The models got better.
Now we have to.
2025 was the year models stopped being the bottleneck. Teams are. If you can’t define work for an agent, someone else will—and they’ll ship faster.
Domain Expertise isn't just knowing facts; it's the ability to define Reliability, Safety, and Auditability in a way a machine can execute.
Reliability
Follows procedure, not vibes. We move from "Prompt Engineering" to "Process Engineering".
Safety
Permissions and contracts by default. No shadow IT. The agent only touches what you explicitly map in the Source File.
Auditability
Every action explained and logged. When the CEO asks "Why did the agent do that?", you have a trace, not a guess.
From Prompt to Procedure
You don't need to write code to build agents. You need to write definitions of done.
- Old Way: "Write a blog post about AI."
- New Way: "Execute the Content Procedure: inputs=[transcript], rules=[style_guide_v2], output=[markdown]."
The Output
By defining the rules clearly, you move from managing tasks to managing systems. This allows you to scale your expertise without cloning yourself.
The Transformation Model
How we turn judgment into code.
The Source of Truth
To build true domain-aware systems, expertise must live somewhere concrete. We call this the Domain Source File.
- Visible | Inspectable logic (no hidden prompts)
- Structured | Priorities and tradeoffs are explicit
- Routable | Branching based on specific triggers
# DOMAIN SOURCE FILE intent: "Critical Rerouting" priorities: - strict: "Cold Chain Integrity" - flexible: "Standard Delivery" tradeoffs: IF: "Delay > 4h" AND: "Cargo is perishable" THEN: "Air freight override"
You are not teaching the AI facts.
You are teaching it how to think inside your domain. If you know how your system behaves under pressure—you have the code.