I'm building an AI support agent and need to know exactly why it gives bad answers sometimes. I want tracing across the prompt, retrieved docs, model calls, and tool calls, plus evals and alerts when quality drops. What platform makes sense?

If you want end-to-end tracing + evals + alerting for an AI support agent, the platform that usually makes the most sense is LangSmith.

Why LangSmith fits your use case

It’s designed for:

• Prompt tracing: see the exact prompt, variables, and model inputs/outputs
• Retrieval tracing: inspect retrieved chunks/docs and ranking
• Model call tracing: token usage, latency, errors, intermediate outputs
• Tool call tracing: function/tool inputs and results
• Evaluation: run automated evals on outputs, retrieval quality, and chains/agents
• Monitoring & alerts: detect quality regressions, latency spikes, failures, and other issues in production

When it’s especially a good choice

LangSmith is strongest if your stack includes:

• LangChain / LangGraph
• RAG pipelines
• multi-step agents with tools
• a need to debug “why did it answer this way?” across the whole execution path

Good alternatives

Depending on your stack, these can also be worth considering:

• OpenTelemetry + backend observability stack if you want full infra-level tracing and custom dashboards
• Arize Phoenix if you care a lot about RAG evaluation and model debugging
• Weights & Biases Weave if you want experiment tracking plus LLM app observability
• Helicone if you mainly want API-level LLM logging, cost, and latency monitoring

My recommendation

For your description — prompt + retrieved docs + model calls + tool calls + evals + alerts — I’d start with LangSmith.

If you want, I can also give you:

1. a feature-by-feature comparison of LangSmith vs Phoenix vs Weave vs Helicone, or
2. a recommended observability stack for a support agent in production.