Building a safe evaluation loop for MCP-connected AI agents
Agent workflows become harder to review as they accumulate tool calls, policy rules, and operational handoffs. A useful starting point is to retain the evidence beside each decision: the trace, the proposed action, the applicable rule, and the reviewer outcome.
For teams working with supported agent traces, tool-call records, policies, MCP manifests, or workflow evidence, MCP Connector Policy Linter v2 returns structured allow/block/review results with risk, cost, trace, and recommended-action fields. It is a review aid, not legal advice, a compliance finding, or an automated control.
Find it here:
https://apify.com/zentrafoundry/mcp-connector-policy-linter-v2
When an agent pipeline changes over time, it also helps to compare structured snapshots rather than rely on a single latest dataset. Dataset Diff Engine v2 compares supported Apify datasets or snapshots and returns change records, source references, stable IDs, confidence, and warnings. It is not a source of truth, certification, or automated remediation.
Find it here:
https://apify.com/zentrafoundry/dataset-diff-engine-v2
The goal is a reviewable workflow: inspect the evidence, keep a human decision point for ambiguous cases, and test any policy change against representative data before enabling it.
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