Product AI Agent Launch Checklist | AI Agent Templates

Checklist objective#

Launching an AI-powered product feature carries different risks than launching a conventional feature. AI outputs are probabilistic, not deterministic. Users need to understand what the agent can and cannot do. Failure modes need to be designed for, not just handled after they appear. This checklist ensures that AI product features are defined, built, communicated, and measured in ways that set the feature up to succeed.

This checklist is designed for product managers shipping any user-facing AI capability: AI-generated content, intelligent recommendations, automated task execution, conversational interfaces, or AI-assisted workflows. Complete all sections before declaring the feature ready to launch.

Section 1: Product definition (7 items)#

[ ] Problem statement is validated. The user problem this AI feature solves has been confirmed through research — user interviews, usage data, support tickets, or other evidence. The feature is solving a real problem, not a hypothetical one.
[ ] AI is the right solution for this problem. The decision to use AI rather than a deterministic or rules-based approach has been explicitly made and documented. AI adds value when variability in input requires adaptive handling, when pattern recognition across large datasets is needed, or when generation of novel content is the goal. If a simple lookup or rule would work, document why AI was chosen instead.
[ ] Agent scope boundaries are defined. There is a written description of exactly what the AI agent is permitted to do, what data it can access, and what actions it can take on behalf of the user. Anything outside this scope is not in scope for this launch.
[ ] Failure behavior is designed and documented. The experience when the AI produces a low-confidence output, an incorrect result, or no result has been explicitly designed — not left to engineering to handle ad hoc. Users should never encounter a silent failure or an unhandled error state.
[ ] Human override is designed. Users can override, dismiss, edit, or ignore any AI output. The design does not put users in a position where they must act on an AI recommendation without the ability to modify it or choose a different path.
[ ] AI disclosure is included. Users are clearly informed when content or recommendations are AI-generated. The disclosure is visible, not buried in settings or legal text, and uses plain language rather than jargon.
[ ] Feedback mechanism is included. Users have a direct way to signal when an AI output was unhelpful, incorrect, or inappropriate. This feedback is routed to a product inbox for PM review, not just logged without follow-up.

Section 2: Technical requirements (7 items)#

[ ] AI model selection is documented. The model or models used (internal or third-party) are documented with version numbers and the rationale for selection. There is a plan for what happens when the model provider updates or deprecates a version.
[ ] Prompt versioning is in place. System prompts, few-shot examples, and retrieval configurations are version-controlled. Changes are tracked with a date and change summary. Rolling back to a previous prompt version is possible without a code deployment.
[ ] Latency requirements are defined and tested. Maximum acceptable latency for AI responses has been defined in the product requirements. Latency has been measured under production-representative load and confirmed to meet the requirement.
[ ] Graceful degradation is implemented. If the AI model API is unavailable, slow, or returns an error, the feature degrades gracefully — falling back to a rule-based alternative, a cached result, or a clear "unavailable" state — rather than breaking the surrounding product experience.
[ ] Cost controls are in place. Token or API call budgets per user, per session, or per time period are configured. There is an alert when usage approaches the budget threshold, and a defined behavior (throttle, disable, notify) when the budget is exceeded.
[ ] Data sent to AI model is compliant. Legal and privacy review has confirmed that the data being sent to the AI model (including any third-party model API) is permissible under the company's privacy policy, applicable regulations, and any contractual obligations. Data that should not be sent to external APIs is filtered before the model call.
[ ] Output filtering is implemented. The model output passes through a filter or validation layer before it is shown to users. The filter catches outputs that violate content policies, contain prohibited categories of information, or fail format requirements.

