A generative AI application can return a wrong answer while every traditional health check is green. Its API may have low latency, its servers may be available, and its logs may show no exception. Yet the answer can refer to an outdated document, invoke the wrong tool, misinterpret an authorized source, or violate a business rule after a prompt change. Production troubleshooting therefore needs evidence that reaches beyond request success into model, retrieval, tool and policy behavior.<\/p>\n
Imagine a fictional support assistant that began recommending an obsolete maintenance procedure after a routine release. Some users report wrong citations; others receive correct facts but a malformed ticket summary. There is no single \u201cmodel problem\u201d until the team reconstructs the request path and change history. The operational task is to distinguish source freshness, retrieval selection, prompt version, model invocation, tool execution and output validation with enough precision to apply a safe repair.<\/p>\n
Diagnosing a failing GenAI rollout requires a causal sequence of evidence, not an overall green dashboard. Record which source passages were retrieved, the prompt and model versions, tool-call permissions, streaming completion state, validator decisions and confirmed business effects. Two equally fluent replies may come from entirely different paths: one may use stale knowledge, while another may request an unauthorized tool action. In independent AWS AIP-C01 troubleshooting practice<\/a> scenarios, a useful exercise is to locate the first evidenced failure\u2014retrieval, authorization, inference, tool execution or validation\u2014instead of inferring correctness from response fluency. Those scenarios are not issuer examination material or evidence of live Bedrock testing. Preserve privacy and log-retention rules; a model-generated explanation is not a substitute for authoritative service records.<\/p>\n A useful trace begins at the authenticated request and records stable correlation identifiers for the task, tenant scope, application build and approved policy version. It can then link retrieval requests, selected source documents, prompt template version, model route, model invocation, tool requests, business-action checks and final validation. Those steps need not expose raw confidential contents in the central log. Source IDs, hashed or restricted references and classified outcomes can provide the causal path while preserving privacy.<\/p>\n Separate work that is deterministic from work influenced by model reasoning. The backend’s authorization decision, input schema validation and successful transaction receipt are facts that can be audited. The model’s explanation of why it selected a tool is useful context but not a replacement for proof that the tool call was allowed. When a support answer is challenged, the investigation should recover both what the model saw and what the trusted application actually did.<\/p>\n The general architecture of CloudWatch, X-Ray and OpenTelemetry on AWS<\/a> provides the distributed-tracing foundation. A GenAI-specific implementation adds model and agent semantic spans, retrieved evidence references, evaluation results and trust-boundary outcomes. Do not simply label one long inference span \u201cAI\u201d and expect a dashboard to explain a wrong answer.<\/p>\n AgentCore observability integrates with Amazon CloudWatch and OpenTelemetry instrumentation for supported agent resources. The current AgentCore observability setup<\/a> describes prerequisites including CloudWatch Transaction Search and agent instrumentation, with operational differences for Runtime-hosted and externally hosted agents. Service-provided metrics and application-generated traces are related, but not identical; operators should confirm which signals are actually available for their deployment.<\/p>\n Amazon Bedrock model invocation logging<\/a> is separate from AgentCore application tracing. It is disabled by default and, according to AWS documentation reviewed October 15, 2026, captures supported invocations through the Telemetry sampling is a design choice. If traces are sampled too aggressively, a rare but harmful sequence may disappear; if every user message is retained indefinitely, the monitoring system becomes a privacy risk. Select sampling and retention based on the consequence of failure and the data classification. Audit records for consequential tool actions may need stronger retention than ordinary model token traces, under controls appropriate to the organization.<\/p>\n Suppose an answer recommends an older procedure. First check the source-of-truth version, the last successful ingestion and the actual retrieved references. If a new policy was not indexed, fix knowledge-base synchronization and staleness gating<\/a>. If the current policy was indexed but not retrieved, investigate metadata filters, embeddings, query transformation or reranking. If the correct passage was retrieved but the model ignored it, inspect prompt construction and response evaluation. Replacing the model without this isolation can produce the same failure in a different style.<\/p>\n Tool failures add another branch. The agent may have asked for an unauthorized object or supplied invalid arguments; the target may have timed out; the call may have committed successfully but lost its response. Each requires different handling. A permission denial must not trigger a more privileged retry. An ambiguous committed action needs an idempotency ledger lookup. A returned tool body may contain indirect prompt injection and should remain untrusted source data rather than become instructions.<\/p>\n Use task-level generative AI evaluation criteria<\/a> as diagnostic evidence, not a substitute for incident reconstruction. Retrieval relevance, factual grounding, tool approval, successful execution and customer outcome describe different stages. If an alert reports an unsafe answer, isolate the first stage that became unacceptable and compare its inputs and versions with a known-good trace. A single model-judge score cannot reveal whether stale data, missing permission or a failed downstream operation caused the defect.<\/p>\nDescribe the GenAI request as a traceable sequence<\/h2>\n
Enable observability deliberately and protect sensitive payloads<\/h2>\n
bedrock-runtime<\/code> endpoint rather than calls through bedrock-mantle<\/code>. Enabled logging may retain sensitive prompts, responses and metadata, so control destination access, encryption, retention and permissible payload collection. Correlate invocation identifiers with application, retrieval and tool traces; no single log proves that a downstream business action was authorized or committed.<\/p>\nIsolate retrieval, model and tool causes independently<\/h2>\n
Version all moving parts before evaluating a release<\/h2>\n