Build Self-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, latency, 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
About the Speakers
Nikhil Pareek, Founder & CEO of Future AGI, is a serial entrepreneur with over nine years of experience building startups and leading AI-driven innovation across industries like healthcare, IoT, consulting, and finance. He’s passionate about bringing engineering rigor to AI systems and solving core infrastructure challenges in model development and deployment. At Future AGI, he’s focused on helping teams build trustworthy, production-ready AI through automation, evaluation, and observability.
Rishav Hada, an Applied Scientist at Future AGI, specializes in AI evaluation and observability. Previously at Microsoft Research, he developed frameworks for generative AI evaluation and multilingual language technologies. His research, funded by Twitter and Meta, has been published in top conferences like EMNLP, ACL, and NAACL and integrated into AI products. His recent work on mitigating bias in language technologies won the Best Paper Award at FAccT’24.
About Future AGI
Future AGI is a San Francisco-based advanced AI ENgineering & optimization platform designed to streamline experimentation, evaluation, optimization and real-time observability. Traditional AI tools often rely on guesswork due to gaps in data generation, error analysis, and feedback loops. Future AGI eliminates this uncertainty by automating the data layer with multi-modal evaluations, agent optimisations, observability, and synthetic data tools, cutting AI development time by up to 95%. By removing manual overhead, it brings software engineering rigour to AI, enabling teams to build high-performing, trustworthy systems faster.
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