Operations AI Agent Deployment Checklist | AI Agent Templates

Checklist objective#

Deploying an AI agent into an active operations workflow introduces a new failure mode to a process that currently works, however imperfectly. This checklist reduces deployment risk by ensuring that process logic, tool integrations, exception handling, human oversight, and team readiness are all confirmed before the agent handles real work.

This checklist is designed for operations managers launching AI agents into any repeatable business process: procurement, vendor management, inventory operations, facilities management, logistics coordination, or internal operations workflows. Complete it in order — each section builds on the one before it.

Section 1: Process assessment (6 items)#

[ ] Process boundaries defined. The process has a documented start event (trigger) and end state (success criteria). Any input that does not match the defined start conditions is excluded from agent scope.
[ ] Decision logic is fully explicit. Every decision point in the process has been written as an explicit if/then rule. Decisions that rely on contextual judgment, institutional knowledge, or case-by-case discretion are flagged as human-owned steps.
[ ] Volume and frequency documented. The number of process instances per day or week is measured or estimated. This baseline is required for capacity planning, exception queue sizing, and post-launch anomaly detection.
[ ] Error impact assessed. The consequences of an incorrect agent action have been evaluated for each step. Steps where an error would require significant remediation, affect financial records, or impact external parties are identified and assigned mandatory human review gates.
[ ] Process owner identified and engaged. A named process owner has reviewed the proposed automation scope, confirmed the documented decision logic, and agreed to serve as the primary escalation contact during and after launch.
[ ] Rollback plan defined. There is a documented procedure for reverting to the manual process if the agent must be taken offline. This includes who initiates the rollback, how in-flight instances are handled, and how the team is notified.

Section 2: Tool configuration (7 items)#

[ ] All source system connections tested. Each integration — API connections, database queries, file watchers, webhook receivers — has been tested end-to-end in the production environment using a representative sample of real data.
[ ] Agent credentials scoped to minimum required access. Service accounts and API keys used by the agent have read/write access only to the specific systems, tables, and fields needed. No agent credential has administrative or unrestricted access to any system.
[ ] Credential storage is secure. All API keys, passwords, and tokens used by the agent are stored in a secrets manager or environment variable system — not in workflow configuration files, code repositories, or shared documents.
[ ] Data validation rules configured. Required field checks, format validations, and business rule checks have been entered into the workflow configuration and tested against both passing and failing sample data.
[ ] Exception routing configured. Each exception type has a defined handler: the exception is logged, categorized, and routed to a named queue or person. No exception type results in a silent failure.
[ ] Notification templates reviewed and tested. Every automated notification — success confirmations, approval requests, exception alerts, escalations — has been reviewed by the relevant recipient for clarity and tested to confirm delivery via the correct channel.
[ ] Downstream system write operations tested with sample data. Any step where the agent creates or modifies records in a system of record (ERP, CRM, HRIS, ticketing system) has been tested with sample data. Test records were created, verified, and cleaned up without affecting production data.

Section 3: Testing (6 items)#

[ ] Happy path testing completed. The complete workflow was run end-to-end with a clean, valid sample instance. The output was verified by the process owner against the expected result.
[ ] Boundary case testing completed. The workflow was tested with instances at the edges of defined rules: minimum and maximum values, optional fields present and absent, the highest-volume period load, and the most complex instance type that falls within scope.
[ ] Validation failure testing completed. The workflow was tested with instances that fail each defined validation rule. Confirmed that each failure: stops processing, logs the specific failure, and sends the correct notification without generating partial outputs.
[ ] Exception handling testing completed. The workflow was tested for each defined exception type. Confirmed that each exception is caught, logged, routed to the correct queue or contact, and that the instance does not continue to downstream steps after an exception.
[ ] Parallel instance testing completed. The workflow was tested with multiple simultaneous instances to confirm that concurrency is handled correctly — no data mixing between instances, no race conditions on shared resources, no notification duplication.
[ ] System unavailability testing completed. The workflow was tested with simulated API failures or timeouts on each integrated system. Confirmed that the agent retries according to the defined retry logic and escalates when retries are exhausted, rather than crashing or processing with missing data.

