Building AI Agents That Improve From Production Data
- Date
- 2026-03-05
- Host
- Future AGI
About this event
Most AI agent demos look sharp in isolation. The real test starts after deployment, when user behavior, edge cases, failures, and feedback create the messy stream of production data that determines whether an agent actually gets better over time. This event is built for people who care about that gap between prototype and reality and want a clearer path to building agents that learn from what happens in the wild. About the Event This in-person gathering focuses on a practical question: how do you build AI agents that improve from production data rather than stall after launch? The conversation sits at the intersection of applied AI, autonomy, and product engineering, with an emphasis on the systems, feedback loops, and operational decisions that matter once agents are being used by real people. You can expect a community-centered format that brings together builders, technical operators, and curious practitioners who are thinking seriously about agent performance in production. Rather than staying at the level of broad theory, the event is designed around concrete challenges such as evaluation, iteration, data collection, reliability, and what it takes to close the loop between usage and improvement. This is also a networking environment for people who want to compare notes with others working on similar problems. If you have been trying to move from one-off model outputs to agents that can adapt, improve, and become more useful over time, this event gives you a room full of people asking the same hard questions. What to Expect The session will center on the lifecycle of an AI agent after it ships. That includes what kinds of production signals are worth collecting, how teams interpret those signals, and how data from real usage can inform the next version of an agent's behavior, tooling, or decision-making. Expect discussion that is grounded in implementation rather than hype. You should come ready for a mix of structured content and peer interaction. Depending on the flow of the gathering, that may include: Framing the core challenge of building self-improving or feedback-driven agents Discussion of common production failure modes and what they reveal Practical thinking around evaluation, observability, and iteration loops Conversation about autonomy, guardrails, and reliability in real deployments Time to connect with other attendees working in AI, engineering, or product roles Because the event is in person, one of the biggest benefits is the ability to have detailed, candid conversations that usually do not happen in public timelines or polished launch posts. You will be able to ask implementation questions, hear how others are approaching similar problems, and pressure-test your own assumptions with people who understand the technical and operational tradeoffs. If you are early in your journey, this can help you build a sharper mental model for what production readiness actually means. If you are already deploying agents, this is a chance to compare approaches and identify blind spots in your current feedback loop. Why Attend AI agents are easy to talk about in terms of capability, but much harder to run as dependable systems. The teams that make real progress are usually the ones with better feedback loops: they know what data to collect, how to evaluate outcomes, when to intervene, and how to turn production experience into better behavior. This event is for people who want to get more rigorous about that process. Attending can help you clarify questions like: What does "improvement" actually mean for an agent in production? Which signals are useful, and which are just noise? How do you connect user interactions to evaluation and iteration? Where should autonomy stop and human oversight begin? What makes an agent robust enough for repeated real-world use? You will also get the value of shared pattern recognition. Hearing how others think about deployment, monitoring, recovery, and continuous learning can save you time, sharpen your instincts, and help you avoid common mistakes before they become expensive habits. Just as importantly, this event creates room for the human side of technical work: meeting peers, building relationships, and finding collaborators who are serious about applied AI. If your work touches agents, data, or operational AI systems, the conversations here are likely to be directly relevant. Practical Details This is an in-person event taking place on Thursday, March 5 at 9:30 AM PST. Showing up in person matters here because so much of the value comes from live discussion, back-and-forth questions, and the kind of nuanced networking that is difficult to recreate online. The topic is especially relevant for people working across AI, tech, autonomy, and product or engineering workflows, but you do not need to fit a narrow title to benefit. If you are actively building, evaluating, deploying, or exploring agent-based systems, you will have useful context for the discussion. A good way to prepare is to arrive with one or two concrete questions from your own work. For example, you might be wrestling with evaluation design, production logging, failure recovery, user feedback, or the gap between promising internal tests and inconsistent live performance. Bringing those questions will make the conversations more useful and specific. Expect a room oriented toward thoughtful exchange, practical insight, and strong peer connections. If you want to think more seriously about how AI agents improve from real usage data, this is a strong place to do it.
Who should attend
This is for people who want to move beyond AI-agent theory and get sharper about what happens after deployment. - You are **building or shipping AI agents** and want better ways to learn from real-world usage instead of relying only on offline testing. - You work in **engineering, product, ML, or applied AI** and care about evaluation, reliability, observability, or iteration once systems are live. - You are exploring **autonomous workflows** and want to understand how feedback loops, guardrails, and production signals affect agent performance. - You are an **operator, founder, or technical leader** trying to connect user behavior and production data to meaningful improvements in agent quality. - You are curious about **how strong teams actually run agent systems in practice**, including how they handle failures, edge cases, and continuous improvement. - You value **in-person conversations with serious practitioners** and want to meet others working through similar technical and operational challenges. If you have been asking how an AI agent gets better after launch, not just how it looks in a demo, you will likely feel at home here.