AI Agent Launch Checklist

NASA’s intricate rocket launch process involves 490 readiness criteria, ensuring every ground and flight system is primed for liftoff. A thorough launch-readiness checklist confirms that all operational and safety protocols are met, with validations commencing well in advance of the final countdown.

Leading DevOps teams leverage advanced CI/CD pipelines to automate their release-readiness procedures. Comprehensive requirements, including continuous testing, robust observability, and assured data readiness, are vital for consistent and reliable deployments.

As organizations increasingly plan to introduce AI agents into production environments, crafting a complete release-readiness checklist becomes indispensable. This checklist must encompass technical, legal, security, safety, brand integrity, and other essential business considerations.

“The release checklist guarantees that every AI agent is secure, adheres to compliance standards, and is trained on superior quality data, enabling it to automate interactions confidently,” states Raj Balasundaram, Global VP of AI Innovations at Verint. “Ongoing testing and diligent monitoring boost accuracy and containment rates, demonstrating that AI effectively reduces effort and operational costs. Continuous user feedback is key to ensuring the agent consistently improves and delivers measurable business outcomes.”

For this article, I gathered insights from experts, focusing on the release readiness criteria vital for DevOps, data science, and infrastructure teams deploying AI agents.

1. Define key performance indicators for value

Teams developing AI agents require a unified understanding of their vision and the value it delivers. Articulating a clear vision statement early on aligns stakeholders, while tracking specific value metrics ensures the team remains focused. A well-defined value target is crucial for determining when an agent transitions from beta to full production.

“Before an AI agent enters production, it’s essential to pinpoint the business outcome it aims to influence and how success will be measured, as many organizations focus solely on model metrics, neglecting value tracking,” advises Jed Dougherty, Head of AI Architecture at Dataiku. “Businesses should implement a measurement framework that links agent activity directly to business results, ensuring deployments yield tangible value, not just technical prowess.”

Checklist: Pinpoint specific value metrics that can serve as early indicators of AI’s return on investment (ROI). For instance, customer service value metrics could involve comparing ticket resolution times and customer satisfaction scores between AI-assisted and purely human interactions.

2. Identify essential trust components

Even prior to developing and rigorously testing AI agents, top-tier IT organizations understand the significance of establishing an AI change management initiative. Leaders of such programs must prioritize guiding end-users to boost adoption and cultivate confidence in the AI agent’s recommendations.

“Trust begins with data that is pristine, uniform, and well-organized, validated for accuracy, refreshed consistently, and safeguarded by clear ownership, ensuring agents learn from the correct information,” states Ryan Peterson, EVP and Chief Product Officer at Concentrix. “Readiness is maintained through comprehensive scenario-based testing, ‘red-teaming’ exercises, and human oversight, complemented by feedback loops that continuously retrain systems as data and policies evolve.”

Checklist: Release-readiness criteria should incorporate elements for building trust, such as having a defined change plan, monitoring end-user adoption rates, and assessing employee engagement with AI agents.

3. Assess data integrity

AI agents rely on enterprise data for training and to provide additional context during their operations. Leading SaaS and security firms are integrating agentic AI capabilities, necessitating clear data quality metrics before these features are rolled out to employees.

Experts suggest that data governance teams need to broaden their data quality practices beyond traditional structured data sources.

“Regardless of technological sophistication, an AI agent cannot effectively reason or act without data that is clean, trustworthy, and properly governed,” emphasizes Felix Van de Maele, CEO of Collibra. “Data quality, especially concerning unstructured data, is the determining factor in whether AI propels progress or succumbs to complexity.”

Businesses in knowledge-intensive sectors like financial services, insurance, and healthcare should aim to productize their data assets and establish rigorous data health metrics. Meanwhile, manufacturers and industrial enterprises must ensure data quality for their operational, IoT, and streaming data streams.

“While the definition of high-quality data varies—be it precise code or sensor readings accurate to the nanosecond—the reality is that data is now driving more concrete actions than ever before,” notes Peter Albert, CISO of InfluxData. “Anyone responsible for deploying an AI agent must grasp their organization’s quality standards, know how to verify that quality, and establish workflows that facilitate user feedback on agent performance.”

Checklist: Before utilizing data for developing and training AI agents, apply data quality metrics to verify its accuracy, completeness, consistency, timeliness, uniqueness, and validity.

