Foglamp: Observability Platform for AI Agents

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Foglamp is an observability tool designed for AI agents that tracks the cost, latency, and output quality of LLM calls. Setup is automated using a single prompt handed to coding agents such as Claude Code or Codex.

Key points

  1. Tracks LLM calls regarding costs, latency, token usage, and evaluation scores.
  2. Helps detect output errors and quality drift before end users encounter them.
  3. Setup via a pre-built prompt that coding agents like Claude Code or Codex integrate directly into existing code.
  4. Dashboard provides visibility into agent execution pipelines, including planning, document retrieval, and generation steps.

Why it matters

Without ongoing observability, AI agents can silently degrade in quality and incur unexpected costs. Streamlining the integration process through coding agents reduces the friction of deploying monitoring tools into production codebases.

Analysis

Foglamp targets the LLMOps market with an emphasis on friction-free onboarding. By leveraging AI coding assistants to locate LLM calls and generate code diffs, the platform lowers setup overhead. Its dashboard focuses on actionable metrics—such as latency, step-by-step agent traces, and eval pass rates—to tackle common agent pitfalls like hallucinations and outdated context.

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Summary generated by AI (Gemini 3.8 Flash, 27 September 2026). It may contain errors – the original source is authoritative.

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