When agents can change production
Define the action, contain its scope, and verify what changed at the destination.
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Define the action, contain its scope, and verify what changed at the destination.
Define inputs, permissions, idempotency, verification, and stop conditions for one operational capability before expanding its authority.
Check agent status, terminal persistence, and recovery before adopting Herdr.
Decide which workflow deserves an agent, where a fixed automation is sufficient, and how to measure the work left for responders.
The stable Kubernetes attributes processor helps connect AI-agent traces to their workloads. Check the metadata joins before upgrading.
Test evidence retrieval, long-session consistency, tool boundaries, and cost before letting a model influence production changes.
Read validated architectures as a starting point for workload, recovery, and support testing, not a promise of automatic reliability.
Compare detection, correlation, investigation, and automation using your incidents, review costs, and failure paths.
Use operational state, visible defaults, and reversible transitions to evaluate complexity without discarding necessary capabilities.
Account for context limits, retries, latency, and the work behind a useful result.
Use language-model assistance for evidence review, draft communication, and practice, with a clear boundary around production decisions.
Follow evidence through technical failure and incident response, then test whether the proposed correction changes the mechanism.