AI Agent Launch Checklist
A recruiter-readable artifact showing how Sararin structures AI launch-readiness thinking before a team moves from idea to PoC, pilot, or workflow adoption.
This is a portfolio framework and simulated launch-readiness checklist, not a client deliverable, production compliance framework, benchmark proof, product claim, or universal AI launch methodology. It shows how I structure AI launch-readiness thinking.
What This Demonstrates
The checklist turns an AI adoption idea into a reviewable launch conversation: what business process is changing, what evidence is ready, which decisions need humans, and when the team should stop instead of pushing automation forward.
- Business-process translation
- AI adoption governance
- Human-in-the-loop design
- Evidence and impact thinking
- Cross-functional delivery readiness
Launch-Readiness Checklist
Each area is a gate for reducing launch risk. A weak answer does not automatically block exploration, but it changes the next step: narrow the PoC, add review, improve evidence, or stop.
Define the business process, decision owner, user group, and failure cost before selecting an AI pattern.
Check whether source knowledge is current, approved, searchable, and traceable enough for AI-assisted use.
Separate work AI may draft from decisions that require human approval, escalation, or sign-off.
Identify sensitive topics, approval owners, forbidden claims, and policy limits before pilot use.
Define the smallest testable workflow, expected users, evidence needed, and pass/fail criteria.
Plan user enablement, operating rhythm, support ownership, and behavior change beyond tool launch.
Name likely failure modes such as outdated knowledge, hallucinated confidence, unclear ownership, or unsupported automation.
Choose evidence that can be reviewed: answer quality, review load, cycle time, escalation rate, adoption, and unresolved gaps.
Set explicit conditions for pausing, narrowing, or reversing launch before users depend on weak behavior.
Simulated Enterprise Scenario
The dry run uses a synthetic enterprise retail scenario. It is not a client engagement, not real company data, and not evidence of production deployment.
- A synthetic enterprise retail scenario starts with repeated business questions across policy, finance, and operating teams.
- The checklist tests whether AI-assisted answers can cite approved knowledge, ask for missing facts, and route sensitive topics to review.
- Launch readiness is judged by evidence quality, decision ownership, user workflow fit, escalation clarity, and stop criteria.
This artifact is meant to show business-to-engineering translation: how an ambiguous AI request becomes a controlled workflow launch discussion with owners, gates, evidence, and rollback criteria.
It complements the AI Operating System case study by showing a concrete artifact that could guide AI adoption planning without claiming production compliance or universal methodology status.
Return to the AI Operating System case study