Buyer response packet
Assemble approved organization, program, product, and policy answers into a buyer-ready packet while the right owners approve anything customer-facing. AI may not invent posture or send buyer responses.
Agentic Workflow Design Sprint
Build one workflow where AI learns from expert work.
The sprint is Verdify's build path for a private improvement environment: a scoped workflow where AI can read, summarize, classify, draft, recommend, or route while people, policies, systems, feedback, and outcomes remain authoritative.
Run a SprintEvidence from the lab
Evidence from the lab: useful AI has a role inside a loop, not unrestricted authority.
In the greenhouse, the AI planning system can write tactical intent through approved paths; the ESP32 still owns physical enforcement. A client workflow should follow the same discipline: narrow AI capability, explicit control layers, logged outcomes, and a clear feedback path.
Download the BlueprintWhat transfers to a sprint
Build one narrow workflow path instead of a broad autonomous system.
Keep irreversible, customer-facing, regulated, or system-of-record actions outside AI authority unless explicitly approved and reversible.
Test prohibited-action cases, approval paths, logs, private evals, and outcome metrics before rollout.
Candidate workflows
A sprint should build one learning workflow, not a grab bag of AI ideas.
These are good sprint candidates when source access, review ownership, prohibited actions, feedback capture, and outcome metrics are clear enough to test.
Pilot evidence pack
Turn pilot data, assumptions, safety notes, deployment plans, and buyer questions into a review-ready packet without letting AI overstate results, certify savings, or make customer claims.
Support-ticket triage
Sort, summarize, and draft internal handoff prep while support owners approve external messages and closure.
Quality document assembly
Read approved sources, draft evidence packets, flag missing records, and preserve required review authority.
Retailer compliance drafting
Summarize claims, chargebacks, retailer context, and response options without making account commitments.
Service exception review
Group work orders, alerts, and notes so reviewers can decide what requires action.
Sprint structure
The sprint moves from controls to working learning signal.
The output is a narrow workflow that can be inspected, tested, handed off, and either expanded or stopped based on private eval and outcome results.
Week 1
Confirm workflow, expertise map, source access, acceptance criteria, action limits, and approval design.
Week 2
Build the first path: read, classify, draft, recommend, route, capture review, or prepare evidence.
Week 3
Run private eval cases, capture failures, tune prompts or workflow logic, and define logs.
Week 4
Review outcome scorecard, handoff notes, rollout risks, and the expand / hold / stop decision.
Before build
The feedback design is part of the scope.
An agentic workflow does not start by asking what AI can do. It starts by deciding what the organization should learn from each expert review and outcome.
What AI can read from approved systems and documents.
What AI can draft for internal or reviewer-facing use.
What AI can recommend and who approves exceptions.
What AI can execute, if anything, and whether that action is reversible and approved.
What AI must never touch in the first implementation.
What gets logged and which private eval or outcome metric determines expansion.
A sprint works when one workflow is ready to become a learning loop.
Good fit when
You already know the workflow to test.
The workflow has repeatable inputs and reviewable outputs.
A human or system of record can remain authoritative.
Success can be measured with a simple private eval and outcome scorecard.
Not a fit when
You want a broad enterprise AI platform before choosing a workflow.
You want AI to take unapproved irreversible action.
The source systems are inaccessible or undefined.
There is no owner for review, exceptions, or rollout.
FAQ
Common buyer questions.
What makes the sprint strategic?
The sprint defines the workflow as a private improvement environment: what AI may read, draft, recommend, execute, and never touch; where human judgment enters; what gets logged; and which outcomes become learning signal.
Will the sprint ship production software?
The sprint builds a narrow working workflow or implementation-ready agentic workflow. Production rollout depends on risk class, systems access, security review, private eval evidence, and feedback-loop design.
What actions should usually stay out of the first sprint?
Irreversible, regulated, customer-facing, unsafe, or high-consequence actions should remain prohibited until the workflow has stronger controls and evidence.