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AgentCore Observability¶

What it is¶

AgentCore Observability is the Bedrock AgentCore surface that provides end-to-end tracing of every agent interaction — from initial request through tool execution to final response. It is the "real-time visibility" half of AgentFlo's Trust pillar: merchants need to see what agents are doing while they do it, not only after something goes wrong.

What it captures¶

Per the AgentFlo Part 2 post, AgentCore Observability emits structured traces for each agent turn (Source: sources/2026-08-21-aws-how-agentflo-built-ai-sales-agents-with-amazon-bedrock-agentcore-part-2):

Model latency per turn.
Tool invocation sequences — which tools were called, in what order.
Token usage.
Error rates.

These traces flow into Amazon CloudWatch, where AgentFlo builds dashboards (active sessions, response times, tool-call patterns), tracks P50/P95 latency and throughput across agent types, alerts when behavior deviates from baselines, and attributes cost per merchant / per agent / per conversation. Full request-to-response traces enable trace-level debugging.

Where it sits¶

Observability is the visibility layer that complements the enforcement layers of AgentFlo's three-layer guardrails: Policy and Gateway prevent unsafe action; Observability surfaces what actually happened so deviations can be caught and fed back into agent improvement. It is distinct from the interaction-capture analytics pipeline (Data Firehose → S3) that AgentFlo uses for ROI/cost-vs-revenue attribution, though both contribute to the concepts/observability posture.

Caveats¶

Architecture-level disclosure only. The post names Observability's outputs (traces, latency/throughput metrics, cost attribution) and its CloudWatch sink but not its internal trace model, sampling, retention, or overhead. Treat as a stub that will thicken as a dedicated AgentCore Observability source lands.

Seen in¶

sources/2026-08-21-aws-how-agentflo-built-ai-sales-agents-with-amazon-bedrock-agentcore-part-2 — per-turn structured traces (model latency, tool sequences, token usage, error rates) into CloudWatch; P50/P95 latency + throughput per agent type; baseline deviation alerts; per-merchant/agent/conversation cost attribution.
sources/2026-09-11-aws-from-zero-shot-forecast-to-purchase-order-with-agentcore — auto-traces every agent invocation (inputs, outputs, tool calls, retry attempts, latency) to CloudWatch with no additional instrumentation, giving each order decision a complete audit trail (which agent ran, which tools were called, what Chronos2 returned, why a quantity was recommended). CloudWatch Logs Insights queries surface patterns like "which SKUs trigger the most constraint violations" without re-running the pipeline. Answers the concern "can we reconstruct why a decision was made?"