Product & Engineering
AI Release Notes Draft Agent: turn scattered evidence into reviewed action
Trace shipped changes from pull requests and issues into source-linked customer updates that product and engineering approve before publishing.
What changes
Evidence gathering
Owners search several systems and copy partial context
ZeroTwo assembles dated source evidence into one review packet
Decision quality
A score or summary hides missing and conflicting context
The recommendation shows sources, confidence, exceptions, and limitations
Approval
Actions move forward through informal messages
A named owner approves consequential actions before execution
Follow-through
The decision and rationale disappear after the meeting
The approved outcome, owner, and next review remain attached
Why teams use ZeroTwo for Release note drafting
Faster evidence assembly
Start from a structured source packet instead of rebuilding the same context for every review.
Reviewable recommendations
See evidence, missing fields, confidence, and limitations before accepting a suggested action.
Visible ownership
Route exceptions and consequential changes to the person accountable for the decision.
Reusable operation
Save the schema, prompt, approval rule, and review date for the next run.
Release note drafting fails when evidence and ownership stay fragmented
Product Management, Engineering Management, Developer Product & Engineering often need information from github, linear, slack. When each owner sees only one system, the decision can look complete while dates, exceptions, or required evidence remain missing.
Manual assembly also makes the review hard to repeat. A recommendation may be correct, but the next operator cannot see which source supported it, which assumption changed, or who approved the action. The agent should reduce collection work without hiding those boundaries.
How ZeroTwo runs the Release note drafting workflow
Collect current source evidence
GitHubCollect current source evidence using dated evidence, explicit decision fields, confidence notes, and a named owner so the result remains inspectable before any consequential action.
Verify shipped state
LinearMatch merged pull requests to completed issues, release tags, flags, and deployment evidence before any change enters the draft.
Classify audience relevance
SlackSeparate customer-visible improvements, fixes, breaking changes, and internal maintenance while keeping uncertain records out of the draft.
Draft release-note options
CopywritingUse the copywriting skill to create concise versions whose claims remain linked to verified issues, pull requests, and releases.
Route approval to the accountable owner
SlackRoute approval to the accountable owner using dated evidence, explicit decision fields, confidence notes, and a named owner so the result remains inspectable before any consequential action.
Record the decision and next review
GitHubRecord the decision and next review using dated evidence, explicit decision fields, confidence notes, and a named owner so the result remains inspectable before any consequential action.
The agent prepares evidence; the accountable owner makes the decision
ZeroTwo treats GitHub, Linear, Slack, and the copywriting skill as inputs for one job: turn verified shipped work into accurate customer communication. The workflow is organized around evidence, audience, exclusions, and publication approval rather than a generic integration recipe.
The boundary matters. The agent should not invent missing evidence, treat a score as certainty, or execute a sensitive update without approval. Conflicting and stale inputs remain visible so the owner can revise, defer, or stop the recommendation.
Get started in under 10 minutes
Connect your tools
One-click OAuth for each integration. No API keys, no engineering.
Describe what you need
“Before every Release note drafting review, gather current evidence from github, linear, slack, identify missing or conflicting context, draft a recommendation with confidence and limitations, and route it to the accountable owner for approval.”
It runs on schedule
Runs on the review cadence with event-triggered refreshes when a material source changes.
Frequently asked questions
An AI release notes agent prepares a reviewed decision packet for Product Management, Engineering Management, Developer Product & Engineering. It gathers source evidence, normalizes the required fields, identifies gaps, and proposes next actions while keeping consequential changes behind human approval.
This workflow uses github, linear, slack. Teams can add other approved sources when they materially affect the decision, but each added connector should have a clear purpose, permission boundary, and accountable owner.
ZeroTwo can automate approved low-risk steps, but sensitive messages, access changes, customer commitments, and material record updates should keep a human approval gate. Missing or conflicting evidence should stop the action rather than produce a more confident draft.
Track evidence completeness, reviewer correction rate, time to approval, reopened decisions, and downstream errors. The goal is faster reviewed action with fewer unsupported claims, not the largest number of automatic updates.
The workflow should preserve its source packet, current stage, proposed action, and owner so a person can resume manually. Recovery behavior belongs in the initial test because a useful agent must fail visibly without losing decision context.
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Turn Release note drafting into reviewed action.
Connect github, linear, slack. ZeroTwo prepares the evidence and keeps owners in control.