4. Guarantee data regulatory adherence

Even if a data product satisfies data quality standards for AI agent integration, it doesn’t automatically grant permission for every use case. Teams must ensure that the AI agent’s usage of a data product aligns with all regulatory and internal company compliance requirements.

Ojas Rege, SVP and GM of Privacy and Data Governance at OneTrust, advises, “Assess whether the agent is authorized to utilize specific data based on regulations, internal policies, data ethics, customer expectations, contractual obligations, and your organization’s unique requirements. AI agents possess the capacity for both tremendous benefit and significant harm rapidly, meaning the negative repercussions of feeding them incorrect data can escalate uncontrollably if not proactively governed.”

Checklist: Initially, confirm whether the AI Agent needs to be GDPR compliant or adhere to the EU AI Act. Regulations are industry-specific; for example, AI agents in financial services are subject to a comprehensive array of compliance mandates.

5. Verify DataOps dependability and resilience

Are the data pipelines, originally built for data visualizations and smaller-scale machine learning models, sufficiently reliable and robust for the demands of AI agents? Many organizations employ data fabrics to centralize access to data assets for diverse business needs, including AI agents. As collaboration with AI agents grows, expectations for data availability and pipeline performance are set to rise.

“Achieving release readiness for AI agents begins with data that is trustworthy, governed, and rich in context,” affirms Michael Ameling, President of SAP BTP and member of the extended board at SAP. “By integrating observability, accountability, and feedback mechanisms at every level—from data quality to compliance—organizations can ensure AI agents operate responsibly and effectively at scale.”

Checklist: Implement Site Reliability Engineering (SRE) principles for data pipelines and DataOps. Define clear service level objectives, track pipeline error rates, and invest in necessary infrastructure enhancements.

6. Articulate design guidelines

Many organizations will integrate future-of-work AI agents into their existing enterprise and SaaS platforms. However, as more companies seek a competitive edge through AI, they will consider developing AI agents tailored to proprietary workflows and distinct customer experiences. Architects and delivery leaders must establish and disseminate clear design principles, as addressing an AI agent’s technical debt can prove costly.

Nikhil Mungel, Head of AI at Cribl, suggests several key design principles:

• Verify access permissions as early as possible within the inference pipeline. If unauthorized data reaches the context stage, it is highly likely to appear in the agent’s output.
• Maintain immutable audit logs detailing all agent actions and corresponding human authorizations.
• Employ guardrails and adversarial testing to guarantee agents operate strictly within their defined parameters.
• Develop a suite of narrowly focused agents that can collaborate, as this often offers greater safety and reliability than a single, all-purpose agent, which may be more susceptible to manipulation by adversaries.

Pranava Adduri, CTO and Co-founder of Bedrock Data, further proposes these AI agent design principles to ensure agents behave predictably:

• Programmatic logic is thoroughly tested.
• Prompts demonstrate stability against defined evaluations.
• The systems from which agents derive context are continuously validated for trustworthiness.
• Agents are comprehensively mapped to a data bill of materials and to connected MCP or A2A systems.

According to Chris Mahl, CEO of Pryon, an agent that cannot recall information from the previous day is not ready for production. “A frequently overlooked yet crucial criterion is the agent’s memory architecture; your system must incorporate appropriate multi-tier caching, including query cache, embedding cache, and response cache, to genuinely learn from usage. Without the preservation of conversations and the retention of cross-session context, your agent will suffer from amnesia, severely compromising data quality and user trust. Thoroughly test whether the agent maintains semantic relationships across sessions, retrieves pertinent context from past interactions, and effectively manages memory limitations.”

Checklist: Identify opportunities to extend your organization’s core principles in DevSecOps and data governance, then formulate specific development principles tailored to AI agent creation.

7. Uphold critical security requirements

Organizations set non-negotiable standards, and agile development teams document non-functional requirements for AI agents. However, IT leaders may face pressure to bypass certain rules for faster production deployment. Given the substantial risks associated with shadow AI and unauthorized AI agents, CISOs are expected to strictly enforce security non-negotiables, particularly concerning how AI models interact with sensitive data.

