Marketing AI Agent Launch Checklist | AI Agent Templates

Checklist objective#

This checklist ensures marketing AI agents are properly configured, validated, and monitored before they touch production workflows. Skipping items in this list is the primary cause of AI marketing deployments that generate embarrassing output, corrupt CRM data, or run unchecked without any performance visibility.

Work through each section sequentially. Do not mark a section complete until every item in it passes.

Section 1: Pre-deployment setup (8 items)#

These items must be completed before any agent is connected to live tools or real data.

[ ] Brand voice guidelines documented. Written guidelines covering tone, vocabulary, sentence structure, topics to avoid, and examples of approved and rejected copy are stored in the agent's knowledge base or system prompt.
[ ] Target audience profiles loaded. At minimum one detailed audience persona — role, industry, company size, pain points, content preferences — is available to the agent for context.
[ ] LLM and model version locked. The specific model version (e.g., GPT-4o, Claude 3.5 Sonnet) is documented. Model updates can change output quality; version-locking prevents silent regression.
[ ] System prompt reviewed by marketing lead. A marketing team member (not just the deployment team) has reviewed and approved the system prompt that governs agent behavior.
[ ] Test environment separate from production. Agent testing runs against sandbox CRM data and a staging CMS — not live contacts, campaigns, or the production website.
[ ] Failure handling defined. Every workflow step has a documented failure state: what happens if the API call fails, if the AI returns an unexpected format, or if the approval reviewer does not respond.
[ ] Agent output logging enabled. All agent inputs, outputs, tool calls, and errors are being logged to a location the team can access for debugging and audit.
[ ] Rollback plan documented. There is a written procedure for disabling the agent and reverting to the manual process if the deployment fails.

Section 2: Content quality validation (6 items)#

Run a minimum of 20 test outputs across the agent's intended use cases before production launch.

[ ] Brand voice consistency test passed. A sample of 10 agent outputs has been reviewed by a marketing team member against the brand voice guidelines. Fewer than 2 outputs required significant voice correction.
[ ] Factual accuracy spot-checked. Any outputs that include statistics, product claims, pricing, or company-specific information have been verified against source materials. No unverifiable claims passed review.
[ ] Keyword and SEO requirements met. For content with SEO requirements, a sample batch confirms primary keyword placement, meta description character limits, and heading structure are being followed.
[ ] Edge case outputs reviewed. The agent has been tested on unusual or ambiguous inputs (vague topic requests, missing required fields, non-English input) and handles them gracefully without generating nonsense output.
[ ] Format and length requirements validated. All output formats (blog post, LinkedIn post, email, ad copy) match the expected length and structural requirements. No truncated, incomplete, or over-length outputs in the test sample.
[ ] Duplicate content check run. A sample of outputs has been checked against existing published content for excessive similarity. AI agents can produce near-identical outputs for similar input prompts at scale.

Section 3: Brand and compliance review (5 items)#

[ ] Legal review completed for regulated industries. If the marketing team operates in a regulated industry (financial services, healthcare, legal), an appropriate legal or compliance reviewer has signed off on the agent's output boundaries and guardrails.
[ ] Competitor mention policy applied. The agent's system prompt includes clear instructions on how to handle competitor mentions — whether to avoid them entirely, use approved language only, or follow other guidelines.
[ ] Claim substantiation guardrails in place. Superlative or comparative claims ("best-in-class," "fastest," "most accurate") are either blocked by the system prompt or routed to human review before publication.
[ ] Confidentiality protections applied. The agent's access to internal documents is limited to materials approved for AI processing. Confidential pricing, roadmap, or personnel data is excluded from the knowledge base.
[ ] Copyright and originality guidelines loaded. The agent is instructed not to reproduce substantial quoted passages from external sources without attribution, and not to produce content that closely mirrors competitor materials.

Section 4: CRM and tool integrations (7 items)#

[ ] CRM connection tested with read access. If the agent reads from the CRM (contacts, company records, deal stages), the connection has been tested with live data and returns accurate, properly formatted results.
[ ] CRM write permissions scoped correctly. If the agent writes to the CRM (updating fields, creating tasks, logging activities), write permissions are limited to only the specific fields and record types the agent needs. Over-permissioned write access is a significant risk.
[ ] Email platform integration tested end-to-end. If the agent creates or sends email drafts, the integration has been tested from agent output through to the email platform (HubSpot, Mailchimp, etc.) without data loss or formatting corruption.
[ ] Social scheduling tool connected and tested. If the agent distributes to social channels, a test post has been scheduled (not published) in each target platform and reviewed for correct formatting.
[ ] Webhook and API error handling tested. Integration failure scenarios have been tested: what happens when the CRM API is unavailable, when rate limits are hit, when the response format is unexpected.
[ ] Field mapping validated. Every data field the agent writes to an external system has been verified to map to the correct field in that system. Field mapping errors are the most common cause of corrupted CRM data.
[ ] Integration permissions documented. A record exists of which service accounts, API keys, and OAuth connections the agent uses, who owns them, and when they expire.

Section 5: Performance measurement setup (5 items)#

[ ] KPIs defined and baseline established. The metrics that will determine whether this AI agent deployment is successful are documented — time saved per campaign, content output volume, engagement rates, reviewer revision frequency. A pre-deployment baseline is recorded for comparison.
[ ] Dashboard or reporting view created. Someone on the team can view agent activity metrics (runs completed, errors encountered, approval gate wait times, revision rates) without needing to query logs manually.
[ ] Review cadence scheduled. A recurring calendar event exists for the first performance review — recommended at 2 weeks post-launch, then monthly.
[ ] Feedback capture mechanism in place. Reviewers at approval gates have a structured way to log why they requested edits or rejected outputs. This feedback drives prompt refinement.
[ ] Alerting configured for errors and anomalies. The team receives a notification if the agent fails to complete a run, if error rates exceed a defined threshold, or if agent output volume drops unexpectedly.

Section 6: Go-live procedures (4 items)#

[ ] Soft launch with limited scope. The agent launches processing a subset of its intended volume — one campaign type, one channel, or one week's worth of briefs — before full deployment. This contains the blast radius of unexpected issues.
[ ] Approval gates active on all consequential actions. On launch day, human approval is required before the agent sends any customer-facing communication, publishes any content, or makes any CRM updates outside a defined safe list.
[ ] On-call contact designated for launch week. A specific team member is designated to respond to agent issues during the first week of production operation. This is not the same as "anyone on the team" — it is a named individual with a specific responsibility.
[ ] First 48-hour review scheduled. A review meeting is scheduled for 48 hours after launch to assess initial output quality, identify immediate issues, and confirm or adjust the soft launch scope.

FAQ#

How long does a full checklist review typically take?#

For a first deployment, plan for one to two full days to work through the checklist properly — testing, reviewing output samples, and getting appropriate sign-offs. Teams that rush this process typically spend significantly more time debugging production issues afterward.

Can we skip Section 3 if we are not in a regulated industry?#

The legal and compliance review item can be scoped appropriately for your industry, but the other items in Section 3 (competitor policy, claim guardrails, confidentiality) apply to all marketing teams. Skipping them creates reputational risk even outside regulated industries.

When should we move from soft launch to full deployment?#

Move to full deployment when: the soft launch has run for at least five business days, the revision rate from human reviewers is below 20%, no critical errors have occurred, and performance metrics are trending in the expected direction.

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