Human Orchestration of Tools and Agents – Beyond the Prototype II — Self-Recursive Agents

This session is for builders who have hit the wall with prototype agents that degrade or stagnate. We are going beyond the prototype to tear down a self-evolving agent architecture.

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Mario will show you how we integrate Structured Experience Learning (SEL), Autonomous Versioning Optimization (AVO), and Human-Agent Collaborative Reinforcement Learning (HACRL) to build systems that actually learn. We’ll look at the honest 2026 line: what works today, what’s aspirational, and the gaps nobody talks about: like memory lifecycle decay, pruning governance, and compute orchestration. Finally, Mario will run a live demonstration of this architecture running on reproducible stack within access to many orgs.

What You’ll Be Able to Do / Learn / Walk Away With

• Architect agents that learn from experience using SEL (Structured Experience Learning).
• Implement execution telemetry and prompt evolution via AVO (Autonomous Versioning Optimization).
• Design a Behavior Registry and Bounded Work Packets to manage task risk and compute orchestration efficiently.
• Apply the STE Communication Discipline to ensure agent-to-agent clarity without token bloat.
  
  

  What Makes This Session Different

• No vision decks. We are looking at the architecture, the P&L, and the unit economics of autonomous systems.
• We are addressing the hard gaps: FadeMem decay, pruning governance, and the MinionS protocol (achieving 97.9% performance at 18% cost).
• This is a live, production-grade system teardown. Mario will show you the exact infrastructure (n8n, Supabase, Qdrant, OpenRouter) that runs MEF Solutions.
  
  

  Who Is It For

• AI engineers and builders who are tired of brittle prototypes and want to deploy resilient, self-improving systems.
• Technical founders trying to bridge the gap between AI experimentation and AI operations.
• RevOps and GTM leaders who need autonomous architectures to drive actual revenue, not just engagement.
  

  Before You Arrive 

• Review your current agent architecture and identify where the feedback loops are broken.
• Calculate the token cost and failure rate of your most complex autonomous workflow.
• Bring your skepticism. We’re going to look at what actually works in 2026.
  

  Instructor Bio

  Mario Facussé is the Managing Director of MEF Solutions, where he builds AI-powered revenue systems for growth-stage companies. He designs and operates autonomous agent architectures that self-improve in production; not prototypes, not demos, but systems that run his own company. His work draws from peer-reviewed research in self-evolving agents, collaborative reinforcement learning, and autonomous optimization, implemented on practical infrastructure. He’s spoken at leading AI conferences and events and advises companies on strategy, GTM, operations, revenue, and bridging the gap between AI experimentation and AI
  operations.

   

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