Building AI-Native Systems: Skills, Infrastructure & Production Workflows
- Date
- 2026-02-17
- Location
- London, England, United Kingdom
- Host
- AI Native Dev
About this event
AI is no longer just a model problem. The real challenge is building systems that can operate reliably in the world: with the right infrastructure, the right workflows, and the right teams behind them. If you are thinking seriously about how AI moves from prototype to production, this event is designed to help you get concrete about what that actually takes. About the Event This in-person gathering in London focuses on AI-native systems: products, tools, and operational setups built with AI as a core capability rather than an add-on. That includes the practical questions many teams are now facing: what skills matter most, what infrastructure choices hold up under production pressure, and how workflows need to change when autonomy becomes part of the stack. The conversation sits at the intersection of AI, infrastructure, autonomy, and manufacturing, with a strong community angle. Expect a room of people who care not just about what models can do in theory, but how real systems are designed, deployed, monitored, and improved over time. Rather than staying at the level of broad trends, the event is centered on the operating realities of AI-native work. That means discussing the layers around the model as much as the model itself: data pipelines, orchestration, evaluation, human oversight, feedback loops, and the processes that make systems dependable in production. What to Expect You can expect an evening built around practical discussion, shared experience, and useful connection. The focus is on understanding how strong AI-native systems are actually put together, and what changes when teams move from experimentation to production workflows. Likely themes include: Core skills for AI-native teams: what builders, operators, and technical leaders need to understand now Infrastructure decisions: the systems, tooling, and architectural thinking that support reliable AI products Production workflows: how teams handle deployment, iteration, evaluation, monitoring, and governance Autonomy in practice: where agentic or semi-autonomous systems create value, and where they introduce complexity Manufacturing and real-world operations: what changes when AI interacts with physical processes, supply chains, or operational constraints Because this is an in-person event, a meaningful part of the value will come from the room itself. You should expect opportunities to hear how other people are approaching similar challenges, compare assumptions, and pressure-test your own view of what “production-ready” really means. This is also a strong format for people who want substance without formality. Whether you are deep in implementation or still mapping the landscape, the event gives you a way to engage seriously with the topic while meeting others working through related problems. Why Attend If you are building with AI today, you have probably noticed that the bottlenecks are shifting. The hard part is often no longer generating a compelling demo. It is creating a system that is observable, maintainable, cost-aware, and trustworthy enough to run repeatedly in production. This event is valuable because it addresses that shift directly. It is a chance to sharpen your thinking on how AI-native systems are architected, what technical and organizational capabilities matter most, and how teams can create workflows that support speed without sacrificing reliability. You should come if you want to leave with a clearer sense of: how production AI differs from prototype AI which infrastructure questions deserve early attention what operational discipline is needed for autonomous behavior how AI-native workflows affect team design and decision-making where opportunities and constraints are showing up in manufacturing and operational settings Just as importantly, you will be in a room with people who are likely asking similar questions from different angles. That makes this a good place to refine your ideas, discover practical patterns, and build relationships with others serious about the next generation of AI systems. Practical Details This event takes place in person in London, United Kingdom on Tuesday, February 17 at 6:00 PM GMT. The in-person format is a meaningful part of the experience: it is well suited to nuanced technical discussion, candid exchange, and stronger networking than you usually get online. If you are considering attending, come ready for thoughtful conversation rather than passive listening. This topic rewards specificity, and the strongest discussions often come from people willing to talk about real architecture choices, workflow tradeoffs, and the messy parts of production. A few useful things to keep in mind: Arrive with questions about systems, infrastructure, or team workflows you are actively thinking through Be ready to compare notes with people from adjacent domains, including autonomy and manufacturing Expect a practitioner-oriented atmosphere focused on implementation, operations, and lessons learned Plan for conversation before and after the main session, since some of the best value often comes from the room If your work touches AI systems beyond the demo stage, this is the kind of event that can help you think more clearly about what comes next.
Who should attend
This is for people who want to understand how AI systems are really built, operated, and scaled once they leave the prototype phase. - You are an **engineer, platform builder, or infrastructure-minded technical contributor** working on AI products and want sharper thinking on deployment, observability, evaluation, and reliability. - You are a **founder, product leader, or technical decision-maker** trying to turn promising AI capabilities into systems that can work consistently in production. - You are exploring **autonomous or agentic workflows** and need a more grounded view of the operational, architectural, and governance challenges involved. - You work in or around **manufacturing, industrial systems, or operational environments** where AI has to interact with real-world constraints, not just clean demos. - You are part of a team figuring out the **skills and workflow changes** required for AI-native development, from experimentation through iteration and maintenance. - You value being in a room with other serious practitioners in London and want conversations that go beyond hype into how things actually work. If you are looking for practical insight, better questions, and peers who care about production reality, you will fit right in.