The Rise of Agentic Workflows: AI That Understands, Decides & Codes

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)