The approach
The model is simple: people, agents, context and governance. If you skip context and governance, you get isolated, short term and fragile results.
Judgement on what to build
The humans own the bookends: what is worth doing, what good looks like, and whether the output can be trusted.
Everyone becomes a manager of agents. That needs judgement, taste and accountability, not just prompt tips.
The AI doing the work
AI agents are set up like capable new hires: clear goals, tools, skills, knowledge, boundaries and feedback.
An agent with no goal, tools, knowledge or boundaries is not automation. It is a new starter joining your business every day needing your attention. To be clear about the word: 'agent' here means bounded AI assistance or automation with defined limits — not an autonomous system left to act on its own. The boundaries are the point.
Shared memory, knowledge
The maintained brain your agents read from and write back to: process, standards, examples, decisions and evidence.
This is the layer most teams skip: a maintained store of process, standards, data, examples and decisions that agents can use repeatedly.
Confidence and trust
Tests, human gates, audit trails and a definition of good. The reason AI can touch real work.
Governance is what makes AI into a business system rather than a demo: tests, approval gates, logs and a definition of good.
Diagnosis before construction
Useful AI starts by finding the workflow where better context, agent help and human checks will genuinely remove work. The diagnosis matters because the tool is rarely the constraint.
The AI Reality Check maps one function against the four layers, names the missing pieces, and gives you a build order that can survive contact with real work.
Next step
Start with the AI Reality Check. Two weeks, clear evidence, and a decision you can stand behind.