One generation of the web had LAMP.
Then we had MEAN.
Then the JAMstack.
AI might be the biggest shift in platform ever.
LAMP - Linux, Apache, MySQL, PHP - was memorable shorthand for a widely used stack. Then MEAN. Then JAMstack - JavaScript, APIs, Markup.
The acronyms exist to give us a shared reference point. LAMP and MEAN defined specific tiers, and how they fit together. We’ve used them as a starting point, as a skillset to hire against, and they’re what we build on.
JAMstack started to define functions as well as purely technology bundles.
AI doesn’t have a stack yet.
It doesn’t have to be sequential. Like JAMstack, it defines the layers the system needs.
Here’s one.
SCAMP. Sources, Context, Artifacts, Models, Proof.
The tiers
Sources
This is where the raw material lives. The codebase. Brand guidelines. Confluence. Documents.
Enterprise is comfortable with this tier because it’s familiar. It looks like data, and enterprises know how to manage and govern data. That’s who can see it, who can’t, and where it’s stored.
For AI the trouble starts the moment the sources start to disagree. AI treats them all as confident, and none of them talks to the others.
Context
The instruction tier. That’s not the data, it’s the guidance on how the agents should use the data. It’s easy to skip, because if it isn’t a source then it looks like overhead.
This isn’t just prompting. It’s durable knowledge that tells your machines what your organization has defined as correct.
It might be the most valuable tier in the stack.
Artifacts
AI output. What it generates. That might be a component. Or an email. Or code.
Models
Claude, Gemini, GPT. And agentic environments and tools that invoke them: Cursor, Copilot, Claude Code, and whatever comes next.
It’s the layer everyone is excited about. It’s also one that might commoditize really quickly. You are very likely to want to change your model. Much more likely than wanting to swap out your standards.
Proof
The tier almost nobody is building. It’s critical for the rest of the stack. You can build and govern all the other four tiers and still ship the wrong thing.
Creating that proof
At Knapsack we evaluated coding agents working with design-system tasks. Give them a matched task set. Two conditions: one where it had context how to use the system. The other it didn’t.
We were surprised by how quickly the agent found the design system - either way. Discovery wasn’t a problem - it knows to check node_modules. It knows to import components and tokens.
But there was a huge difference in the agents understanding of how the organization expected the components to be used.
With context, design fidelity improved by ~10%. Code quality by more than 20%. The number of prompts that produced shippable code went from 40% to over 60%. One single run cost more - but the cost per shippable output dropped by a third.
Access wasn’t the problem. What we solved for was conformance.
Artifacts are easy, and that’s a problem.
It might take minutes to make an artifact. A half-hour to review it. Another hour to find all the deviations. And then who knows how long to fix all of that.
If you have AI without conformance, you just move the costs. It’s not creation, it’s correction - and correction is still mostly done by people. Faster generation can make the system slower if every artifact creates a human correction cost.
You want to be paying for “good”. And what’s good isn’t actually a property of the model.
The critical fifth tier
A four-tier stack will fail this test.
An authorized employee signs in. They’re on an approved tool. They have the right permissions, the model is locked down, and all of their actions are logged.
Nothing leaks. There are no security breaches. The governance team is happy.
AI makes a very plausible artifact. It’s wrong - but not obviously. It doesn’t match the organization’s own standards. Perhaps it violates an accessibility rule, or ignores a code convention.
It doesn’t fail any tests. It looks plausible. It passes governance controls. So it ships.
The four tier stack governs who can ask the questions. It isn’t checking whether the answer you get back is right or not.
That’s why Proof is so important. This is more than disclosure (that just says AI was involved). It includes provenance, but that’s not the whole story. Provenance tells you where something came from. Proof tells you if the output satisfies the constraints you care about.
If a regulator, or a customer, comes back to your work, they’ll want to know the decisions the AI made, the constraints that were applied, and whether it followed those standards.
A self improving loop
Proof isn’t only a gate at the end. While it can close a loop, it’s also how the stack learns.
Sources → Context → Models → Artifacts → Proof → Context
When an artifact generation fails, the question isn’t just “how do we fix it?” We need to know if the Context was insufficient, contradictory or impossible to follow.
Proof detects drift. Then we use that evidence to improve Context. Our next generation uses those constraints to build better.
If you take Proof out the stack, then all you’re left with is a SCAM.
Naming the stack helps you place yourself in it
Knapsack does two things in the stack. And we’re increasingly defining our output in another layer.
We provide Context. The tier that tells a model not just “components are here”, but also how the organization expects them to be used.
We provide Proof. The eval above, the one with the numbers. That’s Proof in action - determinantive testing on whether AI was actually working. Definitively measured against your standard, with the differences in output logged. Evidence that AI output conforms to what you decided.
And we’re increasingly working to create some specifically defined Artifacts. The shape of some outputs so that they adhere to rules and standards.
The Models do the generation. They reference Sources. The Context tells them what “right” looks like. They create an Artifact. And the Proof shows whether the Artifact was what was asked for.
That’s a control plane for AI-generated product decisions. It should be the stack that we’re building in.
Five tiers.
SCAMP.
Further reading:
Your AI governance stack only answers half the problem - on moving from access management to conformance.
Patel, R. The Evolution of Full Stack Development: From LAMP to JAMstack. Medium, Jul 2024.
Budwell, G. These 70 Companies Are Building the Core Infrastructure for Artificial Intelligence (AI). Through the lens of probability, Nov 2025.
Article photo by Markus Winkler on Unsplash.