Policy Linter v2 checks supported agent traces, policies, MCP manifests, and workflow evidence. Find it here: https://apify.com/zentrafoundry/mcp-connector-policy-linter-v2
- Dataset Diff Engine v2 compares supported datasets or snapshots and exposes structured changes and warnings. Find it here: https://apify.com/zentrafoundry/dataset-diff-engine-v2
- Actor Flight Recorder v2 keeps an inspectable execution record for troubleshooting workflow behavior. Get it here: https://apify.com/zentrafoundry/actor-flight-recorder-v2
These are evaluation tools, not a replacement for access controls, security review, or accountable operating procedures.
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