Approach
Most companies do not need another AI transformation consultancy. They need someone to find the right processes, decide honestly among the available architectures, and design the first loop that actually works.
Compounding agent loops
A compounding agent loop is a supervised AI workflow, wrapped in a durable harness of evals, traces, feedback capture, escalation, and permissions, that gets better because it is used.
Basic automation runs the same way every time until someone rebuilds it. A compounding loop captures corrections, exceptions, and outcomes, and feeds them back into the system that produced them. The goal on day one is never full autonomy — it is a loop with clear boundaries, measurable outputs, a real human escalation point, and a mechanism that makes it better next month than it is today.
Three scores, not one
Every opportunity is scored against the same instrument, and the three scores are kept separate on purpose — collapsing them into a single number hides exactly the information a decision needs.
Saturation Readiness
Is a cheaper model plus harness already good enough for this task?
Twelve dimensions — repetitiveness, evaluability, error tolerance, quality-threshold flatness, and more — scored and weighted, with hard caps when a task cannot be measured or its errors cannot be recovered.
Economic Priority
Even if this task is fully automatable, is it worth anyone's engineering time?
Value at stake, revenue proximity, customer experience impact, and scalability leverage. A perfectly automatable task nobody does very often deserves no engineering time.
Harness Durability
Does the investment survive the next model upgrade, or get erased by it?
A harness that encodes business logic, data integrations, evals, and permissions gets more valuable as models improve. A harness that patches over model weakness gets deleted.
Five gates sit above the math — minimum quality, error severity, evaluation confidence, distribution drift, and hidden failure risk. A task that fails a gate does not get automated, no matter how good its numbers look. The score describes what is possible; the gate describes what is not allowed, and the gate always wins.
The engagement loop
- 01Find the work — reconnaissance that recovers how work actually happens
- 02Size it honestly — scoring that decides where automation earns its keep
- 03Match the architecture — platform, vertical product, automation layer, custom build, or nothing yet
- 04Design the blueprint — objectives, evals, escalation, permissions, ownership
- 05Prove the prototype — evidence before budget, not opinion
- 06Run LoopOps — the discipline that keeps a live loop improving instead of drifting
LoopOps
DevOps manages software delivery. RevOps manages revenue systems. LoopOps manages what happens to an agentic workflow after it goes live — and it is where most deployed loops quietly die. Left alone, a live loop drifts, and the first sign anyone gets is usually a customer complaint. LoopOps is the practice of watching the evals, reading the traces, catching drift early, tuning routing as cheaper models become viable, and tracking unit economics well enough to know whether the loop is paying for itself.