Agentic AI Sprint Factory

The Problem This Service Solves

Most enterprises recognise the potential of agentic AI but face a chicken-and-egg problem: governance teams have nothing to govern, and delivery teams are told they cannot deploy without governance. The result is paralysis, or worse, ungoverned shadow AI deployed by individual teams who got tired of waiting.

The Sprint Factory breaks the deadlock. It uses an agile, sprint-based methodology to design, build, and deploy a single governed AI agent use case in 4 to 6 weeks, with full governance baked in from Day 1. The first use case becomes the proof point that justifies the broader governance investment, the CoE charter, and the enterprise roadmap.

This is not a proof-of-concept that gets thrown away. Every Sprint Factory engagement produces a production-grade agent operating under Corvair's Architecture-First governance controls, measured to Six Sigma standards, and aligned to applicable regulatory frameworks.

Service Options

Option A: Discovery + Build (8 weeks)

For organisations that have not yet identified or prioritised their agentic AI use cases.

Sprint 0: Discovery & Roadmap (2 weeks)
Sprints 1 to 3: Build & Deploy (4 to 6 weeks)

Option B: Build Only (4 to 6 weeks)

For organisations that already have a prioritised use case and want to move straight to delivery.

Sprints 1 to 3: Build & Deploy (4 to 6 weeks)

Sprint 0: Discovery & Roadmap (2 Weeks)

Build an ROI-prioritised agentic AI roadmap and select the first use case for Sprint Factory delivery.

Week 1: Landscape & Opportunity Mapping

Interview 8 to 12 stakeholders across operations, risk, compliance, IT, and business lines
Map the current process landscape: coordination overhead, manual handoffs, data gathering, rework
Identify candidate use cases (typically 15 to 30 emerge from interviews)
Catalogue existing data assets, system integrations, and fallback processes

Week 2: Prioritisation & Selection

Score every candidate against the Use Case Selection Framework
Rank by composite score; present the top 5 to 8 to the steering committee
Select the first use case by consensus
Produce the Sprint Factory Roadmap: 3 to 5 use cases over 6 to 12 months

Sprint 0 Deliverables

Agentic AI Opportunity Register: Full catalogue of identified use cases with owner, process area, and preliminary scoring
Sprint Factory Roadmap: Sequenced, ROI-prioritised pipeline of 3 to 5 use cases with recommended sprint timelines and dependencies
Use Case #1 Brief: One-page specification covering problem statement, target audience, data sources, integration points, success criteria, fallback process, and governance requirements

Use Case Selection Framework

Every candidate use case is scored on two axes: Impact and Feasibility. The composite score determines priority. The framework is deliberately designed to select use cases that will succeed, because the first agent must succeed to unlock everything that follows.

Impact Score (weighted 40%)

Criterion	Weight	Scoring
Time Recaptured	15%	Hours per week of knowledge worker time currently consumed by the manual version of this process. Higher is better.
Error Reduction	10%	Current failure rate, rework rate, or exception rate of the manual process. Processes with known, measurable failure modes score highest.
Cost Avoidance	10%	Direct cost savings: FTE time, vendor costs, penalty avoidance, or revenue leakage eliminated.
Strategic Leverage	5%	Does success in this use case unlock downstream use cases, build organisational capability, or create reusable components?

Feasibility Score (weighted 60%)

Feasibility is weighted more heavily than impact because the first use case must succeed. A high-impact use case that fails damages the entire programme.

Criterion	Weight	Scoring
Data Availability	15%	Is the required data already accessible, structured, and of acceptable quality? Or does it require new integrations, cleaning, or permissions?
Process Clarity	15%	Is the current process well-documented and well-understood? Are the decision rules explicit, or buried in tribal knowledge? Processes with clear, documented SOPs score highest.
Ease of Implementation	10%	Technical complexity: number of system integrations, API maturity, authentication complexity, data volume. Fewer dependencies mean a higher score.
Fallback Process Available	10%	Can the organisation revert to the manual process instantly if the agent fails? A robust fallback eliminates deployment risk. This is non-negotiable for the first use case.
Audience (Internal vs. External)	10%	Internal-audience use cases score higher for the first deployment. Customer-facing use cases carry reputational risk that should not burden the proof-of-concept.

Selection Principles

The first use case should be non-controversial. It should be something everyone agrees is tedious, error-prone, and low-value for the humans currently doing it. Nobody should feel threatened by its automation; they should feel relieved.
Target repetitive work or known failure points. The ideal first agent automates a process that is performed frequently, follows a generally predictable path, has a known and measurable failure or rework rate, and consumes skilled human time on unskilled activities.
Exception-handling processes are strong candidates provided the remediation path is generally well-known. If 80% of exceptions follow 3 to 4 standard resolution paths, an agent can handle the triage, data gathering, and routing while escalating the genuinely novel 20% to humans.
Internal audience first, customer-facing second. Internal users are more forgiving of early-stage behaviour, provide better feedback, and do not create reputational risk.
Avoid politically charged processes. If a process is the subject of an ongoing reorganisation, turf war, or strategic disagreement, it is a poor first use case regardless of its ROI score. Choose something boring. Boring is good.

