AI Coding Summit London

There is no single correct way to assemble a software factory. Labs want us to trust agents more than we should. Tech influencers gloss over important, practical details. This workshop introduces several composable patterns for making a codebase more factory-like: to improve the throughput, quality, and verifiability of autonomous agent work in your project.We will cover: - Planning the work that gets handed over to a software factory- Converting plans and specifications to durable internal docs (and rules) that don’t go stale- Task decomposition for coding agents, and the tools that help with it- Review strategies and tools that can manage massive PRs - Agent-friendly QA setups that allow coding assistants to verify their work and fix bugs before human review- When you can safely skip human review entirely- The role of sandboxes (local and remote) for scaling and handling agent work on your codebase

AI coding assistants are evolving from simple autocomplete to autonomous agents that can interact with external systems. But how do you teach an agent to follow your workflows and use the right tools at the right time?In this hands-on workshop, you'll discover:What MCP (Model Context Protocol) is and how it standardizes tool integration for LLMsHow Agent Skills package domain-specific knowledge and workflows for AI agentsThe key differences between MCP servers and Skills, and when to use eachHow to create a custom Skill that orchestrates multiple MCP tools into a cohesive workflowBy the end of this session, you'll build your own AI Skill that uses MCP tools — a Matrix-themed Neo fighting skill.Who should attend: This workshop is ideal for software developers who use AI coding assistants (Claude Code, Cursor, or similar) and want to extend their capabilities with custom integrations and workflows. No prior MCP or Skills experience required — just bring your curiosity and a laptop.

Most developers using AI coding tools hit the same wall on real projects: the demos look magical, but production work falls apart. This workshop shows why — and what to do about it.The core lesson: execution is fast and cheap only when preparation is deep.What we'll build, liveFrom an empty directory to a deployed production app — a mobile-first Conference Companion App with the event schedule, speaker profiles, search, and favourites. Attendees will open it on their phones before the session ends.
The four stages of shipping with AIYou'll see a complete production workflow, deliberately weighted toward the work that actually determines quality:- Research — Gathering everything the AI will need upfront: official docs, code patterns, real data. By execution time, nothing has to be searched for. This is where quality comes from.- Planning — Breaking the app into clear tasks with acceptance criteria, written live. Skipping this is why most AI builds fail.- Execution — Ten tasks running in parallel via git worktrees, orchestrated by Navigator (a Claude Code plugin) on a React/Next.js stack. Code and docs written together.- Review & Ship — Quality gates (tests, lint, types, build), clean commits, merge, deploy to Vercel. Live URL shared with the room.
Bonus: Human + Claude Code vs. fully autonomous agentWhile the live build runs, Pilot — our autonomous coding agent — builds the same app from the same spec, on its own, in a separate repository. At the end, we open both side by side: two working apps, two pull request histories, two live URLs. One human-driven, one fully autonomous.
You'll leave withA repeatable workflow for shipping real software with Claude Code, plus concrete techniques you can apply to your own projects on Monday morning.