NASA’s launch-a-rocket activity includes 490 launch-readiness criteria to ensure that all ground and flight systems are prepared for launch. Having a launch-readiness checklist ensures that all operational and safety systems are ready, and validations begin long before the countdown on the launchpad.

The most advanced devops teams automate their release-readiness checklists in advanced CI/CD pipelines. Comprehensive criteria covering continuous testing, observability, and data readiness are needed for reliable continuous deployments.

As more organizations consider deploying AI agents into production, developing an all-encompassing release-readiness checklist is essential. Items on that checklist will cover technical, legal, security, safety, brand, and other business criteria.

“The release checklist ensures every AI agent is secure, compliant, and trained on high-quality data so it can automate interactions with confidence,” says Raj Balasundaram, global VP of AI innovations at Verint. “Ongoing testing and monitoring improve accuracy and containment rates while proving the AI is reducing effort and lowering costs. Continuous user feedback ensures the agent continues to improve and drive measurable business outcomes.”

For this article, I asked experts to focus on release readiness criteria for devops, data science, and infrastructure teams launching AI agents.

1. Establish value metrics

Teams working on AI agents need a shared understanding of the vision-to-value. Crafting a vision statement before development aligns stakeholders, while capturing value metrics ensures the team is on track. Having a defined value target helps the team decide when to go from beta to full production releases.

“Before an AI agent goes to production, define which business outcome it should change and how success will be measured, as most organizations track model metrics but overlook value tracking,” says Jed Dougherty, head of AI architecture at Dataiku. “Businesses should build a measurement system that connects agent activity to business results to ensure deployments drive measurable value, not just technical performance.”

Checklist: Identify value metrics that can serve as early indicators of AI return on investment (ROI). For example, customer service value metrics might compare ticket resolution times and customer satisfaction ratings between interactions that involve AI agents and those with human agents alone.

2. Determine trust factors

Even before developing and testing AI agents, world-class IT organizations recognize the importance of developing an AI change management program. Program leaders should understand the importance of guiding end users to increase adoption and build their trust in an AI agent’s recommendations.

“Trust starts with data that’s clean, consistent, and structured, verified for accuracy, refreshed regularly, and protected by clear ownership so agents learn from the right information,” says Ryan Peterson, EVP and chief product officer at Concentrix. “Readiness is sustained through scenario-based testing, red-teaming, and human review, with feedback loops that retrain systems as data and policies evolve.”

Checklist: Release-readiness checklists should include criteria for establishing trust, such as having a change plan, tracking end-user adoption, and measuring employee engagement with AI agents.

3. Measure data quality

AI agents leverage enterprise data for training and provide additional context during operations. Top SaaS and security companies are adding agentic AI capabilities, and organizations need clear data-quality metrics before releasing capabilities to employees.

Experts suggest that data governance teams must extend data-quality practices beyond structured data sources.

“No matter how advanced the technology, an AI agent can’t reason or act effectively without clean, trusted, and well-governed data,” says Felix Van de Maele, CEO of Collibra. “Data quality, especially with unstructured data, determines whether AI drives progress or crashes into complexity.”

Companies operating in knowledge industries such as financial services, insurance, and healthcare will want to productize their data sources and establish data health metrics. Manufacturers and other industrial companies should establish data quality around their operational, IoT, and other streaming data sources.

“The definition of high-quality data varies, but whether it’s clean code or sensor readings with nanosecond precision, the fact remains that data is driving more tangible actions than ever,” says Peter Albert, CISO of InfluxData. “Anyone in charge of deploying an AI agent should understand their organization’s definition of quality, know how to verify quality, and set up workflows that make it easy for users to share feedback on agents’ performance.”

Checklist: Use data quality metrics to test for accuracy, completeness, consistency, timeliness, uniqueness, and validity before using data to develop and train AI agents.

4. Ensure data compliance

Even when a data product meets data quality readiness for use in an AI agent, that isn’t a green light for using it in every use case. Teams must define how an AI agent’s use of a data product meets regulatory and company compliance requirements.

Ojas Rege, SVP and GM of privacy and data governance at OneTrust, says, “Review whether the agent is allowed to use that data based on regulations, policy, data ethics, customer expectations, contracts, and your own organization’s requirements. AI agents can do both great good and great harm quickly, so the negative impact of feeding them the wrong data can mushroom uncontrollably if not proactively governed.”

Checklist: To start, determine whether the AI Agent must be GDPR compliant or comply with the EU AI Act. Regulations vary by industry. As an example, AI agents in financial services are subject to a comprehensive set of compliance requirements.

5. Validate dataops reliability and robustness

Are data pipelines that were developed to support data visualizations and small-scale machine-learning models reliable and robust enough for AI agents? Many organizations use data fabrics to centralize access to data resources for various business purposes, including AI agents. As more people team up with AI agents, expect data availability and pipeline performance expectations to increase.

“Establishing release readiness for AI agents begins with trusted, governed, and context-rich data,” says Michael Ameling, President of SAP BTP and member of the extended board at SAP. “By embedding observability, accountability, and feedback into every layer, from data quality to compliance, organizations can ensure AI agents act responsibly and at scale.”

Checklist: Apply site reliability engineering (SRE) practices to data pipeline and dataops. Define service level objectives, measure pipeline error rates, and invest in infrastructure improvements when required.

6. Communicate design principles

Many organizations will deploy future-of-work AI agents into their enterprise and SaaS platforms. But as more organizations seek AI competitive advantages, they will consider developing AI agents tailored to proprietary workflows and customer experiences. Architects and delivery leaders must define and communicate design principles because addressing an AI agent’s technical debt can become expensive.

