Why the Future of Automation Lies Not in the Number of Developers, but in the Accessibility of AI Agents Traditional automation requires time, budget, and the involvement of IT teams. But what if every employee could automate their own processes independently? Agentic Workforce is transforming the very principle of how work is organized. People set goals, agents perform tasks, and the platform provides the tools, access, security, and control. I’ll share our experience building and implementing the Agentic Workforce product in an enterprise organization, the architecture of the agent platform, managing digital agents, and how we envision the transformation of corporate automation.

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.

Why do some teams speed up with the same AI tools while others drown in slop? We'll go through common anti-patterns of working with AI agents in development, from "generate everything at once" to blind trust in the output, what they lead to in practice, and what to do instead.

As the classic once said, “life should be interesting.” Especially in interesting times. And especially when AI barges into our cozy little bubble uninvited and starts threatening to take our jobs. But if you can’t beat them, lead them. So I decided to be the first to voluntarily give AI some of my work. Well, not exactly give it away — more like pass on the honorable title of React monkey. Somewhere along the way, though, I got carried away and ended up building an entire troop of AIpes, each with (almost) clearly defined roles, (almost) well-defined responsibilities and constraints, and the ability to handle my tasks with (almost) a single command. That’s what I want to talk about: how a simple idea of making an LLM write routine code turned into a whole team of specialized agents; how I built my own agent harness in Claude Code; what worked, what broke, and what workarounds I had to invent along the way. And finally, what training my AIpes taught me about models, prompts, and why adding “pwetty pwease” to your prompt still won’t make a rule actually bind.

If your development process looks like “I write a prompt in Claude Code, and then we get the job done using my favorite set of skills”—then you’re a year and a half behind the times, which, in our AI reality, is an eternity. Nowadays, graphs and self-improving loops are all the rage—you define a task, and it’s broken down into 100,500 separate blocks, each of which is simple, produces a measurable result, and is therefore easy to manage and improve. Sounds perfect? Yes—until you get tired of a simple task taking an hour and a half, or until you start calculating how much it costs (in money or tokens—it doesn’t matter). Over the past six months, I’ve built six such factories as a consultant—all following the same blueprint—but now, with every new client, I start a long conversation explaining that you most likely either don’t need this approach, or you do—but in a different way. We’ll touch on three aspects—technological, economic, and cultural—to explain where the problems actually arise and why I no longer consider the “dark software factory” in its graph-and-block form to be a silver bullet for everyone.

A few years ago, we started building Architecture as Code with a fairly simple goal: make architectural knowledge accessible, up to date, and understandable for engineers. C4 diagrams, ADRs, OpenAPI specifications, ERDs, ownership, and other documentation gradually moved closer to the codebase, becoming version-controlled and machine-readable. We built it for humans. Then AI agents arrived. And unexpectedly, we discovered that one of the hardest parts of working with AI — transferring enough system context to an agent — had already been largely solved. In this talk, we’ll explore why repository access alone is not enough, how code context differs from system context, and how Architecture as Code naturally evolves into Context Engineering. Using practical examples, we’ll look at the context an AI agent needs to do more than just write code: understanding domains, architectural decisions, API contracts, ownership, and system constraints. We’ll also discuss the next challenge: ensuring that the context AI relies on actually reflects the real state of the system.

Building an AI agent is only half the job. The real challenge is knowing whether it performs reliably and continues to meet expectations as models, prompts, tools, and data evolve. In this session, we’ll use a practical example to show how to build effective Evals for AI agents: creating representative test cases, defining meaningful quality criteria and metrics, combining automated checks with LLM-as-a-Judge and human evaluation, comparing different agent versions, and making Evals a core part of the development and testing lifecycle.

Nowadays, everyone is creating AI Assistants to help us in our day-to-day activities and "vibe coding" nice and shiny demo apps. However, when bringing AI Assistants to life in production—especially for use by citizens of a Gov Portal—it becomes clear that creating the AI Agent itself is only 10% of the effort required. The other 90% is spent on addressing cross-cutting concerns and meeting the requirements of different stakeholders. This talk will be based on the speaker's own experience of putting citizen AI Assistants for a Gov Portal (built on top of an AI Agentic Platform) into production. He will discuss the challenges his team faced, concerns raised by different groups of stakeholders, important NFRs and quality attributes of the solution, and possible approaches to addressing these challenges. The talk will provide useful tips, offer a broader perspective on real-world challenges, and encourage critical thinking. This talk will be interesting for professionals in various roles who want to gain a broader understanding of real-world challenges and improve their critical thinking. Among other topics, the speaker will cover: -Naive chatbot implementation vs. scalable and mature implementation -Benefits and drawbacks of using an AI Agentic Platform vs. custom implementation -Choice of Public LLM vs. Private LLM -Performance and quality challenges -Infrastructure -Costs -Error handling -UI/UX -Guardrails and protection against misuse and DDoS attacks -Observability -Multilingual support -Testing and evaluation framework -PII data handling -And more...

Today, AI can answer questions, search for information, and complete tasks. But how do you turn it from just another chatbot into a true extension of a brand? Using Mushroom Hennadiiovych, Silpo’s AI assistant, as a case study, I’ll share how we built a product that brings together modern LLMs, product thinking, and a distinctive brand personality. We’ll explore the journey from idea to launch, the solution architecture, the trade-offs, the mistakes we made, and the lessons that can help anyone building AI products.