Section 3: User experience (6 items)#

[ ] AI capability is discoverable. Users who would benefit from the AI feature can find it without specific knowledge of where to look. Discovery surfaces are identified and implemented: tooltips, empty states, onboarding flows, or contextual prompts.
[ ] Onboarding explains AI capabilities accurately. First-time user onboarding or feature introduction sets accurate expectations: what the AI does, how accurate it typically is, and what the user should do when the output needs adjustment.
[ ] Loading and streaming states are designed. AI responses often take longer than conventional API calls. Loading states, streaming text display, and intermediate progress indicators are designed to keep users informed rather than showing a blank screen.
[ ] Empty and error states are designed. Edge cases are covered: what the user sees when no result is available, when the model returns an error, when the user's input is outside the model's capability, and when the feature is temporarily unavailable.
[ ] Accessibility requirements are met. AI-generated content and interactive AI features meet the accessibility standard defined in the product's design system (WCAG 2.1 AA as a minimum). Screen reader compatibility, keyboard navigation, and contrast requirements are validated.
[ ] Mobile experience is designed and tested. If the product has a mobile experience, the AI feature has been designed for mobile screen sizes and input patterns, and tested on representative devices. AI features that depend on large context displays or complex interactions are adapted for mobile, not simply hidden.

Section 4: Launch plan (6 items)#

[ ] Launch segment is defined. The initial launch is targeted at a defined user segment, cohort, or percentage of traffic — not immediate full rollout. The initial segment is chosen to provide meaningful signal while limiting exposure if unexpected issues emerge.
[ ] Feature flag or rollout control is implemented. The AI feature can be enabled, disabled, or rolled back to a subset of users without a code deployment. This control is tested before launch.
[ ] Support team is briefed. Customer support has received a briefing on the new AI feature: what it does, common questions users might have, known limitations, and how to escalate issues that require PM attention.
[ ] Launch communication is prepared. User-facing communication (in-product announcements, email, release notes, help documentation) is prepared and reviewed. Communications accurately describe what the feature does and set appropriate expectations.
[ ] Incident response plan is documented. There is a documented procedure for responding to a significant incident: who is notified, how quickly, what the escalation path is, and under what conditions the feature is disabled. This plan is shared with engineering and on-call before launch.
[ ] Rollback decision criteria are defined. The conditions that would trigger a feature rollback or disable are documented and agreed upon before launch. Criteria include: error rate above a defined threshold, user feedback signal above a defined severity threshold, and cost overrun above a defined percentage.

Section 5: Success metrics (6 items)#

[ ] Primary success metric is defined. There is one primary metric that determines whether this feature is successful. It is measurable, attributable to the feature, and tied to user value or business outcome — not a vanity metric like page views or feature usage count alone.
[ ] Baseline is established. The pre-launch baseline for the primary metric is documented. Without a baseline, a post-launch measurement cannot determine whether the feature improved anything.
[ ] Guardrail metrics are defined. In addition to the success metric, there are guardrail metrics that must not worsen as a result of the feature launch: session completion rate, error rate, user-reported satisfaction, or other indicators of user experience quality.
[ ] AI quality metrics are tracked. In addition to product-level metrics, AI-specific quality is measured: accuracy rate (where measurable), user override rate (how often users edit or dismiss AI output), and negative feedback rate (explicit thumbs-down or report signals).
[ ] Measurement infrastructure is validated. The instrumentation for tracking all defined metrics is in place and has been validated to produce correct data. Instrumentation is tested before launch, not assumed to work.
[ ] Review cadence is scheduled. A scheduled review of all metrics is planned for: 7 days, 30 days, and 90 days post-launch. Review owners are assigned. The 30-day review includes a go/no-go decision on expanding the rollout to full traffic.

FAQ#

What is the right scope for an initial AI feature launch?#

The right scope is the smallest version of the feature that tests your core hypothesis about user value. If you believe AI will help users write better product descriptions, launch with one product description type, one writing style, and one user segment. Expand after you have evidence the core value proposition works. Launching broad too early means you are scaling something that may need fundamental changes.

How do we handle AI output quality before launch?#

Set a quality bar through manual evaluation before launch. Take a representative sample of 50-100 inputs your users will actually provide, run them through the AI, and evaluate the outputs against defined quality criteria. If more than a defined percentage (commonly 10-15%) of outputs are unacceptable, address the prompt, model selection, or output filtering before launch — not after.

Do we need a separate compliance review for AI features?#

Yes, in most cases. AI features that generate content, make recommendations, or take automated actions on behalf of users introduce legal and regulatory questions that standard feature reviews do not cover: model output liability, data processing obligations under GDPR or CCPA, and sector-specific requirements in finance, healthcare, or HR. Involve legal and privacy early — after engineering has scoped the feature but before significant implementation work begins.

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