AI Agents in Production Are Flying Blind — AgentLens Fixes That

The Visibility Problem Running an AI agent in production means dealing with a problem most developers hit quickly. The agent makes 15–20 LLM calls per session — chained, conditional, sometimes parallel. Something goes wrong. The output is bad, the cost spiked, or the agent looped. And there's no answer to any of these questions: Which specific call failed? What did the model actually receive? What did it return? How much did this session cost? Where in the run did it break? Why Existing Tools Do