How do we build a reusable internal AI platform?
AI harness & proprietary platform engineering
An AI harness is a structured platform layer that connects models, tools, workflows, business context, permissions, and vertical-specific use cases into one coherent operating layer. Building one gives an organisation reusable internal capability rather than a collection of one-off agents and fragmented experiments. It is built incrementally alongside real use cases — the shared layers are extracted from working software, not designed in advance of it.
What gets built
A harness is the set of layers every AI use case in the organisation draws on. Built once, governed centrally, and inherited by everything downstream.
- Shared operating layer
- The substrate each use case is built on, so the second and third cost a fraction of the first.
- Agent and skill framework
- Reusable capabilities that compose, rather than a separate bespoke agent per department.
- Tool and model routing
- Which model handles which step, which tools it may call, and how the system degrades when one is slow or unavailable.
- Governance and permissions
- What each agent may read and write, on whose behalf, under which approvals — enforced by the platform, not reimplemented per team.
- Monitoring and feedback loops
- What ran, what it produced, whether it was right, and a route for corrections to improve the system.
Built alongside use cases, not before them
Platform projects that precede real usage produce abstractions the business never needed. The route that works is to deliver one production use case properly, then extract the shared layers as the second and third are built.
That sequencing keeps the platform honest: every layer in it exists because a real workflow demanded it.
What it is not
It is not an agent framework — that is a library you might use inside a harness. It is not a model subscription, which gives individuals a better tool but gives the organisation no capability. And it is not a product you install, because the routing, context and permission model encode decisions specific to your business.
What you get
- Shared operating layer and platform architecture
- Agent and skill framework
- Tool and model routing
- Governance and permission model
- Monitoring, evaluation and feedback loops
- Documentation for the teams building on top
Start here if
- A third AI use case is being scoped and the first two shared nothing
- Each new use case costs as much as the last
- No consistent answer to governance questions across AI projects
- Teams are reimplementing the same integrations and permissions
Platform architecture · Orchestration · Multi-vertical · Rollout models
Does a harness lock us into one model provider?
The opposite, when it is built correctly. Routing is one of the layers the harness owns, which makes model choice a configuration decision rather than an architectural one. Mixing or swapping providers per task is a property you gain from having a harness, not something you trade away.
Can we build this on top of an existing agent framework?
Usually, and often you should. What a framework cannot supply is your business context, permission model and evaluation suites — the part that makes the platform yours.
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Last reviewed · Tayyeb Mahmud, 1AYM