platoseedplatoseed
Reliability platform for AI agents
Lemma catches the silent, semantic failures your observability tools miss, where your AI agent looks like it worked but didn’t. We scan every trace to surface issues before users complain, identify root causes, and help you fix them without manual digging, so your agents improve over time.
Lemma is a reliability platform for AI agents that helps catch regressions, spot failures, and improve agents before users notice issues. It emphasizes automatic root cause analysis, tracing, and feedback-driven improvements tied to production runs.
Lemma diagnoses regressions automatically, traces incidents to exact spans, tool calls, and model outputs, and proposes fixes with one-click pull requests. It includes semantic search over traces, cluster discovery to surface emerging failure patterns, and integrations with existing stacks (SDKs and instrumentation). It tracks production runs with runId to attach feedback and experiments, and provides security and data isolation features.
Who it’s for: AI teams building and operating AI agents who need reliability, debugging, and continuous improvement in production environments.
Mentions product as an applied product and research lab; references to production usage, demos, and 'Book a Demo' suggests early traction and active customer exploration.
developing the next generation of intelligent systems @ lemma (f25) | ex-USC
We enable AI agents to continuously improve by turning real user feedback into automated prompt optimizations.
Lemma provides an end-to-end evaluation and observability platform that automatically learns from live user feedback and production data to continuously improve AI agents. It detects failed outcomes, triggers targeted prompt optimizations, and returns updated prompts with optional PRs to your codebase, aiming to reduce manual iteration and drift-induced performance loss.
Datadog for Agent Reliability
Monitoring and learning layer for long-running agents
