Beyond the Prototype II: Human Guidance and Self-Improving AIs

Build the AI agent that does useful work, grades itself, and gets better without becoming a corporate liability.

Most AI “agents” are just chatbots with a LinkedIn headline. They produce something once, nobody checks whether it was any good, and the whole thing quietly becomes another tab you stop opening.

This is the antidote.

Beyond the Prototype II: Self-Recursive Agents is a three-hour, hands-on build session for operators who are done admiring demos and ready to put AI to work. You will build a real, useful agent around a task that matters to your day job: researching a market, drafting content, summarizing inbox traffic, preparing a customer brief, or another workflow you actually want off your plate.

Then we make it smarter. Your agent will evaluate the quality of its own output, propose a specific improvement, test that improvement against a benchmark, and adopt the new version only if it beats the old one. That is the difference between an AI trick and an operational system.

This is the longer, deeper sequel to Mario Facussé’s popular first Austin AI Alliance session on self-recursive agents. The original session opened the hood. This one hands you the wrench, keeps the theory on a short leash, and gives you three focused hours to build. You do not need to be technical. You do need to be willing to stop outsourcing your operational leverage to a pile of prompts and wishful thinking.

The promise: You will leave with a working recursive agent and a practical operating model for improving it safely—without having to babysit every output, every prompt, or every “clever” idea the model has at 2:00 AM.

What You’ll be Able to Walk Away With

• Turn a real business problem into an agent with a job description. You will define one useful task, specify what “good” looks like, and build a working agent that produces an output you can use—not a generic demo that dies the moment you get back to work.
• Give your agent a quality-control brain. You will add a self-evaluation layer and a small set of “golden tasks” so the agent can judge its output, identify weaknesses, and propose a targeted improvement rather than simply generating more confident nonsense.
• Build a safe improvement loop that earns autonomy. You will implement a simplified recursive cycle: generate, evaluate, improve, test, and deploy only when the candidate version passes. You will also leave with guardrails that stop regressions from sneaking into production wearing a tiny AI trench coat.

What Makes This Session Different

• You build the machine; you do not watch Mario build it. This is not a keynote with tasteful screenshots and a QR code leading to disappointment. The session is deliberately designed for hands-on progress, with clear checkpoints and a usable result by the end.
• It teaches the part most AI workshops skip: safe self-improvement. Anyone can prompt a model. Few people know how to make an agent learn from failures, test its own changes, and reject a “better” version when the evidence says it is worse. That is the useful bit.
• It translates production-grade agent design into operator language. Mario will distill the same ideas behind his ATLAS system—adaptive refinement, self-evolving learning, autonomous validation, trust tiers, regression gates, and benchmark testing—into a version you can understand, build, and extend without a computer-science degree or a spiritual relationship with YAML (Yet Another Markup Language).

Who it is for

• Ambitious executives, managers, and operators who are responsible for outcomes, buried under repetitive work, and tired of hearing that AI is “transformative” without seeing what to do on Monday morning.
• No-code and low-code business builders who want to move beyond one-off prompts and create an AI system that can improve a repeatable business task while maintaining sensible human oversight.

Before you arrive

• Bring one process you would happily fire from your calendar. Choose a repetitive, judgment-heavy task that takes at least 30 minutes each week: research, content drafting, account preparation, inbox triage, meeting summaries, competitive monitoring, or a similar workflow. If it is annoying and measurable, it is a strong candidate.
• Complete the short setup checklist sent before the event. Arrive with your laptop charged and your selected tools/accounts ready to use. The checklist will be intentionally simple; its only purpose is to ensure you spend the session building, not negotiating with a forgotten password reset link.

Instructor Bio

Mario Facussé is the Managing Director of MEF Solutions, a consulting firm helping private equity operating teams identify high-value bottlenecks and solve them with agentic swarms. He hdesigns self-evolving agent systems that move beyond the prototype phase: systems that handle business work, measure their own quality, learn from mistakes, and earn greater autonomy through evidence rather than hype.

Mario’s ATLAS system coordinates more than ten specialized AI agents across business functions, from content creation and financial analysis to infrastructure monitoring. Its operating model combines adaptive prompt refinement, self-evolving learning, autonomous validation, capability-based trust tiers, regression gates, and golden-task benchmarks—because an agent that changes itself without a test is not autonomous; it is unsupervised.

He has spoken at Austin AI Alliance and Comms Week, and he advises enterprise teams on turning AI experiments into production-grade systems that produce measurable work. Mario’s approach is direct: show the machine, prove the outcome, then scale what works. Connect with him on LinkedIn.