Software engineering is moving from agents that help write code to autonomous systems that take an approved requirement, write the code and verify the result with little human supervision: software factories. The approach is new, yet OpenAI, Anthropic, Stripe, Uber and others have already described theirs publicly. Each company tells its own story, but the same principles run through them: what a factory may build, how it proves the result is correct, how far its agents can be trusted, and how it keeps paying off. This talk walks through these principles and why each one is needed, drawing on those public write-ups and on the factories I build at Dev AI Consulting. The measure of success throughout: a factory whose “done” you can trust.

Ambition for 2026 was to boost team's performance by 15-20%... we made 30-50%. AI-assisted SDLC has been converted into AI-based SDLC where people work together with agents with clear responsibility separation: humans - for sense and control, agents - for routine. We use "human-in-the-loop" approach and experiment with agent loops. Can we treat that as an "AI software factory"? Let's think about together... I will show how we work based on one of our biggest solution consisting of 152 projects with 2.5 mln lines of source code and 18.7 mln of tokens to describe shared context. How do we support it? How do we overcome context window size limits? See you on the meeting!

This talk will explore what software engineering looks like when a product becomes part of a national digital infrastructure. Millions of citizens, government registries, critical infrastructure, personal data, dozens of integrations, and strict security requirements fundamentally change the rules of software development. Things that work well in the private sector may require a completely different approach in GovTech. We will follow the journey from idea to production, covering the principles behind building a SuperApp, product discovery, the specifics of the SDLC, on-premise and cloud infrastructure, cybersecurity, and information security. We will also take a closer look at AI in GovTech — from collecting and preparing data for proprietary models to developing AI components and integrating them into government digital products. The talk will show how to balance real-world constraints with modern engineering practices while maintaining the ability to build and continuously evolve digital services at national scale.

What is really happening to outsourcing companies in the AI era? Clients have stopped asking, “How long will this take?” — they now come in with their own estimates. They have already reassessed what we should be capable of, without us making any promises or even agreeing with those expectations. Long-term planning has broken down, and with it, the financial model and the traditional role hierarchy. This talk is about how we are navigating that shift, through two specific cases. The first: a client asked for a 10× productivity increase starting with the very next sprint. We never promised that manual work would disappear — another vendor did. The client removed 6 out of 10 engineers from our team and replaced them with a “swarm of agents.” We’ll look at how that swarm was set up, why features still failed to make it to production, and why engineers are now being brought back. The second: we made the same bet ourselves — pushing AI adoption as far as possible in an internal experiment. The result: 2.5 months instead of one, one out of four features rewritten after release, and hundreds of bugs. But there were valuable lessons. We discovered that the real delivery bottleneck was pull requests, built a PR classifier around that insight, and defined an order of operations between Claude, the classifier, and developers — along with what we call the 80% rule. And finally, we’ll address the question every outsourcing company now has to face: the same feature takes fewer billable hours at roughly the same rate. The client pays less, while we have less room to be wrong.

A story about the creation and development of the largest military app, the ins and outs of IT support, and a look at the pros, cons, and pitfalls of the process