feedback loops

AI Agent Learning: Governing What Becomes Permanent

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TL;DR AI agent learning needs a boundary between using feedback now and changing future behavior. Preserve operational evidence without automatically turning it into trusted memory, procedures, policy, or training data. Qualify durable changes through provenance, scope, evaluation, approval, and controlled release. Keep reusable knowledge separate from execution authority, and make revocation reach cached content and … Explore: AI Agent Learning: Governing What Becomes Permanent

AI Feedback Governance: Control What Becomes Learning

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TL;DR AI feedback governance controls which observations become persistent changes, where those changes apply, and who can authorize them. A positive rating, a verified resolution, and an approved training example are different things. Route corrections to the appropriate layer, preserve attribution, bind approvals to the exact proposed change, and test the release and withdrawal paths. … Explore: AI Feedback Governance: Control What Becomes Learning

AI Agent Stability: When Retries Become the Incident

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TL;DR AI agent stability depends on controlling the next intervention, not simply limiting model retries. Separate repeated delivery of the same operation from reconciliation and newly authorized recovery. Preserve action identity, execution state, and budgets across restarts. Wait for observable effects instead of relying on timers alone, and keep ownership of each operational decision explicit. … Explore: AI Agent Stability: When Retries Become the Incident

AI Agent Verification: Prove the Outcome, Not the Tool Call

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TL;DR AI agent verification must establish what happened in the target environment, not simply whether a tool returned successfully. Separate request acceptance, configuration changes, runtime convergence, and service outcomes. Bind evidence to the approved resource, release, workload, and measurement window. Use explicit PASS, FAIL, PENDING, and UNKNOWN verdicts, enforced outside the planner. Start with a … Explore: AI Agent Verification: Prove the Outcome, Not the…