Module 13 Wrap-up: The Observability Engineer

You have learned that building an AI is only 50% of the job. Observing it is the other 50%. By using set_debug and LangSmith, you have graduated from a hobbyist to a professional who can guarantee that their system is running efficiently, safely, and cost-effectively.

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Hands-on Exercise: The Trace Hunt

1. The Goal

1. Connect your local Python script to LangSmith.
2. Run an agent that uses at least 3 tools.
3. Go to the LangSmith dashboard and find the specific "Tool Call" for search.
4. Identify which step took the most amount of time (Latency).

2. The Implementation Plan

1. Set the LANGCHAIN_TRACING_V2=true env var.
2. Execute the script.
3. Look for the "Trace" with your project name.
4. Share the URL with a (hypothetical) colleague.

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Module 13 Summary

• Debugging: Using set_debug for local, raw data inspection.
• Observability: Professional cloud-based tracing (LangSmith).
• Traces: The visual record of every node execution.
• Playground: Fixing failing prompts without code changes.
• Performance: Identifying and eliminating latency bottlenecks.

Coming Up Next...

In Module 14, we prepare for the "Real World." We will learn about Production Patterns, including building APIs with FastAPI, implementing Caching, and handling Retries to make our system robust for millions of users.

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Module 13 Checklist

• I understand how trace data propagates through a chain.
• I have enabled set_debug(True) to see local logs.
• I have generated a LangSmith API key.
• I can find the total "Cost" of a trace in the cloud dashboard.
• I can explain why observability is mandatory for production AI.