[Free eBook] How We Built a Practical Framework for Evaluating AI Agents in Production

Hey everyone! I'm new to the dev.to community and wanted to share something our team built.

We've all been there: your new AI agent works great in demos, but the moment you think about shipping to prod, you get nervous. How do you actually know it's reliable?

Traditional LLM evals are useless for agents. An agent can give a perfect-sounding summary while, internally, its tool calls failed, it pulled stale data, and it completely forgot the user's original goal. Debugging this is a nightmare.

Our team has been tackling this exact problem while building multimodal agents. We ended up creating our own evaluation playbook and decided to share it as a free e-book.

It's not just theory, it's a practical guide on how to build an eval system. It covers:

• Diagnosing the real failure modes in planning, memory, and tool_calls.
• The 'how-to' of instrumenting your agent to get span and trace-level data (this is the most important part for debugging).
• Moving beyond "accuracy" to evals that check for safety and real business metrics.
• How we think about continuous monitoring and drift detection.

You can grab the free e-book here: https://shorturl.at/4a1He

Hope it's helpful. Happy to answer any questions in the comments!