The Production Agent Framework.
The methodology that separates the 12% of AI agents that reach production from the 88% that don't. Four phases, fixed gates, and evaluation wired in before the first line of agent code. Wedge · Build · Run Alongside · Ship.
Twelve weeks, worst case, from wedge to compounding.
Most teams build first and measure later. We invert that: the metric and the eval harness come before any agent code, so every phase is judged against a number agreed up front — not a demo.
Wedge & instrument · wk 1–2
Name the single workflow. Define the ROI metric the business already tracks. Stand up the evaluation harness — a scored test set with ground truth — before any agent code is written. If we cannot measure "good enough" objectively, we are not ready to build.
Build the spine · wk 3–4
Ingestion, retrieval, orchestration, tools, and human-in-the-loop, assembled against your real systems and data. The eval gate runs on every change, so no commit ships without proof it held or improved the score. Quality is measured from the first day, not the last.
Alongside & harden · wk 5–10
Run the agent alongside your team against live work — it sees real traffic and makes real calls, but a human still owns the outcome. Tune the edge cases the eval set surfaces. Lock the audit trails, permissions, and drift alarms that make the system defensible before it carries any weight.
Ship & compound · wk 11–12
Cut over to production with monitoring live. Report a monthly scorecard against the agreed ROI number — quality, impact, coverage. Then queue the next adjacent workflow, because the hard infrastructure is now built and the second agent is far cheaper than the first.
What a demo skips and production demands.
The gap is not intelligence — it is the operational discipline that lets a business depend on an agent without a human watching it. Four disciplines carry the weight.
Evaluation frameworks
A scored test set, graded against ground truth, that defines "good enough" as a number — and runs as a gate on every change so shipping is a decision backed by data, not a vibe.
Drift monitoring
Continuous checks on live quality, so a silent regression surfaces in hours instead of the quarter it takes for a user to complain.
Audit trails
Every decision the agent makes is logged, attributable, and defensible — the precondition for using one anywhere a regulator or reviewer can ask why.
Human-in-the-loop
Clear escalation, review queues, and override controls, so a person owns every edge case the model should not — which is what lets the business trust the automated path.
Evals on day one is the line between the 12% that ship and the 88% that do not.
Want the framework as a PDF?
The written version is on this page in full. We are packaging the working artifacts — the eval-harness spec, the drift and audit checklists, and the scorecard template. Leave your email and we'll send them when they ship.
The Production Agent Framework
The framework is the how. Here is the rest.
This methodology is what we run inside a paid engagement. If you want the wider picture or proof it holds up under scrutiny, three places to go next.
What we do
How we pick the wedge workflow, price against outcomes, and refuse to ship a pilot that no one is allowed to depend on.
Learn moreProduction Agent Sprint
The framework as a fixed-scope engagement: one workflow, in production, in under 90 days, priced against the number it moves.
Learn moreTrust & security
How the audit trails, permissions, and drift alarms hold up to a regulator, an auditor, or your own internal review.
Learn moreBring one workflow. We'll run the framework.
Name a painful, high-volume process and a number you want to move. We'll tell you in the reply whether it's a clean first wedge.