10 best practices for getting agentic AI apps and agents to production

Article after article and study after study illustrate that while organizations are having some initial successes with AI Chatbots, almost universally companies are struggling to get value from agentic AI, with most still stuck experimenting with these powerful capabilities.

In this blog, I will share a number of best practices that leading organizations are using to scale production deployment of agentic AI and ensure their initiatives deliver sustainable business value rather than becoming stranded in perpetual pilot mode.

Best practices for scaling AI apps and agents

1. Start with an AI strategy, not technology

Align agentic AI initiatives with strategic business objectives.

Successful implementations begin with clear answers to fundamental questions:

• What specific business outcomes are we trying to achieve?
• How will agentic AI advance our strategic priorities?
• What metrics will define success?

Implementation steps:

1. Conduct strategy workshops with business and technology leadership
2. Define specific, measurable outcomes tied to business objectives
3. Create a value roadmap linking technology capabilities to business impact
4. Establish quantifiable success metrics beyond technical performance

Common pitfall to avoid: Starting with technology capabilities rather than business problems. This leads to "solutions in search of problems" that struggle to gain organizational support.

2. Adopt a structured evaluation process for AI use cases

Implement a consistent methodology for identifying and assessing potential use cases.

The effort vs. value framework provides this structure, but effective application requires:

• Cross-functional input to identify opportunities
• Consistent evaluation criteria applied across use cases
• Regular reassessment as capabilities and priorities evolve

Implementation steps:

1. Establish a dedicated evaluation team with business and technical representation
2. Create standardized templates for use case submission and evaluation
3. Implement scoring guidelines that ensure consistent assessment
4. Develop a dynamic roadmap that evolves based on learning and changing priorities

Common pitfall to avoid: Ad hoc evaluation approaches. This that lead to initiative selection based on organizational politics rather than genuine value potential.

3. Build AI apps and agents for production from the start

Design every implementation with production requirements in mind.

Many agentic AI initiatives fail when moving from pilot to production because production considerations weren't incorporated from the beginning:

• Security and compliance requirements
• Integration with enterprise systems
• Performance at scale
• Monitoring and observability
• Operational handoff processes

Implementation steps:

1. Include operations and security teams in initial design discussions
2. Create architecture designs that address enterprise requirements
3. Establish non-functional requirements alongside functional specifications
4. Develop production deployment checklists that inform initial implementation

Common pitfall to avoid: Retrofitting security, compliance, and operational considerations after the initial development. This creates significant delays and often requires substantial rework.

4. Implement comprehensive AI governance

Establish clear governance processes for agentic AI development and operation.

Autonomous agents require appropriate oversight to ensure they operate safely, ethically, and effectively:

• Decision authority frameworks
• Risk assessment protocols
• Audit and compliance processes
• Performance monitoring
• Escalation procedures

Implementation steps:

1. Define decision boundaries for autonomous actions
2. Implement human-in-the-loop protocols for higher-risk scenarios
3. Create monitoring dashboards for agent activities and outcomes
4. Establish regular review processes to evaluate agent performance
5. Develop clear escalation paths for exceptional situations

Common pitfall to avoid: Insufficient governance. This will create organizational resistance and regulatory risks that can derail implementation.

5. Focus on an integration architecture

Design a robust integration architecture that enables agents to work across systems.

Agentic AI's value comes from its ability to operate across organizational boundaries and systems. This requires:

• Secure API strategies
• Identity and access management
• Data exchange standards
• Event notification mechanisms
• State management across processes

Implementation steps:

1. Inventory existing integration capabilities and gaps
2. Implement API management practices that support agent operations
3. Develop standardized approaches for authentication and authorization
4. Create standards for data exchange between agents and systems
5. Design stateful workflow mechanisms for long-running processes

Common pitfall to avoid: Underestimating integration complexity. This will lead to agents with limited capabilities or brittle implementations that fail when conditions change.

6. Prioritize user experience design

Design thoughtful human-AI interaction models that promote adoption and effectiveness.

