AI agents are getting smarter fast.
They can reason through tasks, manage workflows, call tools, and automate decisions across applications. But as these systems become more capable, one challenge becomes impossible to ignore: reliability.
How do you know your AI agent is making the right decisions consistently?
How do you test workflows that involve memory, reasoning, and multiple execution steps?
And how do you debug failures when outputs become unpredictable?
That’s exactly what this free live webinar, “Testing AI Agents in Python: Building Reliable Evals with LangGraph & LangSmith,” is focused on.
The session includes a
“Live demo of the AI agent evaluation pipeline,”
where you’ll see how developers are building structured evaluation workflows using LangGraph and LangSmith to test, trace, and improve AI agent performance in real-world scenarios.
Here is the link to register..
Who Should Join This Session?
This webinar is designed for developers and technical teams working with AI systems, especially:
Python developers building AI agents or LLM workflows
AI engineers exploring evaluation and observability
Architects designing production-ready AI systems
Product teams experimenting with AI automation
Founders building intelligent applications faster
Whether you’re actively deploying AI agents or still evaluating the ecosystem, this session will give you a clearer understanding of how reliable AI systems are actually built.
What You’ll Learn During the Webinar
This isn’t a high-level AI trends session.
The focus is practical implementation, testing workflows, and evaluation strategies developers can actually use.
In this webinar, you’ll learn:
Why evaluation matters for modern AI agents
How LangGraph helps manage complex agent workflows
How LangSmith can trace and monitor agent execution
Ways to create repeatable and scalable evaluation pipelines
Practical approaches for debugging and improving AI agent behavior
See the AI Evaluation Pipeline Live
One of the biggest highlights of the webinar is the live demo.
Instead of static slides or simplified examples, you’ll see an actual AI agent evaluation pipeline in action.
This includes:
Workflow orchestration
Agent tracing and observability
Evaluation logic
Testing outputs across scenarios
Identifying reliability issues in real time
For developers building AI products, seeing the full workflow live can help connect the gap between theory and implementation.
Why This Webinar Matters Right Now
AI development is moving beyond prototypes.
Teams are now expected to ship AI systems that are reliable, observable, and production-ready. And that means evaluation is becoming just as important as model selection or prompt engineering.
This webinar gives you a practical introduction to that shift and helps you understand how modern AI engineering teams are approaching testing and reliability today.
And since the session is completely free and live, it’s a great opportunity to learn directly from real implementation workflows.
If you’re building AI agents in Python in 2026, this is a webinar worth adding to your calendar.