AWS bedrock-agentcore documentation change
Summary
Fixed a broken heading text and replaced multiple straight apostrophes with curly apostrophes throughout the document for consistency.
Security assessment
The heading fix improves readability, and the apostrophe changes are typographical. None of the content alterations introduce, modify, or remove information related to security features, vulnerabilities, or security best practices. The changes are purely cosmetic and editorial.
Diff
diff --git a/bedrock-agentcore/latest/devguide/observability-telemetry.md b/bedrock-agentcore/latest/devguide/observability-telemetry.md index 1fe7a0507..02ec76b53 100644 --- a//bedrock-agentcore/latest/devguide/observability-telemetry.md +++ b//bedrock-agentcore/latest/devguide/observability-telemetry.md @@ -5 +5 @@ -SessionsTracesAgent SpansRelationship +SessionsTracesSpansRelationship Between Sessions, Traces, and Spans @@ -49 +49 @@ By default, AgentCore provides a set of observability metrics at the session lev -A trace represents a detailed record of a single request-response cycle beginning from with an agent invocation and may include additional calls to other agents. Traces capture the complete execution path of a request, including all internal processing steps, external service calls, decision points, and resource utilization. Each trace is associated with a specific session and provides granular visibility into the agent's behavior for a particular interaction. +A trace represents a detailed record of a single request-response cycle beginning from with an agent invocation and may include additional calls to other agents. Traces capture the complete execution path of a request, including all internal processing steps, external service calls, decision points, and resource utilization. Each trace is associated with a specific session and provides granular visibility into the agent’s behavior for a particular interaction. @@ -74 +74 @@ To gather trace data, you need to instrument your agent code using the AWS Distr -A span represents a discrete, measurable unit of work within an agent's execution flow. Spans capture fine-grained operations that occur during request processing, providing detailed visibility into the internal components and steps that make up a complete trace. Each span has a defined start and end time, creating a precise timeline of agent activities and their durations. +A span represents a discrete, measurable unit of work within an agent’s execution flow. Spans capture fine-grained operations that occur during request processing, providing detailed visibility into the internal components and steps that make up a complete trace. Each span has a defined start and end time, creating a precise timeline of agent activities and their durations. @@ -86 +86 @@ Spans include the following essential attributes for agent observability: - * Events marking significant occurrences within the span's lifetime + * Events marking significant occurrences within the span’s lifetime @@ -118 +118 @@ This multi-tiered relationship enables several important observability capabilit - * Correlation of related requests across a user's interaction journey + * Correlation of related requests across a user’s interaction journey @@ -133 +133 @@ While traces provide visibility into complete request-response cycles, spans off -By leveraging session, trace, and span data in your observability strategy, you can gain comprehensive insights into your agent's behavior, performance, and effectiveness at multiple levels of detail. This multi-layered approach to observability supports continuous improvement, robust troubleshooting, and informed optimization of your agent implementations, from high-level conversation patterns down to individual operation performance. +By leveraging session, trace, and span data in your observability strategy, you can gain comprehensive insights into your agent’s behavior, performance, and effectiveness at multiple levels of detail. This multi-layered approach to observability supports continuous improvement, robust troubleshooting, and informed optimization of your agent implementations, from high-level conversation patterns down to individual operation performance.