Patterns

AI agents with ordered toolsets and limits fixed in code, so what they can do doesn't rest on a prompt.

An AI assistant that can never see more than the person asking it.

Let people ask an AI agent about your data, with your database or data lake safe from whatever the model sends.

An MCP server for a multi-tenant platform, where no prompt can move a connection from one client's data to another's.

People sign in to your MCP server with the logins they already have, and the token opens nothing else.

A remote MCP server with no servers to manage, which costs nothing when idle.

One MCP server for the AI tools your people use and the agentic systems you build, with the right identity and controls for each.

One shared library of instructions that keeps AI coding agents to your standards, across every repo and every tool.

AI coding agents at every phase of your SDLC, with the decisions at each one remaining with a human.

AI agents that work to your organisation's idea of engineering best practice, whatever that looks like, and can say why.

One login for every tenant on your platform, where access rests on group membership rather than a value a user can change.

One API for every client on your platform, where each request is checked against the caller's tenant, role and features in one shared place.

Each client's relational data kept apart by the platform itself, with its own storage, compute, backups and credentials.

Fully automated client onboarding, in the hands of the people who operate your platform, with no code written and no wait on a platform team.

One web app for every client on your platform, showing each only what they've bought, from one build promoted through every environment.

A record of who changed what on your platform, which nobody can alter afterwards, including the services that made the changes.

One record per client as the single source of truth for your platform, so adding a client is a data change rather than a code change.

A pipeline that processes every file as it arrives, so one bad file never holds up the rest, and anyone can see where each file has got to.

A data lake that keeps every file exactly as it arrived, and can rebuild everything derived from it whenever the processing changes.

One governed way for people, services and AI agents to query your data lake, where every query is checked and every result stays inside your platform.

Infrastructure shifted left to the teams that use it: they provision it by editing data, while your specialists maintain the patterns behind it.

Data-driven Terraform whose data tier is derived from a system outside the repository, such as a database people manage through a graphical interface, so they manage infrastructure without editing code or data structures.

Data-driven Terraform applied to an AWS Organization, so accounts, guardrails and access change by pull request, and the code is the record of how the organisation is set up.

Data-driven Terraform applied to GitHub as two related concerns, so administrators manage the organisation as data, and anyone can get a properly set-up repository by pull request.

CI/CD run as a platform rather than a collection of pipelines, so your standards are enforced in one place and teams build their own pipelines from ready-made parts.

Every pipeline reaches every AWS account through one trusted entry point, so pipelines hold no long-lived keys, and a new repository or account deploys with no new trust to set up.

Your own up-to-date, queryable copy of another system's state, such as a CI/CD platform or an issue tracker, kept fresh by events and complete by a regular sweep.