AgentCore Production Optimization: A UK SRE Playbook for Silent Agent Failures

What AgentCore production optimization is — and what it is not

On 17 June 2026, AWS announced Amazon Bedrock AgentCore optimization capabilities that turn production traces into a continuous improvement loop. This article is the UK SRE / CAB playbook for that loop — focused on silent agent failures (no error signal, dashboard still green) and how to prove prompt and tool changes before fleet rollout.

This is not a rehash of already-published AIATS posts on:

• AgentCore Gateway HTTP Passthrough
• Runtime Targets
• Memory IngestData
• Policy Cedar
• Identity Consent Portal
• MCP Lambda diagnostics

Those are deployment and access controls. Optimization is the quality/SRE loop: failure, intent, and trajectory insights across hundreds of sessions, plus recommendations, batch evaluation, and A/B testing before you change what agents say and do in production.

Ground this work in AgentCore Evaluations (GA March 2026): Online (sample live traces) and On-demand (CI/CD), with built-in and custom evaluators integrated with AgentCore Observability. Optimization extends that story from “score the run” to “find silent failure patterns and prove the fix.”

The UK risk: silent failures under green dashboards

Enterprise agents fail quietly: wrong tool choice, abandoned trajectories, polite but incorrect answers, policy-adjacent behaviour that never throws. Traditional SRE alerts on 5xx and latency; agent estates need behavioural signals.

AgentCore optimization surfaces:

• Failure insights — recurring patterns, including silent behavioural failures, with root cause and ranking by prevalence.
• Intent insights — cluster sessions by user goal.
• Trajectory insights — group paths through tasks; highlight common paths vs outliers.

You can run continuous monitoring or a targeted investigation in minutes — useful when a CAB asks “did Friday’s prompt change increase silent failures?”

Recommendations grounded in traces and evals

Optimization does not stop at charts. It produces recommendations for prompt and tool-description fixes, grounded in traces and evaluation outputs, with rationale. UK SRE teams should treat those recommendations as change candidates, not auto-apply:

1. Capture the recommendation and linked evidence (trace clusters / eval scores).
2. Propose the prompt or tool-description delta in the same ticket as any agent config change.
3. Run batch evaluation against a defined test dataset with multiple evaluators before rollout.
4. Where risk warrants, run A/B testing: split live traffic and require statistical evidence before fleet rollout.

That sequence is the production gate — not “merge because the recommendation looked sensible.”

Where it runs

Optimization works wherever agents run: AgentCore Runtime, Lambda, EKS, or non-AWS runtimes that emit the traces/evals the service expects. Do not block adoption solely because some agents sit outside AgentCore Runtime — design the observability path so silent-failure insights still cover the estate you care about.

Regions and UK data residency

Per AWS What’s New for the optimization capabilities:

• Failure / intent / trajectory insights: preview in 13 regions.
• Batch evaluations / recommendations / A/B testing: GA in 14 regions.

For Evaluations GA (March 2026), documented regions include Europe (Frankfurt) and Europe (Ireland). London (eu-west-2) may not be listed — UK programmes that require data residency should explicitly choose Ireland or Frankfurt (or another approved EU region on the GA list) and record that choice in the DPIA / architecture decision record. Do not assume London is available for Evaluations or optimization features without checking the current regional matrix for your account.

Evaluations GA — what to wire before the optimization loop

Use Evaluations as the measurement spine:

• Online — sample live traces in production (or pre-prod with production-like traffic).
• On-demand — CI/CD gates before merge or promote.
• 13 built-in evaluators, plus Ground Truth.
• Custom evaluators via LLM or Lambda code.
• Integration with AgentCore Observability.

Without Online/On-demand evals and Observability, optimization insights lack a closed loop: you will see patterns but cannot prove a fix moved the needle.

UK SRE / CAB production-gate checklist

Numbered steps for change records when improving agent prompts, tools, or evaluation weights:

1. Confirm AgentCore Observability is on for the in-scope agents (Runtime, Lambda, EKS, or external) and traces are landing.
2. Enable Evaluations: Online sampling policy + On-demand suite in the promotion pipeline.
3. Select built-in evaluators relevant to the risk (plus Ground Truth where you have labelled cases); add custom LLM/Lambda evaluators only with named owners.
4. Record regional placement (Ireland / Frankfurt / other GA region) against UK residency requirements — do not assume eu-west-2.
5. Baseline: capture failure / intent / trajectory insights for a defined window; note prevalence-ranked silent failures.
6. Accept optimization recommendations only as CAB candidates with linked traces and rationale.
7. Implement prompt/tool-description changes in a branch or non-prod agent version.
8. Run batch evaluation on the defined dataset with multiple evaluators; attach pass/fail thresholds to the ticket.
9. For material production impact, run A/B with traffic split; require statistical evidence before 100% rollout.
10. After rollout, re-check failure-insight prevalence for the same intent/trajectory clusters — prove silent failures fell, not just that latency stayed flat.
11. Keep this loop separate from Gateway HTTP Passthrough, Cedar policy, Consent Portal, and MCP Lambda diagnostic workstreams — different owners, different evidence.
12. Sign CAB when: baseline silent-failure patterns documented, batch (and A/B if required) evidence attached, residency ADR current, and rollback (prior prompt/tool version) is one promote away.

What “good” looks like for UK enterprises

SRE and AI product owners can answer: which silent failure patterns dominate this month, which intents drive outliers, what prompt/tool change is proposed, how batch and A/B proved it, and which EU region holds the eval/optimization data. Green infrastructure dashboards alone are not enough — silent failure detection is the control that stops polite, wrong agents from shipping under CAB radar.