LLM-powered agents are reshaping software, but when they fail, troubleshooting is guesswork. Lumigo’s new AI Agent Observability, now in beta, gives you visibility into the entire lifecycle of your agents, from prompt to response to internal decision logic.
Built for modern AI workloads, this feature is designed to help engineers monitor, debug, and optimize agents running on platforms like OpenAI, Anthropic, and open-source models.
What’s Included:
Lumigo’s AI Agent Observability includes a host of tools designed to help developers and DevOps fully observe and troubleshoot AI agents, including:
Full visibility into agent interactions
See every system prompt, user input, and LLM response in context. No more wondering why an agent took an unexpected action.
Real-time cost tracking
Understand the cost of every LLM call—including token usage—at the granularity of each request. Break down spend by model, endpoint, or team.
Advanced filtering and analysis
Slice data by model, latency, cost, and custom tags. Identify performance patterns, regressions, or efficiency opportunities across deployments.
Decision graph tracing
Explore your agent’s decision path, including internal tools, API calls, and conditional logic. Easily pinpoint failure points or unexpected behavior.
Designed for Developers Shipping AI into Production
Traditional observability tools weren’t built for LLMs. Lumigo connects traces, logs, metrics, and now prompts, responses, and decision logic, into a single platform built for complex microservices and AI.
Whether you’re building copilots, autonomous workflows, or multi-agent systems, this new Lumigo feature is your window into how your agents operate and where they break.
Join the Beta
Interested in early access? Request access or schedule a demo to see how Lumigo’s AI Agent Observability can help you go from black box to total clarity.