Agentic engineering is maturing. We are moving beyond “ask an agent to write some code” toward factories — pipelines where a team of AI agents takes a project through the entire lifecycle: requirements → specifications → tests → implementation → adversarial review → quality evidence. In this talk, we’ll break down what such a factory actually consists of: an orchestrator and specialized sub-agents, deterministic quality gates, end-to-end traceability from requirements to tests, and verification loops that replace “it seems to work” with measurable evidence. The main challenge of autonomous development is not getting agents to write code. It is preventing the factory from confidently reporting success when the result is actually wrong. We’ll examine common mechanisms of pipeline self-deception: “soft” rules in prompts that disappear under the pressure of a real-world project; checks that silently pass when required evidence is missing; and requirements whose acceptance criteria were never implemented as executable checks. I’ll illustrate these problems with real-world artifacts, including a case where the factory passed every gate “green” yet produced a product that failed to meet a key requirement — and what it took to make the exact same run correctly turn red. A separate part of the talk will focus on self-improvement: how to build a loop where every discovered failure becomes a new gate that future projects can no longer bypass. We’ll cover three-state verification semantics (PASS / NOT-EARNED / FAIL), a process-defect registry, and a library of lessons inherited by every new project. Everything will be grounded in real artifacts: commits, metrics, and measurable results. What we’ll cover: • Anatomy of an agentic factory: orchestrator, sub-agents, deterministic gates, and end-to-end traceability • Pipeline self-deception mechanisms — and the forensics of a real “false done” • Maker ≠ checker in practice: adversarial review that actually catches defects • The self-improvement loop: correction → retrospective → new gate → lesson for the next project • What it costs: tokens, time, and where the factory still loses to humans Who is this talk for? Engineers and tech leads who already use AI agents in software development and want to move from “it works sometimes” to a predictable, evidence-driven engineering pipeline with verifiable quality.

AI development hits everyone, so hit us. Everybody wants AI agents to replace regular UIs. In this talk, I will describe the evolution of our multitool AI agent, built with Node.js on top of Google Vertex AI. I’ll dive into our journey of choosing the right models and scaling development through CI/CD, TDD, and performance monitoring. Is it even possible to achieve stable results for AI projects that can hallucinate and return various responses? Interestingly, we eventually decided to remove MCP servers and Zod schema validation—technologies often considered the "standard" for these tasks. Want to know why we moved away from them? Join my session to get these insights and ask your questions live!

Ready to break down the walls between developers and DevOps and lighten your workload? In my quick talk, I want to share my experience on how we improved collaboration between teams and which parts of the work can – and should – be handed over to developers.

We’ll code dive on one of our products to learn how we added AI to it.