From AI demo to production-grade operations.

Every prompt, every retrieval, every tool call, every model response, every agent-to-agent message, every memory write, every policy decision becomes a captured trace. The instrumentation is built on OpenTelemetry-compatible spans flowing into Amazon Bedrock AgentCore Observability and Amazon CloudWatch Generative AI Observability.

The outcome is reproducibility. When an incident occurs at 14:32, an engineer can pull the exact span that produced the response, the prompt that led to it, the retrievals that grounded it, the tools that were called, the policies that allowed or blocked the action, and the user identity that initiated the request. Nothing is lost. Nothing is inferred after the fact.

Token spend is the single most common surprise after deployment. The engagement attributes cost at the token, agent, use-case, tenant, and user level — connected directly to AWS Cost Explorer and AWS Budgets. Expensive prompts surface before they become expensive bills. Retrieval over-fetch shows up as a cost signal, not as a year-end review surprise.

Model right-sizing recommendations identify where a lighter model will outperform a heavier one at a fraction of the cost. The first cost review typically finds 20–40% in waste in the first quarter — and the cadence locks the optimisation into the operating rhythm rather than leaving it as a one-off audit.

Production AI agents fail differently from production microservices. They drift. They hallucinate. They become less grounded as the retrieval corpus changes. They become more confident on the wrong answer over time. Continuous evaluation runs on Amazon Bedrock AgentCore Evaluations with curated suites against your use cases — hallucination grounding, tool-call correctness, prompt-injection resistance, retrieval faithfulness, response safety, bias across demographic cohorts, and policy adherence.

LLM-as-judge evaluators grade what a deterministic test cannot grade. Ground Truth evaluators assert expected tool sequences and exact-output behaviours. Custom evaluators capture the business-specific rubrics your team defines. Failures are not just logged — they are routed to the named owner, attached to a finding, and tracked to closure.

Centralised policy enforcement is established on Amazon Bedrock AgentCore Policy. Natural-language rules — defined by your risk function — are compiled into Cedar policies enforced outside agent code. Tool-use restrictions, data-access controls, human-approval requirements and prohibited-action lists are applied uniformly across every agent in scope, with version control and signed change history.

When something goes wrong, the policy log explains why the agent did what it did, who approved the exception that allowed it, and which control was the last line of defence. For enterprises in MAS, APRA, RBI, HKMA, OJK or BNM scope, this is wired to Glacien’s AgentGuardian so every operational event flows into the regulator-defensible quarterly Evidence Pack.