The latest articles on DEV Community by Karthik Avinash (@karthikavinash). https://dev.to/karthikavinash https://media2.dev.to/dynamic/image/width=90,height=90,fit=cover,gravity=auto,format=auto/https:%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Fuser%2Fprofile_image%2F2242024%2F1265ec00-1375-4e14-b958-fcd753314a8b.png https://dev.to/karthikavinash en Karthik Avinash Thu, 13 Nov 2025 05:33:24 +0000 https://dev.to/karthikavinash/-47pe https://dev.to/karthikavinash/-47pe <div class="ltag__link"> <a href="/karthikavinash" class="ltag__link__link"> <div class="ltag__link__pic"> <img src="https://media2.dev.to/dynamic/image/width=800%2Cheight=%2Cfit=scale-down%2Cgravity=auto%2Cformat=auto/https%3A%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Fuser%2Fprofile_image%2F2242024%2F1265ec00-1375-4e14-b958-fcd753314a8b.png" alt="karthikavinash"> </div> </a> <a href="https://dev.to/karthikavinash/free-ebook-how-we-built-a-practical-framework-for-evaluating-ai-agents-in-production-22pf" class="ltag__link__link"> <div class="ltag__link__content"> <h2>[Free eBook] How We Built a Practical Framework for Evaluating AI Agents in Production</h2> <h3>Karthik Avinash ・ Nov 12</h3> <div class="ltag__link__taglist"> <span class="ltag__link__tag">#ai</span> <span class="ltag__link__tag">#llm</span> <span class="ltag__link__tag">#evaluation</span> <span class="ltag__link__tag">#agents</span> </div> </div> </a> </div> ai llm evaluation agents Karthik Avinash Wed, 12 Nov 2025 05:31:36 +0000 https://dev.to/karthikavinash/free-ebook-how-we-built-a-practical-framework-for-evaluating-ai-agents-in-production-22pf https://dev.to/karthikavinash/free-ebook-how-we-built-a-practical-framework-for-evaluating-ai-agents-in-production-22pf <p>Hey everyone! I'm new to the dev.to community and wanted to share something our team built.</p> <p>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 <em>actually</em> know it's reliable?</p> <p>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.</p> <p>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.</p> <p>It's not just theory, it's a practical guide on <em>how</em> to build an eval system. It covers:</p> <ul> <li>Diagnosing the real failure modes in <code>planning</code>, <code>memory</code>, and <code>tool_calls</code>.</li> <li>The 'how-to' of instrumenting your agent to get <code>span</code> and <code>trace-level</code> data (this is the most important part for debugging).</li> <li>Moving beyond "accuracy" to evals that check for safety and real business metrics.</li> <li>How we think about continuous monitoring and drift detection.</li> </ul> <p>You can grab the free e-book here: <a href="https://shorturl.at/4a1He" rel="noopener noreferrer">https://shorturl.at/4a1He</a></p> <p>Hope it's helpful. Happy to answer any questions in the comments!</p> ai llm evaluation agents