Despite decades of investment in automation, many high-value industrial operations in manufacturing remain largely manual. The limiting factor is no longer hardware – lack of machines or robots – but software. Programming automation of complex tasks is slow, expensive, and dependent on expert and domain specific knowledge.

While AI has been transforming software systems towards higher autonomy, making production environments autonomous is fundamentally harder. Conversational AI can learn through access to massive datasets on the internet, however, there is no such treasure trove or robot data.

This talk will analyze why today’s AI successes in robotics focus on controlled, low-value tasks, and why expectations around humanoids and general-purpose robots remain misaligned with industrial reality. The core challenge is data generation: without systematic ways to capture manufacturing and production data, learning-based robotics cannot scale. And without measures to protect the data and control the learning process, the solutions cannot be trustworthy, nor safe. Our approach to reliable data foundation required for the adoption of AI in robotics will be shared with the audience to open discussions about its benefits, challenges, and opportunities.

Software development is entering a new era where natural language becomes a first-class interface for automation. In this session, we explore Agentic Workflows, a cutting-edge research project from GitHub Next that enables developers to describe complex software tasks in plain language — and let intelligent agents execute them. These workflows understand context, make decisions, orchestrate tools, and perform meaningful actions across repositories, going far beyond traditional CI/CD pipelines.

We provide a deep technical dive into how Agentic Workflows operate: the execution engine, decision-making loops, context grounding, tool calling, safety constraints, and how agents interact with GitHub APIs. Through live examples, we demonstrate how natural-language instructions can trigger autonomous refactoring, dependency updates, code generation, documentation improvements, and multi-step engineering tasks.

Participants will gain insight into the emerging discipline of Agentic Engineering: designing systems where agents collaborate with developers, support daily workflows, and automate engineering tasks that previously required manual effort. We discuss practical use cases, limitations, and the future potential for A2A (Agent-to-Agent) architectures running across development teams and repositories.

This talk gives a first-hand look into the next frontier of software engineering — where code, automation, and AI converge into intelligent, natural-language-driven workflows. I am a contributor to the https://github.com/githubnext/gh-aw project, however in agentic time you mainly submit issues (see bug tracker) and the agents fix/implement new features. Looking into the future, we will have to change the contributor metrics (currently based on LOC)