Open-Source Observability and Prompt Engineering for AI Agents
Latitude is an open-source platform designed to address the unique challenges of monitoring and improving AI agents in production. Unlike traditional observability tools, Latitude integrates observability, prompt engineering, and collaborative debugging into a single workflow. It captures traces of agent interactions, identifies failures, and automates fixes through integration with coding agents like Claude Code or Cursor. The platform is built on an MIT license, ensuring transparency, self-hosting capabilities, and community-driven development.
The core philosophy behind Latitude is to make AI agents self-healing at scale. By automating the detection, analysis, and resolution of failures, it reduces the manual effort required to maintain high-performance AI systems. This approach is particularly valuable for teams deploying complex AI agents where failures are often unpredictable and multi-faceted.
Latitude’s design is rooted in the principle of signal-centric workflows. Instead of treating each failure as an isolated event, the platform groups recurring issues into trackable signals. These signals include metadata such as status, size, trends, and affected user counts, providing a structured way to prioritize and address problems. This methodology ensures that teams focus on patterns rather than individual logs, aligning monitoring with human judgment and automated evaluations.
How It Works
Each step is designed to streamline the identification and resolution of AI agent failures while maintaining full visibility into agent behavior.
Observe: Capturing Agent Telemetry
The first step involves capturing detailed telemetry from AI agent interactions. Latitude instruments multi-turn sessions, tool calls, and full execution paths, converting each interaction into a trace. The platform supports a wide range of LLM providers and frameworks, including OpenAI, Anthropic, Bedrock, LangChain, and custom OpenTelemetry pipelines.
Traces are enriched with metadata such as user IDs, session IDs, tags, and custom annotations. This metadata allows teams to filter and analyze interactions based on specific criteria, such as user cohorts or tool reliability. For example, a trace might include details about a multi-step conversation involving tool calls, retrieval steps, and LLM responses, providing a holistic view of agent behavior.
Understand: Identifying and Grouping Failures
Once traces are captured, Latitude analyzes them to identify recurring failure patterns. The platform uses semantic search, behavioral clustering, and custom flaggers to group similar failures into signals. These signals are prioritized based on their impact, frequency, and trends, allowing teams to focus on the most critical issues.
Semantic search enables teams to find traces by meaning rather than exact text, making it easier to discover patterns such as frustration, refusal, or tool errors. Behavioral clustering further refines this process by grouping sessions into topics or user behaviors, providing insights into what users are actually experiencing. For instance, Latitude might identify a pattern of users abandoning conversations after encountering a specific tool failure, even if the exact wording varies across sessions.
Human annotations play a key role in refining signals. Teams can label traces with feedback, which is then used to create or adjust automated flaggers. This ensures that monitoring remains aligned with human judgment, reducing false positives and improving the accuracy of failure detection.
Fix: Automating Repairs with Coding Agents
When a signal is detected, Latitude dispatches a coding agent—such as Claude Code or Cursor—to investigate and propose fixes. The agent receives full context, including sample traces, error details, and a deep link to the failing code. Using this information, the agent generates the smallest correct fix and opens a pull request (PR) directly in the repository.
This process is facilitated by the Latitude MCP (Multi-Tool Coordination Protocol) server, which allows coding agents to interact with the platform programmatically.
Verify: Ensuring Fixes Are Effective
This creates regression datasets that can be used to monitor for recurring issues, ensuring that fixes do not reintroduce problems.
Golden datasets are automatically generated from validated production traces, offering a reliable way to test new changes before deployment. These datasets can also be used for local development, allowing teams to simulate production environments and catch issues early in the development cycle.
Use Cases
Its signal-centric approach makes it particularly useful for teams managing complex, production-grade AI systems.
Production Monitoring and Debugging
The platform provides visibility into agent behavior, including multi-turn conversations, tool call reliability, and cost efficiency. For example, teams can track how often an agent fails to complete a task, how long it takes to resolve issues, and which tools are most prone to errors.
Latitude’s observability features extend beyond basic logging. The platform offers dedicated views for users, tools, memory, and cost, allowing teams to drill down into specific areas of concern. For instance, the Tools view might reveal that a particular API integration is failing at a higher rate than others, while the Cost view could highlight unexpected spikes in LLM usage.
Failure Pattern Detection and Prioritization
AI agents often fail in ways that are difficult to predict during development. Latitude helps teams identify and prioritize these failure patterns by grouping recurring issues into signals. Each signal includes metadata such as the number of affected users, the trend over time, and example traces, providing a clear picture of the problem’s scope and impact.
Latitude would group these instances into a signal labeled ‘Retrieval Failure,’ complete with metrics on how often it occurs and which user segments are most affected.
