Copilot and Agent PRD Template

Copilot and agent requirements: jobs, autonomy levels, actions and undo, approval points, eval acceptance criteria, latency and cost, failure UX, rollout

AI・データフルIndustry practice; Guidelines for Human-AI Interaction (CHI 2019); PAIR Guidebookcopilot prd更新 2026/10/22

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使い方: Use it when an AI feature chats, plans or takes actions on behalf of users; use the AI Feature PRD for single-prediction features. Fill the autonomy levels and the actions table with undo paths first, then write acceptance criteria as evaluations. The main pitfall is shipping the happy path and leaving failure UX to the model's default text.

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Copilot and Agent PRD: [Product or feature name]

Product requirements document for a copilot or agent: an AI feature that works alongside users and can take actions on their behalf. It records users and jobs, the autonomy level per action, actions with their undo path, human approval points, eval-driven acceptance criteria, quality, latency and cost targets, failure UX, transparency, telemetry and rollout. Based on common industry practice for AI product requirements, informed by the Guidelines for Human-AI Interaction (Amershi et al., CHI 2019) and the Google PAIR People + AI Guidebook. Replace every [bracketed] value and every row marked Example.

Document control

Field	Value
Document ID	[CPRD-001]
Product or feature	[Name]
Version	[0.1]
Status	[Draft / In review / Approved / Superseded]
Owner	[@product-manager]
Approvers / reviewers	[@eng-lead], [@design-lead], [@ml-lead], [@security], [@legal]
Target release	[Example: Beta in Q3, GA in Q4]
Surface	[Example: Side panel in web app; Slack app]
Last updated	YYYY-MM-DD
Classification	[Internal / Confidential]
Upstream documents	AI Opportunity Canvas (`ai-data/ai-opportunity-canvas`), AI Use Case Intake (`ai-data/ai-use-case-intake`)

Approvals

Role	Name	Decision	Date
Product	[@name]	[Approve / Approve with conditions / Reject]	YYYY-MM-DD
Engineering	[@name]	[Decision]	YYYY-MM-DD
Design and research	[@name]	[Decision]	YYYY-MM-DD
Security and privacy	[@name]	[Decision]	YYYY-MM-DD
AI governance	[@name]	[Decision]	YYYY-MM-DD

Revision history

Version	Date	Author	Change summary
0.1	YYYY-MM-DD	[@name]	First draft
0.2	YYYY-MM-DD	[@name]	Example: Added `send_email` action at autonomy level 2 with approval
1.0	YYYY-MM-DD	[@name]	Example: Approved for beta

Problem, users and jobs
Goals, non-goals and scope
Autonomy levels
Actions, undo and approval points
Interaction design
Eval-driven acceptance criteria
Quality, latency and cost targets
Failure UX
Transparency and user control
Telemetry and feedback
Rollout plan
Risks, dependencies and open questions
Traceability and glossary

1. Problem, users and jobs

1.1 Problem statement

[Describe the problem in user terms and its cost today.]

Example: Account managers spend about 5 hours a week preparing for renewal calls: pulling usage data, reading recent tickets and drafting a summary email. Preparation quality varies, and 1 in 5 renewals reaches the call without a usage review.

1.2 Users

Persona	Context	Expertise	What they must stay in control of
Example: Account manager	Weekly renewal prep, desktop	Expert in accounts, novice with data tools	Anything sent to the customer
Example: Sales manager	Reviews pipeline	Expert	Discount approvals
[Persona]	[Context]	[Expertise]	[Control]

1.3 Jobs to be done

ID	Job	Frequency	Verifiable by user?	Copilot role
CPRD-JOB-001 (Example)	Summarise an account's last 90 days before a call	Weekly per account	Yes, against source links	Draft
CPRD-JOB-002 (Example)	Draft a renewal email with usage highlights	Weekly	Yes	Draft, user sends
CPRD-JOB-003 (Example)	Log call notes to the CRM	After each call	Yes	Act with confirmation
CPRD-JOB-004 (Example)	Propose a discount	Rare	Hard	Out of scope
[ID]	[Job]	[Frequency]	[Yes / No]	[Suggest / Draft / Act]

2. Goals, non-goals and scope

Type	Statement
Goal	Example: Cut renewal prep time from 5 hours to 2 hours per week per account manager
Goal	Example: 95% of renewal calls preceded by a usage review
Goal	[Goal]
Non-goal	Example: The copilot does not negotiate prices or send anything to customers without the user pressing Send
Non-goal	Example: No access to HR or payroll data
Non-goal	[Non-goal]

In-scope systems: [Example: CRM (read and write notes), product analytics (read), ticketing (read), email (draft only).]

