Overview

Agentic Observability MCP server exposes APIs for monitoring AI agent activities in real-time. It collects execution traces, logs, and performance metrics from agentic workflows, enabling programmatic inspection without manual intervention.

Key Capabilities

Agent tracing: Records step-by-step decision sequences and tool calls during agent runs.
Log retrieval: Fetches detailed event logs including inputs, outputs, and errors.
Metrics querying: Provides data on latency, success rates, token usage, and error frequencies.

These functions integrate with MCP to surface observability data for agent debugging.

Use Cases

Debugging agent failures: Query traces to identify where an agent deviated from expected paths in a task automation pipeline.
Performance optimization: Analyze metrics from repeated agent runs to reduce latency in customer support chat agents.
Error tracking in multi-agent systems: Retrieve logs from coordinated agents to pinpoint failures in collaborative planning scenarios.
Cost monitoring: Track token and compute metrics to manage expenses in production AI agent deployments.

Who This Is For

AI developers building agentic applications, MLOps engineers managing agent fleets, and teams requiring runtime visibility into LLM-based autonomous systems.