Product Agility
Building an AI-native platform

Small team.
Agent-scale ambition.

I’m exploring how AI agents can operate as a real delivery organisation — understanding requests, proposing improvements and building the platform autonomously inside a controlled environment.

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01 Why Product Agility

Time to learn.
Space to build.

Semi-retirement has given me more freedom to follow the things I want to learn, without losing the pleasure of doing useful work.

Renovating my house has introduced me to carpentry, insulation, roofing and the satisfaction of making progress with my own hands. It has also reminded me that every worthwhile problem rewards curiosity, patience and a willingness to rethink the plan.

A knee replacement added a rather more literal pause. Time away from the usual pace gave me room to recover, think and decide what I wanted the next chapter to contain. The answer turned out to be equal parts learning, making and seeing what AI agents can do when they are given a proper job — and sensible boundaries.

Product Agility has long been a home for my experience in strategy and product management. Now it is also my AI learning laboratory.

The long story, shortened ↗
01Make

Learning by doing, whether in a codebase or on a roof.

02Learn

Staying curious enough to be a beginner again.

03Orchestrate

Giving specialist agents the context, tools and guardrails to deliver.

02 The work in progress

A platform that can help build itself.

I’m building a configurable platform where ideas, context, comparisons and workflows sit alongside an agent organisation capable of improving the product itself.

User requests can become structured enhancement work automatically. Agents investigate, plan, implement and test — while approvals, permissions and audit trails keep people firmly in control.

Visit the platform holding page ↗
03 AI & autonomous delivery

Not one assistant.
An organisation.

I’m designing a system of specialist agents that can collaborate continuously. Each has a defined remit, limited authority and a clear hand-off to the next role.

SignalUser request

Feedback and enhancement ideas enter a structured queue.

→
Agent organisation
01Understand
02Design
03Build
04Test
→
ControlHuman approval

Evidence, permissions and audit history govern release.

01

Automatic intake

User needs become classified, prioritised improvement candidates.

02

Autonomous execution

Specialist agents coordinate work from discovery through testing.

03

Controlled change

Approval gates, scoped access and audit trails keep people in control.

04 The technology

Learning by
building.

A modern TypeScript platform, containerised for repeatability and connected to leading AI models and coding agents.

OpenAI CodexClaudeTypeScriptReactNode.jsPostgreSQLDrizzle ORMDockerViteAgent workflows
05 Notes from the build

What I’ve been
working on.

Short dispatches from an evolving platform.

01
Build note

Giving agents a controlled route to production

Scheduled jobs, explicit approvals, scoped access and auditable changes create useful independence, not uncontrolled automation.

02
From the workshop

Turning user requests into platform improvements

The platform captures a request and dispatches the right agent to investigate it, inside a visible, governed workflow.

03
What I’m learning

Designing an organisation of agents

Specialist agents understand demand, design solutions, build, test and review one another’s work.

06 The complete journal

Sixteen days.
From the beginning.

16documented
Build Days