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Runs simulations across thousands of agent configurations to tune prompts, tools, and policies against a custom reward metric.
Lucidic AI helps teams improve an AI agent's performance after it's already in production, by varying its prompts, tool access, memory, and policies across many simulated configurations and scoring each against a reward metric the customer defines — resolution rate, accuracy, or CSAT. It integrates with agent frameworks like LangChain and LangGraph and with model providers including OpenAI, Anthropic, and Gemini, and adds production monitoring with controlled rollouts to catch regressions early. The buyer is enterprise teams running agents for support, legal, data analysis, or coding, where reliability matters more than novelty. The company is based in San Francisco, went through Y Combinator's Winter 2025 batch, and has raised early funding. Pricing isn't published — the site is built around booking a demo rather than self-serve signup.
No pricing is published — the site is built around booking a demo rather than self-serve signup, typical of an enterprise sales motion.
Use tool ↗Lucidic AI treats agent improvement as an optimization problem — simulate many configurations, score against a real business metric, deploy the winner — rather than just logging traces for a human to read. That's a meaningfully different approach from a pure observability tool, but it's enterprise-priced and demo-gated, so there's no way to try it without a sales conversation.
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