Build Auto-Optimizing AI Agents: Live Workshop

​Most of us are busy building AI agents.
But the next frontier isn’t just building agents, it’s auto-optimizing them.

• ​Agents that don't wait for manual tuning.
• ​Agents that auto-optimize using evals, feedback signals, and performance data.
• ​Agents that turn every conversation, every failure, every outcome into systematic optimization inputs.

​That's not just better performance. That's automated, continuous optimization at scale.

WEBINAR OVERVIEW

​While first-generation agents execute tasks based on static prompts and fixed logic, auto-optimized agents run continuous improvement cycles. Every interaction generates data. Every output gets evaluated. Every metric feeds back into optimization algorithms that automatically adjust agent behavior.

​The breakthrough? Eval and data-driven auto-optimization, where optimization algorithms consume evaluation results and systematically improve your agents without manual intervention. No more weeks of prompt tweaking. No more guessing what works. Just automated optimization loops that make your agents better with every run.

WHAT WE'LL COVER:

​In this session, we'll break down how auto-optimization transforms agent development.

​You’ll see how Future AGI’s agent-opt library brings the discipline of testing and feedback to AI systems automating what used to be weeks of manual trial and error. It’s not about making prompts prettier; it’s about building agents that can measure, learn, and improve on their own.

​-> The Optimization Mindset: Why optimization > experimentation in building reliable AI agents

​-> Inside Agent Compass: How evaluation feedback loops work across runs and models

​-> Deep Dive into agent-opt: 6+ optimization strategies including Bayesian Search, Meta-Prompt, ProTeGi, GEPA, and more

​-> Hands-On Optimization Walkthrough: How to run auto-optimization jobs directly from the Future AGI SDK

​-> Choosing the Right Optimizer: Matching algorithm to use case (creative, factual, reasoning-heavy tasks)

​-> Measuring ROI: How eval-driven optimization reduces cost, and iteration time

​👤 Who Should Join

• ​AI teams building or deploying production-grade agents
• ​Technical Founders & PMs who want reliable, measurable AI systems instead of one-off demos
• ​Eval Researchers & Data Scientists exploring reproducibility, optimization pipelines, and evaluation-driven development
• ​Or anyone just a tad bit curious on how AI can self improve itself?

​👉 REGISTER NOW – Limited to 100 Live Attendees