Purple Orange AI
Short version
An AI workflow evidence packet is the proof layer between a promising workflow and a bigger commitment. It collects the records, outputs, edits, failures, costs, and owner decisions needed to decide whether the workflow deserves production access, more users, more systems, or more money.
The packet should be short enough for a founder to read and specific enough for a technical owner to reproduce. If the team cannot build the packet, the workflow is not ready for more authority.
This is the missing artifact in many AI rollouts. Teams run a demo, get a few good outputs, then make a vague leap toward production. The problem is not enthusiasm. The problem is that the next decision has no durable evidence.
Use the packet after an AI workflow audit brief, shadow-mode run, QA pass, or pilot. It turns scattered examples into a launch decision: cleanup, reviewable agent, sprint, operations buildout, infrastructure, or no launch.
Why the packet matters
Founders and operators do not need another AI status meeting. They need to know whether the workflow is safe and useful enough to receive the next unit of authority: a write permission, a customer-facing step, a larger budget, a second team, or a broader tool surface.
The evidence packet forces five questions before that authority expands:
- What real cases were tested? Include normal, edge, failed, and rejected examples from actual source systems.
- What did the workflow produce? Capture drafts, proposed updates, tool calls, rejected outputs, accepted outputs, and final actions.
- What did humans change? Reviewer edits expose missing context, unclear policy, weak prompts, and hidden process rules.
- Where did the workflow fail? Failed writes, missing fields, duplicate records, permission errors, stale data, and low-confidence cases matter more than best-case demos.
- What decision did the owner make? The packet should end in a clear launch, repair, expand, or stop recommendation.
Without those answers, the team is not scaling a workflow. It is scaling a story.
What to collect
A useful packet does not need to become a binder. It needs enough evidence to make the next decision legible.
1. Source sample
Attach the real records used in the test window: CRM opportunities, form submissions, tickets, call notes, email threads, docs, invoices, project tasks, reports, or data exports. Mark which source is authoritative and which sources are supporting context.
2. Workflow run log
For each run, capture trigger, source record ID, prompt or workflow version, retrieved context, tool calls, output, errors, retries, cost, latency, destination system, and final state. If the workflow cannot show this much, it is too hard to inspect.
3. Accepted outputs
Show examples the owner accepted with little or no edit. These prove useful shape. They also define the standard for future QA fixtures and acceptance criteria.
4. Reviewer edits and rejected outputs
Reviewer edits are not embarrassing. They are product data. They tell the builder which fields, policies, examples, permissions, and source records need repair before the workflow gets more scope.
5. Exceptions and blocked cases
List cases that stopped the workflow: missing required fields, conflicting records, high-risk accounts, legal exposure, sensitive customer context, failed API calls, duplicate entities, or uncertain ownership. Good exception evidence prevents bad autonomy.
6. Cost and review load
Track cost per run, reviewer time, rejection rate, owner wait time, and repair effort. A workflow can be accurate and still not worth expanding if review cost destroys the value.
7. Owner decision
End with one recommendation. The evidence packet is not a scrapbook. It is the record behind a business decision.
Score the evidence
Use a simple score before granting more authority. Green means the evidence supports expansion. Yellow means repair before expansion. Red means the team should stop, clean up the workflow, or pick a better candidate.
| Evidence area | Green | Red flag |
|---|---|---|
| Source records | Real records are attached, traceable, and representative. | The sample is synthetic, cherry-picked, or impossible to reproduce. |
| Run logs | Each run shows input, context, tool calls, output, errors, cost, and final state. | The team can only inspect the final AI answer. |
| Reviewer edits | Edits are tracked by reason and used to improve the workflow. | Review happens informally in chat with no structured record. |
| Exceptions | Blocked cases route to the right owner with source evidence and recovery action. | Failures disappear, retry blindly, or rely on whoever notices first. |
| Controls | Approval rules, permissions, rollback, and pause criteria are visible. | The workflow asks for write access before controls are proven. |
| Business value | The packet shows saved time, better quality, faster response, risk reduction, or revenue motion. | The only proof is that the output sounds plausible. |
If the score is mostly yellow, do not call the workflow a failure. Turn the packet into a repair list, then rerun the narrowest test that would change the decision.
Stack fit
Evidence packets often expose a stack problem before they expose an AI problem. The workflow may need cleaner CRM fields, stable lead routing, a better approval queue, a source-of-truth rule, a telemetry event, or a safer write-back path before the model can create reliable value.
Use Purple Orange Stack's AI automation audit as supporting owned research when the packet touches CRM readiness, lead routing, sales automation, marketing operations, reporting, docs, inboxes, workflow tools, or implementation priority. Treat it as stack-fit context. The actual decision still has to come from the team's own records and operating constraints.
For a narrow draft workflow, the stack may already be enough. For a workflow that touches several systems, the packet may justify an integration map, approval queue, telemetry layer, or MCP tool surface. For a fragile data layer, the honest answer may be cleanup before AI gets more access.
Decision path
The evidence packet should end with one of these paths.
| Recommendation | Use when | Next move |
|---|---|---|
| No launch | The evidence is weak, the workflow is low-value, or the failure modes are not worth the risk. | Stop the build, keep the notes, and choose a stronger candidate. |
| Cleanup first | The workflow is valuable, but records, ownership, permissions, or routing are too messy. | Fix fields, source-of-truth rules, owner labels, permissions, or process drift. |
| Reviewable agent | The workflow drafts useful work, but human approval should remain before send or write-back. | Install one narrow agent with logs, review queue, exception handling, and owner handoff. |
| AI workflow sprint | The evidence supports value, but the workflow needs integrations, controls, QA, and launch support. | Scope a two-week sprint around one production workflow and a clear launch gate. |
| Operations buildout | Several workflows share the same source systems, approvals, logs, and operating layer. | Build the common layer once, then add workflows with stronger evidence. |
| Production AI infrastructure | The workflow needs custom MCP tools, evals, observability, security review, CI, or engineering handoff. | Treat the work as infrastructure and require stronger technical acceptance criteria. |
The practical test: after reading the packet, a skeptical operator should be able to reproduce the run, understand the risk, see the value, and agree with the next decision even if they were not in the demo.
For adjacent checks, use the AI workflow acceptance criteria before launch, the AI workflow ROI model for expansion economics, and the AI workflow runbook before handoff.
Turn scattered examples into a decision.
Bring one workflow to the free workflow audit. We will review the evidence you have, identify the missing proof, and decide whether the next move is no launch, cleanup, a reviewable agent, a sprint, an operations buildout, or infrastructure.
FAQ
How is this different from a readiness review?
The evidence packet is the proof. The readiness review is the decision meeting that uses the proof. A good packet makes the review shorter and less political.
How many examples should the packet include?
Use enough examples to represent normal work, edge cases, failures, and rejected output. For a first workflow, 20 to 50 real cases can be enough if they are traceable and selected honestly.
Should rejected outputs be included?
Yes. Rejected outputs are often the most useful part of the packet because they show missing context, weak policy, unclear ownership, bad source data, or unsafe action boundaries.
Can the packet justify no build?
Yes. That is a good outcome when the workflow is low-value, too risky, poorly owned, or not ready. The packet saves money by making that visible before the team buys tools or grants access.