Latitude is an open-source, MIT-licensed observability and monitoring platform designed specifically for teams building AI agents. By providing deep visibility into multi-turn agent interactions, it allows developers to move beyond traditional logging to understand the complex, non-deterministic behaviors inherent in modern AI systems. The platform is organized around a core loop: Observe, Understand, and Refine.

In the Observe phase, Latitude captures comprehensive telemetry from your agents, grouping individual LLM calls, tool executions, and reasoning steps into unified sessions. This allows developers to inspect full multi-turn conversations, monitor performance metrics like latency, token usage, and costs, and identify structural failures in real-time. The platform supports OpenTelemetry, allowing seamless integration with existing stacks without proprietary lock-in.

The Understand phase leverages semantic search and behavioral clustering to surface production issues that might otherwise remain buried in noise. By analyzing completed sessions, Latitude automatically identifies and groups failure patterns into trackable signals. These signals provide a structured view of common issues, such as tool failures, model refusals, or user frustration, complete with trends and affected user cohorts. Human annotations can be applied to traces to further inform these signals, bridging the gap between raw data and developer judgment.

The Refine phase allows teams to turn these findings into proactive guardrails. Users can create monitors that trigger notifications when specific signals reappear, and generate evaluations based on actual production failures. These evaluations run against live traffic to catch regressions immediately after a fix is deployed, creating a robust feedback loop that ensures agents remain reliable as they scale.

Some of the key features are:

• Agent-Native Telemetry: Grouping of multi-step agent actions, tool calls, and model outputs into coherent, inspectable sessions.
• Semantic Signal Discovery: Automatic clustering of production traces to surface recurring behavioral patterns and failure modes without requiring complex queries.
• OpenTelemetry Compatibility: Standardized ingestion for traces, allowing integration with any existing telemetry setup via OTLP over HTTP.
• Automated Evaluations: Generation of quality checks from real production failures, enabling automated testing and regression monitoring.
• MCP Integration: A Model Context Protocol server that enables AI coding agents like Claude or Cursor to query, manage, and annotate the Latitude workspace directly.
• Collaborative Annotations: Ability for teams to leave inline feedback on traces, turning human review into actionable data for model tuning and signal refinement.

The platform is designed for flexible operation, offering both a managed cloud service and a fully self-hostable version. By maintaining an open-source architecture, Latitude ensures that engineering teams retain full control over their production data and can contribute to or customize the platform to fit their specific infrastructure needs.

Some common use cases include:

• Debugging Complex Agent Chains: Identifying exactly where a reasoning step or tool call failed within a multi-turn agent conversation.
• Production Monitoring for AI: Setting up proactive alerts for regressions, such as unexpected model refusals or degradation in response quality after a deployment.
• Continuous Improvement: Curating golden datasets from validated production traces to refine prompts or fine-tune models based on actual user feedback.
• Collaborative Triage: Using the shared dashboard to analyze, annotate, and cluster production issues across distributed engineering teams.