“The most prevalent errors in deploying agents typically fall into three main categories: exposure of sensitive data, poor access management, and insufficient policy enforcement,” explains Elad Schulman, CEO and Co-founder of Lasso Security. “Companies must clearly define which tasks AI agents can execute autonomously and which necessitate human oversight, especially when dealing with sensitive information or critical operations. Principles such as least privilege, real-time policy enforcement, and complete observability are crucial to implement from the outset, rather than as retrofitted protections post-deployment.”

Checklist: Leverage AI risk management frameworks such as NIST, SAIF, and AICM. For developing security requirements, refer to best practices from Microsoft, MIT, and SANS.

8. Scale infrastructure optimized for AI

AI agents represent a fusion of DataOps, data management, machine learning models, and web service functionalities. Even if your organization has adopted platform engineering best practices, it’s highly probable that AI agents will necessitate novel architectural designs and enhanced security protocols.

Kevin Cochrane, CMO of Vultr, suggests these multi-layered security measures for scaling and securing an AI-first infrastructure:

• Tenant segmentation and confidential computing technologies.
• Comprehensive end-to-end encryption for data both in transit and at rest.
• Robust access controls and sophisticated identity management systems.
• Safeguards at the model level, including versioning, resilience against adversarial attacks, and defined usage boundaries.

“By integrating these layers with strong observability, continuous monitoring, and effective user feedback mechanisms, organizations can achieve ‘release-readiness’ and transform experimental autonomous AI into secure, scalable enterprise impact,” Cochrane states.

Checklist: Begin by utilizing reference architectures from AWS, Azure, and Google Cloud as foundational guides.

9. Standardize visibility, testing, and oversight

I gathered numerous recommendations concerning standardized observability, rigorous testing protocols, and thorough monitoring for AI agents.

• Observability: “Achieving readiness for agentic AI goes beyond basic telemetry—it demands comprehensive visibility and uninterrupted tracking of every model invocation, tool execution, and workflow step,” asserts Michael Whetten, SVP of Product at Datadog. “By combining end-to-end tracing, latency and error monitoring, and granular telemetry with experimentation frameworks and rapid user feedback loops, organizations can swiftly pinpoint regressions, confirm improvements, manage costs, and bolster reliability and safety.”
• Automated testing: Rishi Rana, CEO of Cyara, states, “Teams must approach testing as a critical trust stress test: Continuously validate data quality, intent accuracy, output consistency, and compliance to identify failures before they impact users. Testing should encompass edge cases, complex conversational flows, and human error scenarios, while structured feedback loops allow agents to safely adapt in real-world environments.”
• Monitoring: David Talby, CEO of Pacific AI, comments, “After deployment, continuous monitoring and feedback loops are indispensable for detecting drift, bias, or safety concerns as conditions change. A robust governance checklist should incorporate data quality validation, stringent security guardrails, automated regression testing, effective user feedback capture, and comprehensive audit trails to maintain trust and compliance throughout the AI lifecycle.”

Checklist: IT organizations should establish a foundational release-readiness standard for the observability, testing, and monitoring of AI agents. Teams should then engage with business and risk management stakeholders to outline additional requirements specific to the AI agents under development.

10. Implement end-user feedback mechanisms

Once an AI agent is released into production—even if it’s initially for a small beta group—the development team must have the necessary tools and processes to collect user feedback effectively.

“The most effective teams now leverage custom LLM judges and specialized domain evaluators to grade agents against actual business criteria before full production deployment,” explains Craig Wiley, Senior Director of Product Management at Databricks. “After establishing robust evaluation methods, teams need to track how performance evolves across model updates and system changes, and integrate human-in-the-loop feedback to transform evaluation data into continuous improvement.”

Checklist: Mandate an automated process for AI agents to gather feedback and subsequently enhance the underlying LLM and reasoning models.

Conclusion

AI agents transcend the mere sum of their data practices, AI models, and automation capabilities. Todd Olson, CEO and Co-founder of Pendo, emphasizes that AI demands robust product development methodologies to sustain user trust. “We conduct extensive experimentation to drive continuous enhancements, utilizing both qualitative user feedback to grasp the user experience and agent analytics to understand how users interact with an agent, the outcomes it generates, and whether it delivers genuine value.”

For organizations aiming to excel in delivering business value through AI agents, embracing a product-driven organizational structure is fundamental to achieving transformative results.