RSV Global / Approach
Architecture first.
AI compresses implementation afterwards.
The workflow, boundaries and acceptance criteria are written down before implementation. That lets AI accelerate delivery without quietly becoming the architect. This page is the long version of how that works, and it is the same standard we apply to our own products.
Five stages, in this order.
The order matters more than any individual stage. Most failures we are asked to review are a stage taken out of sequence.
Understand
Observe the current work, the systems of record, the volume, the edge cases and the consequence of failure. Including the workarounds, because the workarounds are the specification nobody wrote down.
Specify
Define contracts, states, architecture decisions, approval boundaries and evaluation rules. The output is a document you own and can build with anyone.
Build
Implement in bounded increments, with schema changes, tests and migration awareness carried alongside. Each increment ships independently.
Evaluate
Run named checks before consequential external action. Do not replace a red baseline with a weaker test.
Operate
Monitor failure, retain audit trails, maintain credentials, and turn operating history into better evaluations.
The rules behind the work.
Domain first
A good AI system understands the job, its economics and its exceptions. Generic intelligence is not a substitute for operational depth.
Controls outside the model
Money, publication, customer contact and destructive actions need policy and human boundaries the model cannot override.
Operate for change
Models, APIs and interfaces change. We design for monitoring, maintenance, retry, fallback and deliberate replacement.
The model is one component.
Durable value comes from how the system handles state, evidence, permissions, tools, failure and the human decision boundary. These are the parts that decide whether it survives contact with production.
We start with the consequence of being wrong.
Then we decide what the model may research, recommend, write, publish or never touch without another control. Money, publication, customer contact and destructive actions sit outside the model by default, and have to be argued back in rather than assumed. Selecting who to contact and then contacting them unsupervised is not one step, and we do not build it as one.
We let AI agents write production code. Under a written standard.
Most teams are working out how to let AI agents near production without losing control. We run a versioned, harness-agnostic operating standard that governs it, and it is the honest reason the delivery economics work.
- 01Issue as source of truth, one issue per pull request
- 02Canonical verification before review, CI must pass
- 03Red baseline policy, never weaken a test for green CI
- 04Concurrent-work claiming across agents and humans
- 05Provenance trailer recording which harness produced each commit
- 06Explicit safety rules for agent access to infrastructure
What we actually run.
Chosen against the operating constraint rather than a house stack. Three deployment models across three production estates.
The same standard, in four systems.
Show us the workflow everyone has learned to tolerate.
We will help determine whether it needs a focused product, an autonomous operating system, a stronger data layer, a review of what already exists, or a simpler fix that involves no AI at all.