Observability and monitoring

Observability is essential for operating event-driven, AI-powered systems at scale. Unlike monolithic applications, serverless and generative AI systems are distributed, stateless, and composed of ephemeral compute and integrated AI services (for example, Amazon Bedrock and Amazon SageMaker). These characteristics require new thinking around visibility, correlation, and accountability.

Without observability, teams face the following issues:

• Blind spots in execution and agent behavior

• Undetected cost anomalies or performance regressions

• Limited insight into model outputs and large language model (LLM) quality

• Difficulty in root-cause analysis across asynchronous workflows

Observability plays a critical role in the following areas of serverless AI:

• AI outputs – LLMs are non–deterministic. Logging and inspecting their outputs is the only way to validate their correctness over time.

• Serverless execution – AWS Lambda, AWS Step Functions, and Amazon EventBridge don't run on fixed hosts. Monitoring needs to be trace-based, not server-based.

• Costs and latency – Amazon Bedrock usage is based on tokens. Lambda and Step Functions are charged per duration and execution.

• Security and governance – Prompt logs, agent tool usage, and API calls must be audited and scoped to identity and role context.

• User experience – Failures, delays, or hallucinations impact trust. Early detection of these issues is key to maintaining user confidence in AI systems.

Key observability metrics to monitor

The following table describes the importance of key metrics related to observability and monitoring.

Metrics category	Metric	Why the metric is important
Agent behavior	Tool selection rate Invalid tool invocations	Reveals misalignment between intent and action.
Cost trends	Inference cost per user or session	Enables FinOps reporting and tiered model routing decisions.
Invocation metrics	Lambda invocations Error rate Cold starts	Validates pipeline stability and error resilience.
Knowledge base retrieval	Hit/miss ratio Grounding relevance score	Measures how well the RAG pipeline is performing.
Latency	Inference latency per model	Detects slowdowns in Amazon Bedrock or SageMaker. Optimizes user response time.
Prompt and response quality	Hallucination rate Fallback rate	Ensures grounding is working and prompts are behaving as expected.
Security and access	Agent and tool usage by IAM role	Ensures principle of least privilege and traceability.
Token usage	Total input and output tokens (Amazon Bedrock)	Controls cost. Detects prompt bloat or model misuse.
Workflow health	Step Functions workflow failures, retries, and timeouts	Surfaces orchestration issues and retry loops.

AWS services for observing serverless and generative AI

The following table describes AWS services and features that support observability for serverless and generative AI applications, including their ideal use cases.

AWS service	Description	Ideal use case
Amazon CloudWatch Logs	Captures logs from Lambda, Step Functions, Amazon Bedrock Agents, and Amazon API Gateway	Debugging Audit trails User session tracing
Amazon CloudWatch metrics	Custom and service-generated key performance indicators (KPIs), such as invocation count, duration, and token count	Dashboarding Alerts Trend analysis
AWS X-Ray	Traces across serverless flows, including Lambda, API Gateway, and Step Functions	Root-cause analysis Latency tracking Dependency mapping
CloudWatch embedded metric format	Structured logging for advanced metrics in log streams	Enable analytics without separate metrics calls
Amazon Bedrock agent trace and model invocation logging	Native Amazon Bedrock Agent execution trace, tool calls, and RAG insights	Monitor agent behavior and troubleshoot failures
Amazon EventBridge Pipes and schema registries	Tracks and validates event formats flowing through your pipeline	Prevent malformed events Ensure contract consistency
AWS CloudTrail	Logs all API calls and identity context	Compliance Security audits Agent and tool usage by role
Amazon OpenSearch Service	Indexes inference responses, structured logs, or audit records	Semantic search of responses Observability dashboards
Amazon CloudWatch Synthetics	Simulates traffic to test endpoints or workflows proactively	Ensure uptime and regression monitoring across versions

Example: Monitoring an agent-based support workflow

To effectively monitor an agent-based support workflow, consider using the following metrics at their associated workflow stage:

1. User query to API Gateway – Monitor response time and 5xx errors.

2. Pre-processor Lambda function – Monitor cold starts and parsing failures.

3. Amazon Bedrock agent – Monitor prompt, tool call traces, token cost, and latency.

4. Tool Lambda function (for example, getOrderStatus) – Monitor execution time and tool invocation count per user.

5. RAG query through knowledge base – Monitor relevance score and missing grounding.

6. Post-processor Lambda function – Monitor schema validation and fallback triggers.

7. Logs CloudWatch and OpenSearch – Monitor session logs, trace IDs, and model response quality.

8. Alarms – Monitor alerts for high failure rates, spikes in cost per session, and degraded latency.

Best practices for observability

Consider the following best practices for observability in serverless and generative AI workflows:

• Instrument AI flows with structured logs to enable correlation across components (for example, user session, trace ID, and model response).

• Use consistent logging schema to support downstream parsing, alerting, and analytics pipelines.

• Emit custom metrics per layer to help trace model-related errors compared to infrastructure issues.

• Tag logs with environment and context to enable filtering by user role, region, version, or team.

• Use anomaly detection alarms to detect token surges, latency spikes, or output drift.

• Correlate LLM response logs with downstream impact to link agent outputs to decisions, escalations, or failures.

• Automate report generation through weekly dashboards with prompt cost, model usage, and fallback rates to drive accountability and improvement cycles.

Summary of observability and monitoring

In AI-driven serverless systems, you don't monitor hosts. Instead, you monitor behavior, cost, and correctness. Observability provides the foundation for operational resilience, cost control and forecasting, LLM performance evaluation, governance and compliance, and continuous prompt and agent improvement.

Native AWS services that support observability and monitoring, along with structured, event-aware telemetry provide the necessary capabilities. With these capabilities in place, teams can confidently operate AI workloads at scale, knowing what's happening, where, and why.