Collaborative Debugging and Prompt Engineering
Annotations can be used to document why a particular failure occurred or to suggest improvements to prompts or tool configurations. These annotations become part of the signal, ensuring that human judgment is incorporated into the automated monitoring process.
Latitude would capture the results of these experiments, allowing teams to compare performance metrics and roll back to previous versions if necessary.
Integration with Coding Agents for Automated Fixes
One of Latitude’s most innovative features is its integration with coding agents like Claude Code and Cursor. When a signal is detected, the platform dispatches a coding agent with the full context of the failure, including traces, error logs, and relevant code snippets. The agent then generates a fix and opens a PR, streamlining the repair process.
For example, if an agent consistently fails to handle a specific type of user input, Latitude would dispatch a coding agent to analyze the traces, identify the root cause, and propose a fix—all without human intervention.
Compliance and Security
The platform is SOC 2 certified and adheres to industry-leading standards for data protection. All data is encrypted in transit with TLS 1.2+ and at rest with AES-256, ensuring confidentiality and integrity. Additionally, Latitude supports single sign-on (SSO) with SAML 2.0, allowing teams to enforce strict access controls.
The platform can be deployed on-premises or in private cloud environments, with full control over data storage and processing. This flexibility ensures compliance with global privacy laws such as GDPR and CCPA, while also accommodating teams with specific security or regulatory needs.
Cost Optimization and Efficiency
AI agent operations can be costly, particularly when it comes to LLM usage.
Additionally, Latitude’s memory view tracks how persistent memory evolves over time, helping teams optimize storage and retrieval strategies. By identifying inefficiencies in memory usage, teams can reduce costs and improve agent responsiveness.
Key Features and Capabilities
Latitude’s feature set is centered around observability, signal management, and automation. The platform provides tools for capturing telemetry, analyzing failures, and automating repairs, all while maintaining flexibility and openness.
Telemetry and Tracing
Latitude supports comprehensive telemetry collection, including traces for LLM calls, tool calls, and multi-turn conversations.
For example, a team using OpenAI’s API can instrument their agent with a few lines of TypeScript or Python code, capturing every interaction as a trace. These traces are then sent to Latitude, where they can be analyzed, searched, and grouped into signals. The platform also supports custom metadata, enabling teams to tag traces with additional context such as user roles or session types.
Signal Discovery and Management
At the heart of Latitude is its signal-centric workflow. Signals are automatically generated from recurring failure patterns, with each signal including metadata such as status, trend, and affected user count. Teams can prioritize signals based on their impact and use them to track the lifecycle of issues from detection to resolution.
Latitude’s semantic search and behavioral clustering further enhance signal discovery. Teams can search for traces by meaning rather than exact text, making it easier to find patterns such as frustration or tool errors. Behavioral clustering groups sessions by topic or user behavior, providing insights into what users are actually experiencing.
Automated Fixes with Coding Agents
When a signal is detected, Latitude dispatches a coding agent to investigate and propose fixes. The agent receives full context, including sample traces, error details, and a deep link to the failing code. Using this information, the agent generates the smallest correct fix and opens a PR in the repository.
This process is facilitated by the MCP server, which allows coding agents to interact with Latitude programmatically.
Regression Testing and Golden Datasets
Latitude generates golden datasets from validated production traces, providing a reliable way to test new changes before deployment. These datasets can be used to replay failing traces against updated agents, ensuring that fixes are effective and do not reintroduce problems.
Monitors and alerts are set up to notify teams if a previously resolved issue resurfaces. For example, if a fix for a tool failure is deployed, Latitude would continue to monitor for similar issues and alert the team if they reappear.
Self-Hosting and Open Source
Latitude is fully open source under the MIT license, allowing teams to self-host the platform or contribute to its development. The platform provides ready-to-go container images for Docker and Kubernetes, enabling teams to deploy Latitude at any scale, from a single host to a full cluster.
Self-hosting ensures that teams have full control over their data and infrastructure. It also allows for customization and adaptation to specific needs, such as integrating with proprietary tools or enforcing additional security measures. The open-source nature of Latitude fosters collaboration and innovation, with contributions welcome from the community.
Security and Compliance
The platform is SOC 2 certified and adheres to industry-leading standards for data protection. All data is encrypted in transit with TLS 1.2+ and at rest with AES-256, ensuring confidentiality and integrity.
Latitude supports single sign-on (SSO) with SAML 2.0, allowing teams to enforce strict access controls. The platform also provides detailed activity and access logs, offering full visibility into who is accessing the system and what actions they are performing.
For teams with specific compliance requirements, Latitude offers hosted data storage in Europe, ensuring that data remains within regulatory boundaries. The platform also includes tooling and agreements to help teams meet global privacy laws such as GDPR and CCPA.
