Joseph Katsioloudes

Coding agents help us ship fast, but they’ve introduced a new challenge: ensuring developers can understand, trust, and maintain agent-generated code.

That challenge now shows up across the software development lifecycle:

1. Context is scattered across multiple sessions. 2. Developers have more code to review and less context to review it with.
2. Code hosts face more automated activity than they were designed for.
3. Agents burn tokens just trying to understand the codebase.

Solving these problems requires a new workflow and mindset for working with agents. In this hands-on workshop, you’ll learn how to preserve context across sessions, reduce wasted tokens, and review agent-generated changes with the context you need to merge confidently.

This workshop is for engineers, tech leads, and reviewers of all experience levels who want to work more efficiently with coding agents.

Coding agents help us ship fast, but they’ve introduced a new challenge: ensuring developers can understand, trust, and maintain agent-generated code.

That challenge now shows up across the software development lifecycle:

1. Context is scattered across multiple sessions. 2. Developers have more code to review and less context to review it with.
2. Code hosts face more automated activity than they were designed for.
3. Agents burn tokens just trying to understand the codebase.

Solving these problems requires a new workflow and mindset for working with agents. In this hands-on workshop, you’ll learn how to preserve context across sessions, reduce wasted tokens, and review agent-generated changes with the context you need to merge confidently.

This workshop is for engineers, tech leads, and reviewers of all experience levels who want to work more efficiently with coding agents.

Coding agents are writing code faster than ever, picking up issues and stories with growing autonomy. Yet context is fragmenting across chats, prompts, sessions, and branches, leaving agents stateless and developers without a shared understanding of intent, decisions, and learning. And the human developer still sits at the center of the pull request, reviewing code and suggestions.

It's time for a new software development lifecycle for a world of agent-to-human and agent-to-agent collaboration, one that collapses planning, coordination, design, implementation, review, and deployment into a continuous loop. And one that removes bottlenecks while preserving quality, trust, and developer happiness. Join Joseph Katsioloudes for a demo-filled talk on what this loop looks like in practice today, where it breaks, and what you can start changing in your own workflow.

Coding agents are writing code faster than ever, picking up issues and stories with growing autonomy. Yet context is fragmenting across chats, prompts, sessions, and branches, leaving agents stateless and developers without a shared understanding of intent, decisions, and learning. And the human developer still sits at the center of the pull request, reviewing code and suggestions.

It's time for a new software development lifecycle for a world of agent-to-human and agent-to-agent collaboration, one that collapses planning, coordination, design, implementation, review, and deployment into a continuous loop. And one that removes bottlenecks while preserving quality, trust, and developer happiness. Join Joseph Katsioloudes for a demo-filled talk on what this loop looks like in practice today, where it breaks, and what you can start changing in your own workflow.

Artificial intelligence (AI) already serves as a copilot in our daily lives, acting as a digital assistant and delivering personalized experiences. Despite progress in many areas, AI has historically fallen short of improving software development practices. This changed with the introduction of AI pair programmers, which distill the collective technical know-how of the world’s developers, and their widespread adoption has been quite telling.


While the process of building software has become easier and faster, the question remains: What about more secure? In this session, we’ll demonstrate six practical ways developers can use AI to tap into the world’s security knowledge, showcased through 14 demos in GitHub Copilot. The audience will gain a deep understanding of AI capabilities for security, the pros and cons of security MCP servers, how to make informed decisions for supply chains, and other key practices along with insights drawn from our own lessons as developers striving to ship secure code. Finally, we’ll share a playground repository where attendees can safely experiment with everything demoed.