Mastering Agentic Coding - Session Playbook

Who This Is For

You already use AI coding tools daily. You've got Copilot or Cursor in your workflow and you're comfortable prompting for code. But you know there's another level - you've seen demos of multi-agent systems building entire features autonomously, and you want to get there with your own projects.

I run multi-agent coding workflows every day on production codebases. I've benchmarked different orchestration strategies, measured what actually improves output quality, and built systems where 3-5 AI agents coordinate on complex tasks. This session is where I share what I've learned.

Common situations:

Engineers who use Cursor or Claude Code daily but hit a ceiling on what they can delegate
Tech leads designing AI-assisted development workflows for their team
Developers who want to set up multi-agent pipelines for larger coding tasks
Engineers whose AI tools keep losing context on bigger projects and want to fix that
Anyone building with CLAUDE.md files, custom instructions, or MCP servers who wants to do it better

What We Cover

Multi-Agent Orchestration

Running parallel agents on a single codebase. Coordinator patterns, task decomposition, file-based handoffs, and how to avoid agents stepping on each other's work.

Project Architecture for AI

How to structure your repo, CLAUDE.md, and context files so agents can navigate your codebase effectively. The difference between a project AI struggles with and one it flies through.

Autonomous Coding Patterns

Moving from "write this function" to "implement this feature end-to-end." How to give agents enough context and constraints to work autonomously without constant hand-holding.

Context Management at Scale

Keeping agents effective on large codebases. Memory files, persistent instructions, subagent delegation, and strategies for when your project outgrows a single context window.

Testing & Verification

Getting agents to write meaningful tests, run them, and fix failures autonomously. Setting up verification loops so you can trust agent output without reviewing every line.

Production Team Workflows

Integrating agentic coding into code review, CI/CD, and team processes. What changes when half your PRs are AI-assisted, and how to maintain code quality at scale.

Example Scenarios

Senior engineer, uses Claude Code daily but can't get it to handle multi-file features reliably

We'd look at how you're structuring prompts and context for larger tasks, set up a CLAUDE.md and memory system for your project, and walk through patterns for breaking complex features into agent-sized chunks that compose into working code.

Tech lead, wants to roll out agentic coding workflows across a 10-person team

We'd design a shared context architecture (CLAUDE.md, coding standards, test requirements), discuss how to integrate AI-generated code into your review process, and build a playbook for onboarding team members from basic copilot usage to autonomous agent workflows.

Full-stack developer, wants to run multiple agents in parallel on a monorepo

We'd cover coordination strategies - file-based protocols, task backlogs, agent notes - and discuss which patterns work for your repo structure. I'd share real benchmarking data on what orchestration approaches actually improve throughput vs. just adding complexity.

How to Prepare

Before the session

Know which AI coding tools you currently use and your comfort level with each
Have a specific project or codebase in mind that you want to apply these techniques to
Think about the biggest task you've tried to delegate to AI that didn't work well
If you use CLAUDE.md or custom instructions already, have them ready to share

What to expect

Deep technical discussion, not a beginner overview - we'll move fast
Real patterns from production multi-agent systems, backed by benchmarking data
Specific recommendations for your project architecture and workflow
Concrete setup steps you can implement the same day

3-Session Bundle

Mastering agentic coding is best done iteratively - set up, practice, refine. A typical 3-session arc:

Session 1: Architecture and setup. Design your project's AI context layer (CLAUDE.md, memory, instructions), establish multi-agent patterns for your codebase, and set up your first autonomous workflow
Session 2: Scaling up. Review what's working, troubleshoot failure modes, add multi-agent coordination, and optimize your prompts and context based on real usage
Session 3: Production patterns. Team rollout strategy, CI/CD integration, code review workflows for AI-generated code, and advanced orchestration for your most complex tasks

All 3 sessions must be used within 2 months.

Book a Session