Out-of-scope systems: [Example: Billing, contract management.]

3. Autonomy levels

Level	Name	What the copilot does	What the user does
0	Inform	Answers questions, explains, retrieves	Decides and acts
1	Suggest	Proposes an action or draft	Edits and executes it
2	Act with approval	Prepares and executes after explicit confirmation	Approves each action
3	Act and notify	Executes within limits, then reports	Reviews, can undo
4	Act autonomously	Executes and reports in a periodic digest	Audits samples

Job or action	Level at launch	Target level	Evidence required to raise
CPRD-JOB-001 Summarise account (Example)	0	0	Not applicable
CPRD-JOB-002 Draft renewal email (Example)	1	1	Never auto-send to customers
CPRD-JOB-003 Log call notes (Example)	2	3	Edit rate below 10% over 4 weeks and zero wrong-account writes
[Job]	[Level]	[Level]	[Evidence]

4. Actions, undo and approval points

ID	Action	Side effect	Autonomy level	Approval point	Undo path	Undo window	Blast radius
CPRD-ACT-001 (Example)	`create_crm_note`	Writes a note to an account	2	Preview card with Confirm	Delete note from the action history	30 days	One account
CPRD-ACT-002 (Example)	`update_next_step`	Changes opportunity next step and date	2	Diff view with Confirm	Revert to previous value	7 days	One opportunity
CPRD-ACT-003 (Example)	`create_email_draft`	Creates a draft in the user's mailbox	1	None (draft only)	Discard draft	Until sent	None until sent
CPRD-ACT-004 (Example)	`schedule_meeting`	Sends invitations to external people	2	Confirm with attendee list shown	Cancel meeting (notification already sent)	Partial	External recipients
[ID]	[Action]	[Effect]	[Level]	[Approval]	[Undo]	[Window]	[Radius]

Approval point rules:

Show exactly what will change, on which record, before the user confirms. Never ask for approval of an action the user cannot inspect.
Group approvals only when the actions are of the same type and target the same record set; show the count.
If the user does not respond, the action does not run. Pending actions expire after [Example: 24 hours].
Actions that reach people outside the organisation always require approval, regardless of autonomy level.

5. Interaction design

5.1 Entry points

Entry point	Context passed	Typical job
Example: "Prep this renewal" button on the account page	Account ID, user role	CPRD-JOB-001
Example: Side panel chat	Current page, selection	Any
Example: Post-call prompt after calendar event ends	Meeting ID, attendees	CPRD-JOB-003
[Entry point]	[Context]	[Job]

5.2 Main flow

Describe the main flow for the most frequent job. Example: renewal preparation, from the account page to a logged next step.

5.3 Steering and correction

Users can edit any draft inline before acting on it.
Users can say what to change in plain language ("shorter", "mention the outage") and see a revised draft.
Users can stop a running multi-step task at any time; partial results remain visible and no further actions run.
[Additional steering control]

6. Eval-driven acceptance criteria

ID	Criterion	Dataset	Metric	Threshold	Gate
CPRD-EVAL-001 (Example)	Summaries are faithful to source data	200 real accounts, labelled by 3 account managers	Share of summaries with no unsupported claim	at least 95%	Blocking
CPRD-EVAL-002 (Example)	Every factual claim links to a source	Same set	Citation coverage	at least 98%	Blocking
CPRD-EVAL-003 (Example)	Actions target the right record	150 scripted tasks	Wrong-record actions	0	Blocking
CPRD-EVAL-004 (Example)	Task completion for multi-step jobs	150 scripted tasks	Completed without user correction	at least 80%	Blocking for level 3
CPRD-EVAL-005 (Example)	Resistance to instructions embedded in tickets	60 adversarial tickets	Unauthorised actions	0	Blocking
CPRD-EVAL-006 (Example)	Draft usefulness	50 drafts rated by users	Mean rating out of 5	at least 4.0	Non-blocking
[ID]	[Criterion]	[Dataset]	[Metric]	[Threshold]	[Blocking / Non-blocking]

Re-run all blocking criteria on every change to model, prompt, tools or retrieval index. Record results in the release notes.

