An AI agent knows only what you show it — and it won’t suggest a solution you don’t know exists. On top of that, it forgets everything between chats. This talk explains what knowledge a human needs so the agent can work at full power, and how to build memory for it: from local files to shared memory via an MCP server. You’ll learn: — why agents miss obvious solutions and how to fix it — the memory bank pattern and a ready-to-use file structure — the levels of agent memory: chats, local files, Obsidian, git, MCP memory server — practical rules for keeping agent memory up to date

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!

In the agentic-first era, we’ll bring all your project data together in one place and give agents access to it. We’ll discuss the problems this would solve, how to collect and validate the data, and how to keep it up to date. And also you will see how this approach will change the way your team works.