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A meta-agent workflow that generates red-team cases, runs regression suites, scores agent behavior, and blocks production rollout when a workflow violates its safety contract.
Designed for
AI companies shipping agents that need repeatable evals, guardrail testing, and release confidence
Operating goal
Catch behavior regressions before agent workflows reach production users.
3 steps from trigger to verified handoff, with success and failure paths.
1 MCP layer and 4 connected tools with explicit auth and risk levels.
3 guardrails, 3 evals, and 1 harnesses before production use.
Creates representative happy-path and adversarial tasks.
Reasoning model
Runs the suite and captures traces.
Deterministic execution route
Scores results and decides whether release can proceed.
Policy reasoning model
Loads the workflow goal, allowed actions, escalation policy, and output contract before the agent plans work.
A skill for generating scenario suites, red-team cases, pass/fail rubrics, and regression gates for agents.
Centralizes high-risk action checks for writes, secrets, customer data, billing, deploys, and public communications.
Exposes scenario datasets, trace reads, judge prompts, and release-gate results.
Generate or refresh scenarios from the workflow contract.
Execute scenarios and capture traces.
Compare metrics to thresholds and prepare a release decision.
Open it in Codelit, refine it with the agent chat, then generate the architecture or product board from the same workflow spec.
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A Slack-native engineering agent that receives operational requests, gathers context from tickets and repos, routes work to specialist agents, and drafts auditable responses before anything risky happens.