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Flowlines

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Flowlines is behavioral observability for production AI agents and MCP-powered products. It helps product and engineering teams understand what their agents are actually doing in production, especially when individual tool calls and traces look successful but the user outcome is not. Traditional LLM observability tools are good at showing logs, latency, token usage, errors, and individual traces. Flowlines adds the behavioral layer. It reconstructs the full session from the user’s original intent through model responses and tool calls to the final outcome, then analyzes those sessions at scale to uncover patterns that are difficult to see by debugging traces one at a time. Flowlines surfaces silent failures, repeated retries, tool misuse, context loss, unmet user intent, user frustration, and recurring behavioral issues across sessions. Teams can identify what users are trying to accomplish, which workflows agents struggle with, what failure modes happen most often, and where the product can be improved. Flowlines can be added directly to an AI application or connected to an existing observability stack, allowing teams to keep their current infrastructure while adding deeper analysis of agent behavior. Issues can be investigated down to the individual session and turn, while higher-level analysis highlights trends across users and production traffic. Flowlines also exposes its analysis through MCP, so developers and coding agents can query sessions, issue clusters, and behavioral insights directly from tools such as Claude Code or Codex. Flowlines is built for teams shipping AI agents, copilots, assistants, and MCP servers that need to move beyond “did the API call succeed?” toward understanding whether the agent actually accomplished what the user wanted.

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