7. Quality, latency and cost targets

Job	Time to first token (p95)	Time to complete (p95)	Max tool calls	Cost per task
CPRD-JOB-001 (Example)	1.5 s	12 s	6	USD 0.03
CPRD-JOB-002 (Example)	1.5 s	8 s	2	USD 0.01
CPRD-JOB-003 (Example)	1.0 s	5 s	2	USD 0.005
[Job]	[Target]	[Target]	[Limit]	[Cost]

Monthly cost estimate (Example): 400 account managers x 25 tasks per week x 4.3 weeks x USD 0.02 average = USD 860 per month. Budget alert at USD 1,200.

Degradation rules: when a budget is exceeded, the copilot [Example: stops after 6 tool calls, shows partial results and asks whether to continue].

8. Failure UX

ID	Failure	Detection	What the user sees	Recovery
CPRD-FAIL-001 (Example)	Data source unavailable	Tool error	"Usage data is unavailable right now. The summary below covers tickets and notes only."	Retry button; summary marked partial
CPRD-FAIL-002 (Example)	Low confidence or no source found	Retrieval score below threshold	"I could not find support for this in the account records."	Offer search link; no claim made
CPRD-FAIL-003 (Example)	Ambiguous request	Planner detects two matching accounts	Short clarifying question with both options	User picks one
CPRD-FAIL-004 (Example)	Action failed after approval	Tool returns error	Clear error, nothing partially written	Retry or open record manually
CPRD-FAIL-005 (Example)	Request out of scope	Policy classifier	States what it cannot do and where to go	Link to the right tool or team
CPRD-FAIL-006 (Example)	Wrong output noticed by user	Thumbs down or undo	Undo confirmation and optional reason field	Feedback logged for review
[ID]	[Failure]	[Detection]	[Copy]	[Recovery]

Rules for failure copy: say what happened, what was and was not done, and what the user can do next. Do not blame the user. Do not apologise repeatedly. Never claim an action succeeded until the tool confirms it.

9. Transparency and user control

Requirement	Implementation	Status
Users know the copilot is AI	Example: Label "AI copilot" on panel header and on every AI-written CRM note	[Planned / Done]
Capabilities and limits explained	Example: First-run card listing what it can read and change	[Status]
Quality expectations set	Example: "Check drafts before sending; summaries may miss recent changes"	[Status]
Sources shown	Citations link to the record or ticket	[Status]
Action history	Example: Panel tab listing every action, time, target and Undo	[Status]
Global controls	Example: Settings to turn off proactive prompts and to limit which systems the copilot may read	[Status]
Change notices	Example: Release note in the panel when abilities change	[Status]
Legal disclosure where required	Example: AI interaction disclosure per applicable law; see AI Content Transparency Plan (`ai-data/ai-content-transparency-plan`)	[Status]

Run a full review of the interaction against the 18 guidelines using the Human-AI Interaction Review (ai-data/human-ai-interaction-review).

10. Telemetry and feedback

Event	Fields	Purpose	Retention
`copilot_invoked` (Example)	Entry point, job type, user role	Adoption by entry point	13 months
`action_proposed` (Example)	Action ID, level, target type	Funnel to approval	13 months
`action_approved` / `action_rejected` (Example)	Action ID, time to decide	Trust and friction	13 months
`action_undone` (Example)	Action ID, time since execution	Quality signal	13 months
`feedback_given` (Example)	Rating, reason code, optional text	Quality review	90 days for free text
`trace` (Example)	Tool calls, latency, tokens, model version	Debugging, cost	30 days, redacted
[Event]	[Fields]	[Purpose]	[Retention]

Production quality metrics: acceptance rate of drafts, edit distance before send, undo rate per action, rejection rate at approval points, escalations to support, and the guardrail metrics from section 6. Trace format and dashboards follow the LLM Observability Specification (ai-data/llm-observability-spec).

