Three phases for moving AI agents
from concept to durable operation.
A practitioner's playbook for understanding the workflow, building agents that fit, and operationalizing what gets built. Assess, Integrate, Scale.
Map your operations. Identify where AI agents carry real weight.
The work starts with conversations — your team showing us how things actually happen, not how the org chart says they happen. From there, we document the workflows that matter, score them on effort and impact, and identify the integration map: which existing tools the agents will touch, what data they will see, what guardrails apply. The output isn't a slide deck. It's a written plan you could hand to another firm and they would know what we recommend and why.
Deliverables
- A written workflow map for the parts of your business that AI agents would touch
- A prioritized list of agent-suitable workflows with effort/value scoring
- An integration map for existing tools
- A phase-one recommendation with scope and approach
Also offered as a standalone AI Readiness Assessment for firms that want a clear written plan before committing to a full engagement.
Build and deploy what we identified.
Build means real working agents — not prototypes, not demos. They handle the workflow they were designed for, integrate with the tools your team already uses, and come with the runbooks needed to operate them. Where appropriate, we work with specialist subcontractors on specific build tasks; the founder leads strategy and integration design throughout. Iteration happens against your real workflow, not against artificial test data.
Deliverables
- Working agents in production
- Integration documentation
- Runbooks for routine operation
- Client-team training so your operators can use, observe, and adjust the agents
From working to durable.
This is where most AI projects fail — the agents work in the demo, then quietly degrade over the first few months because nobody owns them. Scale is about making the agents an operational asset your team can monitor, refine, and expand on. We set up the monitoring patterns appropriate to the scope, refine runbooks against real-world conditions, and identify the next workflows worth automating. The engagement either concludes here or transitions to ongoing support.
Deliverables
- Monitoring patterns appropriate to the scope
- Refined runbooks based on real-world behavior
- A written recommendation for next-phase expansion
Convictions shape the work.
- Start with workflows, not tools. Operations come first; agents are means, not ends.
- Reliability over novelty. A boring, robust agent that runs for five years beats a clever one that breaks in three months.
- The client owns the system at the end. Documentation, training, and architectural clarity are deliverables, not afterthoughts.
- Document everything. Tribal knowledge is the antithesis of durable AI operations.
- Produce outcomes. Payroll math, capacity created, time recovered — not AI maturity scores or model architectures.
Ready to move?
Book a 20-minute discovery call and we'll talk through your operation.
Book a 20-minute discovery call