Six Layers Around the Execution Layer
Execution alone does not give a deterministic output. Boundary, context, skills, memory and verification decide whether parallel agents ship work or slop, and a shared verification layer is where most teams stall.
Chapters
- Boundary to delivery
- An engineering process for AI
- Boundary, context and skills
- Memory and decision docs
- Where teams slow down
Transcript
Boundary to delivery
Boundary context skills, execution, verification, and delivery. Today we are going to focus primarily on execution layer, but all six layers combined, you can able to produce a deterministic output. So it is not just about the execution, but it is also about what kind of execution you want, whether a verifiable execution you want, or you want something which is an AI slop. Every time you give some input, AI will produce some output, and if that output is not consistent enough, then over a period of time your organization will struggle in terms of tech devs
An engineering process for AI
and so on. So, how you are going to control that? You need to follow a engineering process around it. It is not new. Actually, engineering processes around around these are defined even before AI era or AI adaption era, where you you use have certain set of processes, you just need to tweak that processes to adapt AI to speed and up instead of doing things sequentially, you can do things more in parallel when you are able to adapt
Boundary, context and skills
that. So if you define your boundary clearly, like what it can execute, what it can't execute, and if you define your context, what is the knowledge? Like this is my knowledge in terms of copywriting, this is my knowledge in terms of my voice in terms of copywriting, this is how my design component looks like, this is how my design token look like, and this is how I create an invoice or whatever functionality it is. If you create clearly create those skills along with the context, then you will be able to execute with more deterministic output because AI will pick those skills and the context and able to do more informed decisions in the execution and it will be able to verify.
Memory and decision docs
And then you need to improve those with a self-improving loop that whether it has a single engineer or multiple engineers who are working in a team environment, your Claude have its own memory, their Claude have its own local memory, and then you need to ingest those for you know company-wide memory. Either you can manage it like a git process, mostly before AI era, how this is managed is like decision docs. You create your decision docs, and then everyone will push whenever there is a PR which is merged, then you will create a decision doc of what change has happened, that change management will act as a memory for yourself. That these are the changes which happened in this logical order, and you will be able to use that for your next iteration.
Where teams slow down
The majority of the place where software development currently failing is this verification layer where everyone in the company will start to do their own processes and every individual use AI in a different way, then collaboratively they are not able to verify what they are producing. So that is what slows them down.
This lesson is cut from the free Lightning session Build Your First Software Factory Execution Harness. The Live Labs build the same system with you, free, on Thursdays: see the Live Labs.