11. Rollout plan

Stage	Audience	Autonomy	Entry criteria	Exit criteria	Rollback trigger
Internal dogfood (Example)	20 volunteers	Levels 0 to 1	Blocking evals pass	2 weeks, no severity-1 issues	Any wrong-record write
Beta (Example)	10% of account managers	Levels 0 to 2	Dogfood exit met	4 weeks, goals trending to target	Undo rate above 15%
GA (Example)	All account managers	Levels 0 to 2	Beta exit met; launch review approved	Not applicable	Severity-1 incident
Level raise (Example)	GA users opt in	Level 3 for CPRD-ACT-001	Evidence in section 3 met	4 weeks stable	Any wrong-record write

Launch readiness is confirmed in the AI Launch Review (ai-data/ai-launch-review). A kill switch disables all actions within [Example: 5 minutes] while keeping read-only answers available.

12. Risks, dependencies and open questions

ID	Type	Description	Mitigation	Owner
CPRD-RISK-001 (Example)	Over-reliance	Users stop reading summaries and repeat errors to customers	Citations, sampled QA, training	[@product-manager]
CPRD-RISK-002 (Example)	Indirect prompt injection	Ticket text instructs the copilot to change records	Tool permission limits; approval on writes; eval CPRD-EVAL-005	[@security]
CPRD-RISK-003 (Example)	Dependency	CRM API rate limits at peak	Caching; backoff; partial results	[@eng-lead]
[ID]	[Type]	[Description]	[Mitigation]	[@owner]

Open questions:

[Example: Should sales managers see their team's copilot action history? Owner: [@product-manager], decide by YYYY-MM-DD.]
[Question, owner, decision date]

13. Traceability and glossary

13.1 Traceability matrix

Job	Actions	Acceptance criteria	Failure states	Telemetry
CPRD-JOB-001 (Example)	None (read only)	CPRD-EVAL-001, CPRD-EVAL-002	CPRD-FAIL-001, CPRD-FAIL-002	`copilot_invoked`, `feedback_given`
CPRD-JOB-002 (Example)	CPRD-ACT-003	CPRD-EVAL-006	CPRD-FAIL-005	`action_proposed`
CPRD-JOB-003 (Example)	CPRD-ACT-001, CPRD-ACT-002	CPRD-EVAL-003, CPRD-EVAL-004, CPRD-EVAL-005	CPRD-FAIL-003, CPRD-FAIL-004	`action_approved`, `action_undone`
[Job]	[Actions]	[Criteria]	[Failures]	[Events]

13.2 Glossary

Term	Meaning in this PRD
Agent	An AI system that plans steps and calls tools to reach a goal
Approval point	A step where the user must confirm before an action runs
Autonomy level	The degree to which the copilot acts without per-action confirmation (section 3)
Blast radius	The scope of harm if an action goes wrong
Copilot	An AI assistant embedded in a user's workflow that informs, drafts or acts
Undo window	The time during which an action can be fully reversed

13.3 References

Amershi, S. et al. (2019). Guidelines for Human-AI Interaction. CHI 2019. https://doi.org/10.1145/3290605.3300233
Google PAIR, People + AI Guidebook (living document): https://pair.withgoogle.com/guidebook/
Related templates: AI Feature PRD (ai-data/ai-feature-prd), AI Agent Specification (ai-data/ai-agent-spec), Agent Evaluation Plan (ai-data/agent-evaluation-plan), Human Oversight Design (ai-data/human-oversight-design), AI Launch Review (ai-data/ai-launch-review).

Standard and licence note

Based on common industry practice for writing product requirements for AI copilots and agents, informed by the Guidelines for Human-AI Interaction (Amershi et al., CHI 2019) and the Google PAIR People + AI Guidebook, which are cited and not reproduced. The autonomy levels, IDs and examples are original to this template; example numbers and prices are illustrative. Check the current versions of the cited sources before use. Template licence: MIT.