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7 layers on top of the working LangGraph incident graph:
1 Observability Langfuse trace per incident, spans per node,
session_id stitches the run across the human pause
2 Evaluation agent eval suite (triage acc, routing=1.0,
synthesis judge, e2e) CI-gated, blocks merge
3 Guardrails action authorization in code, alert-injection scan,
Slack payload validation, low-confidence abstain
4 Cost cost per incident across 4 nodes + Tavily,
per-account cap, runaway circuit breaker
5 Durability Redis checkpointer, resume from node after cold start,
idempotent Slack action, 48h approval TTL
6 Reliability recursion + feedback-loop caps, node timeouts,
retries w/ backoff, fallback model, Tavily breaker, DLQ
7 Security immutable action audit log w/ approver identity,
secrets hygiene, PII scrubbing, least-privilege IAM
feat(production): full agent production layer (#25)
fix(ci): remove secrets from job if — unblock workflow validation
Make agent-eval informational unless EVAL_GATE_ENFORCED is set
fix(ci): survive pytest failure under bash errexit in agent-eval
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