Observability

Estimated reading: 2 minutes

Observability is the telemetry, diagnostics, and evaluation control plane for AI agents. It provides visibility into agent execution, enabling teams to monitor performance, troubleshoot issues, and analyze cost, latency, and outcomes across agent interactions.

Why Observability Is Required

Unlike traditional applications, AI agents execute tasks through dynamic reasoning, model invocations, and tool interactions. Understanding how a response was generated or why a workflow failed can be difficult without detailed execution visibility.

Observability addresses this challenge by capturing end-to-end execution data for every agent interaction. By tracking each step of the agent workflow, teams can diagnose issues, evaluate performance, and continuously improve agent behavior.

Core Pillars

1. Step-Level Trace Visibility

Observability breaks agent execution into individual trace spans, providing visibility into each stage of a workflow.

a. Execution Details: Inspect prompts, inputs, outputs, tool invocations, and responses.
b. Performance Metrics: Analyze latency, token usage, execution duration, and cost at each step.

2. Session Correlation

Related interactions are grouped into sessions to provide a complete view of ongoing conversations and workflows.

a. Context Tracking: Monitor how context and memory evolve across interactions.
b. Session Analysis: Identify issues that originate in earlier steps and impact subsequent outcomes

3. User Analytics

Observability links execution data to users, enabling teams to understand adoption, usage patterns, and experience quality.

a. Usage Insights: Analyze common workflows, interaction trends, and recurring issues.
b. Experience Metrics: Monitor response times, success rates, and user outcomes.

4. Feedback & Continuous Improvement

Execution data can be used to improve prompts, models, and agent workflows over time.

a. Dataset Creation: Convert historical traces into evaluation datasets.
b. Playground Integration: Replay and test previous interactions using alternative prompts or models.
c. Annotations & Feedback: Add ratings, labels, and comments to support evaluation and optimization.

Observability

Why Observability Is Required

Core Pillars

1. Step-Level Trace Visibility

2. Session Correlation

3. User Analytics

4. Feedback & Continuous Improvement

Observability

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