No Shared Definition of Done
Data science measures model accuracy in isolation; the business measures operational impact. Without one shared exit criterion, both sides can call the same system 'ready' or 'not ready' at the same time.
Technical Paper · AI Solution Delivery·Engagement
Engineering AI Solutions to a Verifiable Definition of Done
A governed delivery framework for enterprise AI systems that reach production without regression risk · Published August 2026
Executive Summary
Enterprise AI investment has outpaced enterprise AI delivery. Most organizations can produce a working demo in weeks; far fewer can put an AI system into production and trust it under real operating conditions. The gap is rarely the model — it is the absence of a shared, verifiable definition of what 'done' means, and a governance structure that enforces it. This paper describes the delivery framework we use to close that gap: a phase-gated approach — Discover, Design, Build, Validate, Scale — where every phase ends in a named, checkable exit criterion instead of a vague sense of readiness. The result is an AI system that ships with human review built into its lifecycle, observability wired in before go-live, and a rollback path that was tested rather than assumed.
The Business Problem: Why AI Pilots Stall
AI pilots tend to fail in a small number of recognizable ways, and rarely because the underlying model was inaccurate. They fail because the team building the system and the team operating it never agreed on what 'finished' looks like, because an agent was given more autonomy than the business was ready to trust, or because nobody could explain — after the fact — why the system produced a specific output. Each of these is a governance failure, not a modeling failure, and each is preventable with the same discipline used to ship any other production system: named exit criteria, independent review, and a tested path backward.
Autonomy Outpacing Trust
Agents are given the ability to take action — send an email, update a record, place an order — before the organization has a tested way to review, limit, or reverse that action.
No Rollback, No Observability
Teams plan the launch and skip the un-launch. When a model regresses or an agent misfires in production, there is no dashboard to see it and no rehearsed path to back it out.
Solution Architecture: The Governed Delivery Framework
The framework treats an AI system the same way a regulated software system is treated: as something that moves through named phases, each with an owner, an artifact, and an exit gate that a second party — not the build team — signs off on. Nothing advances to the next phase on informal confidence; it advances because a specific, written condition was met and someone independent verified it.
DiscoverProblem & data audit
DesignArchitecture & DoD
BuildIterative delivery
ValidateIndependent QA gate
ScaleMonitored rollout
The Challenge
Agent-based systems are often built with implicit trust: once an agent can call a tool, it is assumed safe to let it call that tool in production, in any situation, without a human in the loop.
Our Approach
Every action-capable agent ships with a scoped permission boundary, a human-review queue for anything outside pre-approved patterns, and a kill switch that has been exercised in a rehearsal — not just documented.
Delivery Approach
PHASE 0
Discover
Problem framing, data readiness audit, and success criteria agreed in writing.
PHASE 1
Design
Architecture, guardrails, and the Definition of Done are drafted and signed off before build starts.
PHASE 2
Build
Iterative delivery against the design, with review checkpoints at every increment.
PHASE 3
Validate
Independent QA — a team outside the build team — verifies the Definition of Done against evidence.
PHASE 4
Scale
Monitored rollout with an exercised rollback path and a defined ownership handoff.
Governance & Risk Controls
Named, written Definition of Done for every phase
Independent QA sign-off, separate from the build team
Human approval required before any autonomous action ships
Model and data lineage logged for every material decision
Role-based access and least-privilege service identities
Rollback path tested before go-live, not just documented
Production monitoring and alerting wired in before cutover
Incident runbook rehearsed with the operating team prior to launch
Illustrative Scenario
Composite scenario — not a single disclosed engagement
A multi-site outpatient healthcare network was running six separate clinical-documentation assistants, one per clinic, each configured differently and none reviewed by the same governance process. Applying this framework, the assistants were consolidated behind a single governed interface: one Definition of Done covering clinical accuracy and note-taking latency, one independent review pass before any clinic went live, and mandatory clinician sign-off retained on every generated note. The rollback path — reverting a clinic to manual documentation — was tested before the first clinic launched, not after the first incident.
Technical Appendix
In practice, the framework is implemented with a small set of recurring architectural patterns regardless of the underlying model provider: retrieval and generation are isolated into separate, independently testable stages so a retrieval regression can be diagnosed without re-evaluating the model; agent tool-calling is scoped to an explicit allow-list rather than an open action space; every model or prompt change runs through a fixed evaluation harness before it can reach the Validate gate; and production rollout follows a canary pattern — a small, monitored slice of traffic first — with automatic rollback triggers tied to the same metrics used in the Validate gate, so 'ready for production' and 'safe in production' are measured the same way.
In Scope
Discovery and architecture design, phase-gated build and independent validation, production rollout with monitoring and a tested rollback path, and a defined operating handoff.
Out of Scope
Selecting or licensing a specific model provider on the client's behalf, and any unsupervised autonomous action outside a pre-approved, human-reviewable pattern.
Reaching Definition of Done is a discipline, not a milestone.
A phase-gated framework with named exit criteria turns 'we think it's ready' into 'we can show you it's ready.' That is the difference between a demo and a production system.
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