Template

AI Roles & Handoff Protocol Template (Interactive)

Interactive template to define when AI performs tasks, who owns outputs, human-in-the-loop checkpoints, acceptance criteria, data retention rules, escalation paths, and governance details for collaborative AI-assisted work.

Interactive Tool

AI Roles & Handoff Protocol

Use this template to define clear task boundaries, ownership, quality checks, and escalation paths whenever AI tools participate in collaborative work. Complete the fields below for each AI-enabled task or workflow. This creates a concise, auditable handoff protocol your team can follow and review.

Short descriptive name for this AI task or workflow (e.g., 'Customer Email Drafting - AI Assist').
Protocol version or revision identifier (e.g., v1.0).
Who is accountable for this protocol and its outcomes? Provide a role if a person may change (e.g., 'Content Manager').
Name the model, vendor, or internal service used (include version where available).
Describe the work the AI will perform and the expected outputs (be concrete).
Select the operational mode describing how AI is used in practice.
Who owns and is accountable for the AI-generated output (role or team)?
Role(s) responsible for reviewing or approving AI outputs (if any). List primary and backup reviewers.
Be specific and measurable where possible. Examples: accuracy thresholds, style guidelines, allowed data sources, prohibited content.
Select applicable checkpoints. Describe any custom checkpoints in Notes.
How often are outputs checked against acceptance criteria?
Step-by-step actions, who to contact, SLAs for response, and when to pause automated actions. Include contact roles and backup contacts.
Where are inputs, outputs, and decisions logged? How long are they retained? Who can access logs? Mention encryption, access controls, and retention periods.
Record model versions, configuration, prompt templates, and where artifacts are stored (repos, artifact registry).
If yes, list applicable regulations and controls in Notes.
Notes about training data source, licensing, biases, and constraints relevant to this use.
Estimate the potential harm from incorrect outputs (customer impact, regulatory, financial, safety).
List the controls in place to reduce risk (e.g., human review, canary deployments, limited-scope rollout, test datasets).
How often should the protocol be reviewed or updated?
Include links to runbooks, logs, model registry entries, legal guidance, or related policies.
You can explore this tool now. Sign in or create an account to save your responses and return to them later.
Sign In to Save Create an Account
Make this tool part of your work

Save a personal copy, bring it to your team, or tailor the questions and workflow to fit what you are hungry to improve.

Member customization and team collaboration are coming soon.
Discuss Ask the Librarian

Discussion

Comments and conversation will live here.