Section 4: Team training (5 items)#

[ ] Process team briefing completed. Everyone whose work is affected by the automation — requestors, approvers, downstream teams — has received a clear explanation of what the agent does, what changes in their workflow, and what stays the same.
[ ] Exception queue owners trained. The people responsible for resolving exception queue items have been trained on: how to access the queue, what information is provided per exception, how to resolve each exception type, and how to log the resolution.
[ ] Escalation contacts confirmed and trained. All named escalation contacts have confirmed their role and understand the situations that will trigger an escalation to them, the expected response time, and how to signal that an escalation has been resolved.
[ ] "Agent is wrong" procedure communicated. All team members know how to report a case where they believe the agent produced an incorrect output: who to contact, what information to provide, and that the report will be investigated rather than dismissed.
[ ] Support documentation available. A brief reference guide covering the agent's scope, how to interact with notifications and approval requests, and who to contact for questions is accessible to all affected team members — not just those who built the automation.

Section 5: Go-live (6 items)#

[ ] Pilot instance defined. The initial production launch is scoped to a defined subset of instances — a specific process category, a volume cap per day, or a specific team — rather than full volume on day one.
[ ] Monitoring enabled before launch. Exception rate alerts, processing time alerts, and notification delivery monitoring are active before the first production instance is processed. Do not rely on post-hoc log review during the first week.
[ ] Process owner on standby for launch day. The process owner is available and monitoring during the first day of production operations to respond to exceptions, validate outputs, and make quick judgment calls if an unanticipated case type appears.
[ ] Manual fallback ready. The manual process steps are documented and accessible to the team for the launch period. If the agent must be paused mid-day, work can continue manually without a search for how the process used to work.
[ ] Launch communication sent. All affected stakeholders have been notified of the launch date and what to expect. Stakeholders know this is a new automated system and that feedback is actively sought during the initial period.
[ ] First-week review scheduled. A formal review of the first week's exception log, processing metrics, and stakeholder feedback is scheduled for the end of the first full week of production operation.

Section 6: Post-launch review (5 items)#

[ ] Week-one exception log reviewed. All exceptions from the first week are reviewed by the process owner and the team that built the automation. Each exception is categorized as: expected (handled correctly), unexpected but recoverable, or unexpected and requiring a rule change.
[ ] Rule accuracy baseline established. After the first two weeks of production, calculate the percentage of instances that the agent processed correctly without exceptions or human intervention. Document this as the baseline accuracy rate.
[ ] Exception queue backlog zero'd. Any exceptions from the first weeks that remain unresolved are worked through and closed. A clean queue before the end of week two prevents exception backlog from accumulating.
[ ] Reporting baseline confirmed. The first full reporting cycle is reviewed against the manual process to confirm the metrics are accurate and the report format meets the needs of the intended audience.
[ ] 30-day review scheduled. A formal 30-day review is scheduled to assess: accuracy trends, exception rate trends, team feedback, and whether the automation is delivering the expected time savings. Use this review to decide whether to expand volume or scope, maintain the current state, or address identified issues.

FAQ#

How long does a deployment following this checklist typically take?#

The timeline depends on process complexity and integration availability. For a process with three to five integration points and straightforward decision logic, teams typically complete Sections 1-3 (assessment, configuration, testing) in two to four weeks. Sections 4-5 (training and go-live) typically take one additional week. Section 6 runs over the first month of production. Budget six to eight weeks total for a first-time operations agent deployment.

Which section has the highest failure rate?#

Section 2 (tool configuration) and Section 3 (testing) are where most deployments encounter delays. The most common issues are: API access that was approved in discussion but not provisioned before testing, edge cases that appear in real data but were not anticipated during rule design, and notification routing that works in testing but reaches the wrong person in production. Allocate extra time for both sections.

Do we need to complete the checklist for every new process we automate?#

Yes, but subsequent deployments become faster. The first deployment establishes your credential management approach, your exception queue tooling, your monitoring setup, and your team training templates. Most teams find that the second and third process deployments take roughly half the time of the first.

Parent page: AI Agent Templates
Related template: Operations Process Automation Prompt Template
Related template: Operations Workflow Automation Blueprint
Cross-playbook: Human-in-the-Loop
Cross-playbook: How to Deploy AI Agents in Your Company