Agentic AI systems are transforming how developers approach the Software Development Lifecycle, enabling a shift from "coder of every line" to "architect of outcomes." These autonomous agents understand context, reason about complex problems, and execute multi-step workflows across the entire SDLC. We will demonstrate how agentic AI tools accelerate open source development while maintaining code quality and security.

The session will include:

• Overview of agentic AI capabilities: multi-file refactoring, legacy modernization, security-first development, and knowledge transfer.
• Architectural patterns for AI integration: Model Context Protocol (MCP) for connecting to external systems and Agent Skills for domain-specific guidance.
• Demonstrations of context management, spec-driven development, MCP-based integrations, and agent skill implementation.
• Best practices for balancing automation with human oversight in open source projects.

Attendees will leave with practical understanding of how to leverage agentic AI to accelerate development, while simultaneously maintaining control using intent-based specifications in their open source projects.

Target Audience

Open source maintainers, software architects, DevOps engineers, and developers interested in AI-assisted development

In this talk, we will introduce vLLM and explore why monitoring matters for LLM serving. We'll start from why serving LLMs is hard in the first place and build up to what that complexity looks like as real, observable metrics.

vLLM is a high-performance library designed for LLM inference and serving. Its core optimizations, including PagedAttention, continuous batching, and memory management, directly shape the behavior you'll see in production. Understanding what those techniques are doing under the hood makes it easier to understand what your metrics are actually telling you.

The session will include a demo showing how to instrument vLLM with observability tooling and interpret the signals that matter: KV cache usage, request queue depth, and end-to-end latency. No GPU or production cluster required. Just a small model running locally and some practical monitoring tools.

By the end of this session, you'll have a clearer mental model of LLM serving, a working understanding of vLLM's built-in observability, and the monitoring foundation to understand what's happening inside your server before something goes wrong.