User experience determines whether agentic AI becomes an integral part of work processes or an unused capability:

• Natural interaction patterns
• Appropriate transparency
• Trust-building mechanisms
• Effective handoffs between AI and humans
• User feedback channels

Implementation steps:

1. Conduct user research to understand workflow contexts
2. Design interaction patterns that match user expectations and needs
3. Create transparency mechanisms that explain agent decisions and actions
4. Implement feedback loops that capture user input and improve agent performance
5. Develop metrics that measure user adoption and satisfaction

Common pitfall to avoid: Technology-first implementations. This neglects the human factors that determine whether agents will be trusted and used effectively.

7. Build internal capabilities progressively

Develop internal expertise through deliberate skill-building and knowledge transfer.

Sustainable agentic AI implementation requires organizations to build internal capabilities:

• Technical skills for agent development and maintenance
• Functional expertise in identifying and evaluating use cases
• Operational knowledge for running agent-powered processes
• Governance capabilities for oversight and risk management

Implementation steps:

1. Create role-specific training programs for different stakeholder groups
2. Implement paired development approaches that combine external and internal resources
3. Establish centers of excellence that capture and share best practices
4. Develop internal communities of practice around agentic AI
5. Create knowledge management processes that preserve implementation learning

Common pitfall to avoid: Over-reliance on external resources. This creates dependency and limits organizational learning.

8. Implement continuous improvement processes

Establish mechanisms for ongoing evaluation and enhancement of agentic AI implementations.

Agentic AI effectiveness improves through:

• Performance monitoring and analysis
• User feedback collection and application
• Regular reviews of business impact
• Systematic enhancement planning
• Continuous learning from operational data

Implementation steps:

1. Implement comprehensive monitoring and analytics for agent performance
2. Create structured feedback channels for users and stakeholders
3. Establish regular review cadences for evaluating business impact
4. Develop enhancement roadmaps based on operational insights
5. Build learning loops that improve agent effectiveness over time

Common pitfall to avoid: Treating agent deployment as a "set and forget" implementation rather than a continuously evolving capability.

9. Address change management proactively

Develop comprehensive approaches to managing the organizational changes agentic AI introduces.

Successful implementations recognize that agentic AI changes how work gets done:

• Job role evolution
• Process transformation
• Skill requirement shifts
• Cultural adaptation
• New collaborative models

Implementation steps:

1. Analyze impact on roles and responsibilities
2. Develop communication strategies that address concerns and build support
3. Create training programs that help employees adapt to new ways of working
4. Identify and empower champions who can advocate for adoption
5. Establish feedback mechanisms that capture implementation challenges

Common pitfall to avoid: Underinvesting in change management. This will create resistance that undermines even technically successful implementations.

10. Scale AI through reusable components

Architect for reusability to accelerate deployment and ensure consistency.

Scalable implementation depends on:

• Modular agent components
• Reusable integration patterns
• Consistent development approaches
• Shared best practices
• Cross-functional collaboration

Implementation steps:

1. Establish component libraries for common agent capabilities
2. Create reusable integration adapters for enterprise systems
3. Develop design patterns that guide consistent implementation
4. Implement knowledge-sharing mechanisms across teams
5. Create cross-functional working groups to drive standardization

Common pitfall to avoid: Siloed implementation approaches. This will create redundant work and inconsistent solutions that increase maintenance costs/complexity and limit scalability.

By following these best practices, organizations can successfully transition from agentic AI experimentation to enterprise-scale deployment that delivers sustainable business value. The key lies in approaching implementation comprehensively—addressing technology, process, people, and governance dimensions in a coordinated manner that builds capability progressively while delivering measurable outcomes.

CIO GUIDE

The CIO's guide to agentic AI in the enterprise

From assessing potential use cases through an effort-versus-value framework to establishing best practices for moving from experimentation to production, this guide provides a strategic roadmap for CIOs looking to unlock the full potential of autonomous AI agents.

DOWNLOAD THE GUIDE