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Agent Evaluation, Reliability & Security Lab

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Turn agent demos into production systems with explicit task suites, regression gates, traces, SLOs, abuse tests, tool authorization, human approval and incident recovery.

Part of Agent Mastery 2.0's shared production layer: protocols & interoperability → evaluation/reliability/security → cross-platform portfolio capstone.

Benchmark: OpenAI Agents SDK + AWS AgentCore production-controls documentation (current Sep 2026)

Mastery Ladder

EVALUATION

Prove Task Quality

Golden task sets, realistic user simulations, success criteria and failure taxonomies. Tool-use correctness, groundedness, LLM-as-judge calibration and human review samples. Regression gates across prompt/model/tool changes and adversarial test cases.

RELIABILITY

Operate Under Failure

End-to-end traces, tool spans, handoffs, state transitions, latency/cost and token accounting. Timeout/retry/idempotency, partial failure, fallback, recovery, SLOs and incident drills. Long-running sessions, state cleanup, deployment rollback and change control.

SECURITY

Limit Agent Authority

Prompt-injection tests, sandboxing, data boundaries, tool input/output validation and secrets. Least privilege, approval gates for consequential actions, policy checks and auditability. Threat-model MCP servers, external content, cross-agent delegation and supply-chain dependencies.

Reference Resources

OpenAI Agents SDK - tracing

OpenAI Agents SDK - guardrails

AWS AgentCore - Runtime / Gateway / Identity production controls

Minimum Production Gate

Pass a representative eval suite; meet latency/cost/error SLOs; block unsafe or unauthorized actions; detect a simulated compromise; recover from tool/model failure; preserve trace/audit evidence; and show that failures changed the design before release.