Example Use Cases That Score Well

Use Case	Why It Scores Well
Regulatory change monitoring & impact triage	High volume of regulatory updates; structured inputs (publications, circulars); well-understood triage logic; internal audience only; existing manual process is time-consuming and error-prone
Internal IT service desk triage & routing	High ticket volume; repetitive classification; well-documented resolution paths; instant fallback (human agent takes over); internal audience
Compliance exception report generation	Repetitive data gathering from multiple systems; templated output format; clear rules for escalation; internal audience; currently consumes 4 to 8 hours per report
Vendor risk assessment pre-screening	Structured questionnaire inputs; checklist-based evaluation; well-defined scoring criteria; human reviewer makes final decision; internal audience
Meeting action item extraction & follow-up tracking	High frequency (daily); simple NLP task; structured output; internal audience; low governance risk; immediate productivity gain
Data quality exception triage	High volume of data quality alerts; 80% follow known resolution patterns; structured inputs; internal data team audience; measurable reduction in resolution time

Sprints 1 to 3: Build & Deploy

Sprint 1: Foundation (2 weeks)

Register the agent in the Agentic Registry with declared capabilities and blast radius boundaries
Define agent identity (cryptographic, auditable, non-repudiable)
Map the use case to applicable regulatory requirements
Connect to data sources; validate quality and access permissions
Build the semantic entitlement layer (RBAC/ABAC)
Decompose the process into atomic, verifiable tasks
Define the DMAIC baseline metrics

Sprint 2: Build & Test (2 weeks)

Build the agent workflow: prompt chains, tool integrations, orchestration logic
Implement reasoning assurance: Decision Validity Warrants, Composable Lenses, SCAR scoring
Build Human-in-the-Loop (HITL) checkpoints
Functional testing against known-good cases
Edge case, exception, and adversarial testing
Regression testing against the manual process
Pre-deployment compliance review and kill switch validation

Sprint 3: Deploy & Measure (2 weeks)

Deploy to a limited pilot group (5 to 15 internal users)
Graduated release: shadow, then supervised, then autonomous
Collect DMAIC baseline data: accuracy, throughput, exception rate, time-to-resolution
Calculate initial process sigma against Six Sigma targets
Measure coordination tax reduction
Hand over operational responsibility to the designated internal team
Conduct Sprint Retrospective and recommend the next use case

What You Get

From Sprint 0 (if included)

Agentic AI Opportunity Register

Comprehensive catalogue of 15 to 30 candidate use cases with owner, process area, and preliminary scoring.

Sprint Factory Roadmap

Sequenced pipeline of 3 to 5 prioritised use cases over 6 to 12 months with recommended sprint timelines and dependencies.

Use Case #1 Brief

Detailed specification for the selected first use case covering problem statement, target audience, data sources, integration points, success criteria, fallback process, and governance requirements.

From Sprints 1 to 3

A Governed, Production AI Agent

Not a prototype. A real agent processing real work under real governance.

Agent Governance Pack

Registry entry, identity credentials, blast radius model, regulatory mapping, kill switch configuration, and compliance checklist.

DMAIC Baseline Report

Statistical performance baselines with Six Sigma process control metrics.

Agent Operations Runbook

Operational procedures for monitoring, escalation, drift detection, and retraining.

Coordination Tax Impact Assessment

Quantified measurement of human time recaptured, error reduction, and cost avoidance.

Sprint Retrospective & Next Use Case Recommendation

Lessons learned and a recommendation for the next Sprint Factory cycle.

How It's Different

This is not a strategy engagement that produces a report. In 6 weeks, your organisation has a governed agent in production, measured to Six Sigma standards, with a clear operational handover. No other firm combines Lean Six Sigma process control with patent-pending agentic AI governance architecture and hands-on agent delivery in a single sprint-based engagement.

The methodology treats every agent deployment as a quality-controlled manufacturing process: define the process, measure the baseline, analyse the defects, improve the design, control the output.

The Sprint Factory is also designed to be repeatable. Each cycle builds governance capability. The registry grows, the entitlement layer expands, the DMAIC baselines accumulate, and the organisation's governance muscle strengthens. By the third or fourth cycle, the CoE is effectively bootstrapped from the bottom up.

Engagement Details

Sprint 0 (optional): 2 weeks, 8 advisory days. Discovery, stakeholder interviews, roadmap, use case selection. Sprints 1 to 3: 6 weeks, 18 to 20 advisory days. Foundation, build, test, deploy, measure. Full engagement: 8 weeks, 26 to 28 advisory days.
Delivered personally by Christopher Jackson. Senior-only, no junior analysts, no delegation.
Pairs naturally with the CoE service. The Sprint Factory produces the first governed agent and governance baseline; the CoE service then scales the operating model across the enterprise.
Measurable outcomes. Every engagement includes DMAIC baselines, Six Sigma process control metrics, and a Coordination Tax Impact Assessment.
Repeatable. Subsequent Sprint Factory cycles for additional use cases are available at reduced scope (no Sprint 0, reuse existing governance infrastructure), typically 4 weeks per use case.

How It Connects to Other Corvair Services

Service	Relationship
MAS AIRG Readiness Assessment	The assessment identifies governance gaps; the Sprint Factory demonstrates how those gaps are closed in practice through a real deployment.
AI Governance Framework Design	The framework provides the policy architecture; the Sprint Factory instantiates it for a specific use case.
Agentic AI Risk & Controls Workshop	The workshop educates teams on agentic AI risk; the Sprint Factory gives them hands-on experience governing a real agent.
Fractional AI Governance Advisor	The fractional advisor provides ongoing oversight; the Sprint Factory accelerates the first deployment that the advisor will then govern.
Data & AI CoE for Agentic AI	The Sprint Factory bootstraps governance capability from the bottom up; the CoE service scales it from the top down. They are complementary entry points for different organisational maturity levels.