AI Trace Debugger for Agent Development

Developers struggle to debug why AI agents underperform without visibility into token usage and context bloat. This tool provides a profiler/debugger for Claude Code sessions showing where performance issues originate. Target users are AI engineers building and optimizing agent systems.

Agent observability is a real and growing pain point — the LLM agent space has exploded in 2024-2025 and debugging why a multi-step agent fails or burns tokens is genuinely opaque with current tooling. LangSmith (by LangChain) is the closest incumbent but focuses on tracing LangChain-specific pipelines, leaving Claude Code and framework-agnostic agent work underserved. The $2k–10k/mo revenue band is credible for a dev tool with a clear ROI story — engineers burning compute budget on inefficient context windows will pay for something that surfaces the fix. The biggest risk is platform dependency: Anthropic could ship native profiling inside Claude Code directly, making a third-party layer redundant overnight.