Building Better AI Agents: Artificial Intelligence Observability How To

AI agents don't fail like traditional software. When an agent takes hundreds of steps, repeatedly calls tools, updates state, and still produces the wrong result, there is no stack trace to inspect. Nothing crashed. What failed was the agent’s reasoning. 

Observability and evaluation for agents are fundamentally different from what most software teams are used to, and traces have become the primary source of truth when building agentic systems. 

In this video, LangChain CEO, Harrison Chase, and Product Lead, Vivek Trivedy, walk through: 

- Why agents require a fundamentally different approach than traditional software—you don't know what your agent does until you run it 
- How observability powers evaluation: turning traces into datasets and metrics for continuous improvement 
- Setting up tracing with LangSmith and the three most common methods 
- Prompt optimization techniques and packaging improvements into skills 

🔗 Resources mentioned: 
LangSmith: https://shorturl.at/BBgE9 
Docs: https://docs.smith.langchain.com/