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The next breakthrough in AI isn't better models, it's agents that improve themselves.
About the Webinar
Most AI teams are stuck in an endless loop: build an agent, manually test it, tweak prompts for weeks, ship it, watch it fail in production, repeat.
Meanwhile, a new generation of AI systems is emerging, agents that don't wait for human intervention. They evaluate themselves, learn from every interaction, and systematically optimize their own performance using eval-driven feedback loops.
In this live workshop, we reveal how eval-driven auto-optimization is replacing months of manual tuning with automated improvement cycles that run continuously. You'll see how production-grade AI agents use evaluation frameworks and optimization algorithms to turn every conversation, every failure, and every success into systematic performance gains, without human bottlenecks.
This isn't about prompt engineering tips. It's about building agents that measure, learn, and optimize themselves at scale.
👉 Who Should Watch
AI Engineers, ML Developers, Product Teams, and Technical Founders building production-grade agents who want to replace manual tuning with automated, eval-driven optimization at scale.
🎯 Why you should watch
Replace weeks of manual tuning with automated optimization loops that run 24/7
See 6+ optimization strategies in action: Bayesian Search, Meta-Prompt, ProTeGi, GEPA, and more
Learn the optimization mindset that separates production-ready agents from demos
Master Agent Compass: Build evaluation feedback loops that drive measurable improvements
Cut costs and iterations while improving agent performance across every metric
Get hands-on with the Future AGI SDK (Open Source) and run your first auto-optimization job
💡 Key Insight
First-generation agents execute tasks. Next-generation agents optimize themselves using evaluation frameworks and optimization algorithms, turning every interaction into systematic performance gains without human intervention.
👉 To know more about Agent Optimize, click here.
🌐 Visit Future AGI