Nikhil Mungel, head of AI at Cribl, recommends several design principles:

• Validate access rights as early as possible in the inference pipeline. If unwanted data reaches the context stage, there’s a high chance it will surface in the agent’s output.
• Maintain immutable audit logs with all agent actions and corresponding human approvals.
• Use guardrails and adversarial testing to ensure agents stay within their intended scope.
• Develop a collection of narrowly scoped agents that collaborate, as this is often safer and more reliable than a single, broad-purpose agent, which may be easier for an adversary to mislead.

Pranava Adduri, CTO and co-founder of Bedrock Data, adds these AI agent design principles for ensuring agents behave predictably.

• Programmatic logic is tested.
• Prompts are stable against defined evals.
• The systems agents draw context from are continuously validated as trustworthy.
• Agents are mapped to a data bill of materials and to connected MCP or A2A systems.

According to Chris Mahl, CEO of Pryon, if your agent can’t remember what it learned yesterday, it isn’t ready for production. “One critical criterion that’s often overlooked is the agent’s memory architecture, and your system must have proper multi-tier caching, including query cache, embedding cache, and response cache, so it actually learns from usage. Without conversation preservation and cross-session context retention, your agent basically has amnesia, which kills data quality and user trust. Test whether the agent maintains semantic relationships across sessions, recalls relevant context from previous interactions, and how it handles memory constraints.”

Checklist: Look for ways to extend your organization’s non-negotiables in devops and data governance, then create development principles specific to AI agent development.

7. Enforce security non-negotiables

Organizations define non-negotiables, and agile development teams will document AI agent non-functional requirements. But IT leaders will face pressure to break some rules to deploy to production faster. There are significant risks from shadow AI and rogue AI agents, so expect CISOs to enforce their security non-negotiables, especially regarding how AI models utilize sensitive data.

“The most common mistakes around deploying agents fall into three key categories: sensitive data exposure, access mismanagement, and a lack of policy enforcement,” says Elad Schulman, CEO and co-founder of Lasso Security. “Companies must define which tasks AI agents can perform independently and which demand human oversight, especially when handling sensitive data or critical operations. Principles such as least privilege, real-time policy enforcement, and full observability must be enforced from day one, and not as bolted-on protections after deployment.”

Checklist: Use AI risk management frameworks such as NIST, SAIF, and AICM. When developing security requirements, consult practices from Microsoft, MIT, and SANS.

8. Scale AI-ready infrastructure

AI agents are a hybrid of dataops, data management, machine learning models, and web service capabilities. Even if your organization applied platform engineering best practices, there’s a good chance that AI agents will require new architecture and security requirements.

Kevin Cochrane, CMO of Vultr, recommends these multi-layered protections to scale and secure an AI-first infrastructure:

• Tenant isolation and confidential computing.
• End-to-end encryption of data in transit and at rest.
• Robust access controls and identity management.
• Model-level safeguards like versioning, adversarial resistance, and usage boundaries.

“By integrating these layers with observability, monitoring, and user feedback loops, organizations can achieve ‘release-readiness’ and turn autonomous AI experimentation into safe, scalable enterprise impact,” says Cochrane.

Checklist: Use reference architectures from AWS, Azure, and Google Cloud as starting points.

9. Standardize observability, testing, and monitoring

I received many recommendations related to observability standards, robust testing, and comprehensive monitoring of AI agents.

• Observability: “Achieving agentic AI readiness requires more than basic telemetry—it demands complete visibility and continuous tracking of every model call, tool invocation, and workflow step,” says Michael Whetten, SVP of product at Datadog. “By pairing end-to-end tracing, latency and error tracking, and granular telemetry with experimentation frameworks and rapid user-feedback loops, organizations quickly identify regressions, validate improvements, control costs, and strengthen reliability and safety.”
• Automated testing: Rishi Rana, CEO of Cyara, says, “Teams must treat testing like a trust stress test: Validate data quality, intent accuracy, output consistency, and compliance continuously to catch failures before they reach users. Testing should cover edge cases, conversational flows, and human error scenarios, while structured feedback loops let agents adapt safely in the real world.
• Monitoring: David Talby, CEO of Pacific AI, says, “Post-release, continuous monitoring and feedback loops are essential to detect drift, bias, or safety issues as conditions change. A mature governance checklist should include data quality validation, security guardrails, automated regression testing, user feedback capture, and documented audit trails to sustain trust and compliance across the AI lifecycle.”

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

10. Create end-user feedback loops

Once an AI agent is deployed to production, even if it’s to a small beta testing group, the team should have tools and a process to capture feedback.

“The most effective teams now use custom LLM judges and domain-specific evaluators to score agents against real business criteria before production,” says Craig Wiley, senior director of product management at Databricks. “After building effective evaluations, teams need to monitor how performance changes across model updates and system modifications and provide human-in-the-loop feedback to turn evaluation data into continuous improvement.”

Checklist: Require an automated process for AI agents to capture feedback and improve the underlying LLM and reasoning models.

Conclusion

AI agents are far greater than the sum of their data practices, AI models, and automation capabilities. Todd Olson, CEO and co-founder of Pendo, says AI requires strong product development practices to retain user trust. “We do a ton of experimentation to drive continuous improvements, leveraging both qualitative user feedback to understand what users think of the experience and agent analytics to understand how users engage with an agent, what outcomes it drives, and whether it delivers real value.”

For organizations looking to excel at delivering business value from AI agents, adopting a product-driven organization is key to driving transformation.