AI coding assistants are great for individual developers, but building an autonomous multi-agent pipeline across a team's entire agile sprint workflow surfaces harsh architectural realities. Moving past basic prompt engineering requires designing strict integration boundaries, robust state management, and deterministic validation layers.
In this highly practical case study, we pull back the curtain on a 7-phase agentic software factory spanning backlog refinement to automated verification. We will examine the real-world friction of multi-agent orchestration, the technical realities of context window depletion, and how we balanced lightweight local execution (Phi via Ollama) with large cloud models. Attendees will walk away with a practical blueprint for using the Model Context Protocol (MCP) to securely connect AI agents directly to local IDEs and deployment pipelines without breaking corporate compliance frameworks.
What you will learn:

- Orchestration Patterns: Design patterns for coordinating reliable state handoffs between specialized AI agents.
- Deterministic Safety: Implementing schema-driven validation gates to safely contain non-deterministic LLM behavior.
- MCP Integration: Practical strategies for utilizing Model Context Protocol to hook models securely into local developer workspaces.