CLI and API Accessibility
The platform’s TypeScript SDK and MCP server further enhance accessibility, enabling teams to integrate Latitude into their existing workflows. The SDK provides methods for capturing traces, managing signals, and interacting with coding agents, while the MCP server enables programmatic access to the platform’s features.
Integration with Third-Party Tools
Latitude is provider-agnostic and supports a wide range of LLM providers and frameworks. It works out of the box with OpenAI, Anthropic, Bedrock, Vercel AI SDK, LangChain, and any OpenTelemetry-compatible application. This flexibility ensures that teams can use Latitude regardless of their existing infrastructure.
Additionally, Latitude integrates with coding agents such as Claude Code, Cursor, and Codex, enabling automated debugging and fix generation. The platform also supports integrations with Slack, email, and webhooks for notifications, allowing teams to receive alerts when new issues are detected or existing ones escalate.
Development and Community Support
Latitude is built with the community in mind. The platform provides comprehensive documentation, a Slack community for collaboration, and a contributing guide for developers. Teams can start tracing their agents in minutes, with setup guides available for both manual instrumentation and coding agent-assisted deployment.
The platform’s changelog and release notes keep users informed about new features and improvements. For example, recent updates include named profiles for API key management, enhanced CLI flags for debugging, and support for backfilling existing traces from other platforms.
Contributions are welcome, with good first issues available for new developers. The open-source nature of Latitude encourages collaboration and innovation, allowing teams to adapt the platform to their specific requirements.
Technical Implementation and Workflow
This ensures that teams can integrate Latitude into their existing workflows without proprietary lock-in.
OpenTelemetry Integration
The platform supports both manual instrumentation and automatic capture of LLM calls. For manual instrumentation, teams can use the Latitude SDK to wrap LLM calls and capture additional metadata such as user IDs or session tags. For automatic capture, Latitude can be configured to intercept LLM API calls and generate traces without requiring code changes.
Signal Processing and Machine Learning
Latitude’s signal processing pipeline combines rule-based flaggers with machine learning-based behavioral clustering. Rule-based flaggers detect common failure patterns such as frustration, refusal, or tool errors, while behavioral clustering groups sessions by semantic similarity. This hybrid approach ensures that signals are both accurate and actionable.
For example, a rule-based flagger might detect when an agent returns an empty response, while behavioral clustering might group sessions where users express frustration despite receiving a response. Together, these techniques provide a comprehensive view of agent failures, enabling teams to address issues at their root cause.
Automated Fix Generation
When a signal is detected, Latitude dispatches a coding agent with the full context of the failure. The agent uses this context to generate a fix, which is then reviewed and applied by the development team. This process is facilitated by the MCP server, which provides a standardized interface for coding agents to interact with Latitude.
For example, a team might configure Latitude to automatically open a PR when a fix is generated, or they might review the proposed fix manually before applying it. This flexibility ensures that automation complements rather than replaces human judgment.
Self-Hosting and Scalability
Latitude is designed to be self-hostable at any scale. The platform provides ready-to-go container images for Docker and Kubernetes, enabling teams to deploy Latitude on a single host or a full cluster.
For teams that prefer managed services, Latitude Cloud offers a hosted version of the platform. Both self-hosted and cloud deployments run the same open-source codebase, ensuring consistency and avoiding lock-in.
Security and Data Protection
Latitude’s security model is built on industry-leading standards. All data is encrypted in transit with TLS 1.2+ and at rest with AES-256, ensuring confidentiality and integrity. The platform also supports SSO with SAML 2.0, allowing teams to enforce strict access controls.
For teams with specific compliance requirements, Latitude offers hosted data storage in Europe. This ensures that data remains within regulatory boundaries and complies with laws such as GDPR and CCPA. Additionally, the platform provides detailed activity and access logs, offering full visibility into system usage.
CLI and API Design
For example, a team might create a production profile and a sandbox profile, switching between them using CLI flags or environment variables.
The API provides programmatic access to Latitude’s features, enabling teams to integrate the platform into their existing workflows. For example, a team might use the API to automate the creation of signals, dispatch coding agents for fixes, or generate regression datasets for testing.
Integration with Coding Agents
Latitude’s integration with coding agents such as Claude Code and Cursor is a key differentiator. When a signal is detected, the platform dispatches a coding agent with the full context of the failure, including traces, error logs, and relevant code snippets. The agent then generates a fix and opens a PR, streamlining the repair process.
This integration is facilitated by the MCP server, which provides a standardized interface for coding agents to interact with Latitude. The server supports both synchronous and asynchronous workflows, allowing teams to customize how fixes are generated and applied.