Audience
Who this is for
Engineering Leadership (CTOs, VPs, EMs)
Establish a scalable strategy for AI adoption across your organisation. We guide leadership in standardising agentic workflows, governing secure access to production repositories, and measuring authentic velocity improvements beyond raw output metrics.
Staff & Senior Engineers
Elevate your technical leadership from code generation to systems orchestration. Your lead engineers will master building custom MCP integrations, scripting autonomous multi-file architectural updates, and asserting strict design patterns at scale.
Mid-level & Junior Developers
Accelerate the trajectory of your developing talent. We equip them with advanced agentic debugging and proactive context-management techniques, enabling them to independently resolve deep architectural dependencies and compound their daily impact.
Overview
Workshop overview
AI coding now requires an architectural mindset, shifting from basic autocomplete toward sophisticated agentic operations. In this intensive 3-day deep-dive, we train your engineering teams to treat AI as an autonomous technical peer: architecting bespoke multi-agent pipelines, establishing strict MCP-enabled quality gates, and reliably automating comprehensive epics from initial triage through to tested production readiness.
Curriculum
The three days
The days are cumulative and run in order. Each builds on the one before, so a team starts at Day 1 and continues as far as it needs. Every exercise uses your team's own tasks and files, so the series fits whatever your function does.
Advanced Agentic Workflows & System Architecture
The team builds a working agent from the ground up — one that reads a ticket, proposes a solution, and commits validated code. By taking the agent stack apart in production context, engineers stop treating AI as a black box and start treating it as a composable system.
- Build an agent that reads a Jira ticket, produces a solution, and commits validated code
- Master the intersection of reasoning models, memory, and tool integration at production depth
- Automate multi-stage engineering pipelines that currently depend on sustained manual effort
Agent Architecture & Performance Optimisation
Not every problem needs the same architecture, and over-engineering is its own failure mode. The team learns to match agent design to the actual task — picking the right pattern, keeping API costs predictable as usage scales, and tying every decision to something the business can measure.
- Match retrieval-based and action-oriented agents to the right use case — and know when each breaks down
- Cut token consumption and manage compounding API costs without sacrificing capability
- Tie agent architecture to outcomes the business can measure, not just benchmarks
Governance, Safety & Human-in-the-Loop Operations
Autonomy creates risk alongside capability. The team builds the trust frameworks and guardrails that let agents run confidently in production — putting human oversight exactly where it matters and removing it where it only slows things down.
- Put human-in-the-loop checkpoints where they matter and remove them where they create drag
- Define governance protocols for where agents intersect with critical business data and systems
- Orchestrate multi-agent workflows using Deep Research, NotebookLM, and AI Studio
Workshop details
Larger cohorts available on request.
