Key best practices for successful agentic AI implementation.

Agentic AI is quickly emerging as one of the most transformative technologies in artificial intelligence. Unlike traditional or generative AI models that specialize in producing outputs such as text, images, or code, agentic AI is designed for autonomous action. It can reason through complex problems, plan steps toward goals, and execute tasks across systems and tools, often with minimal human intervention.

This leap in capability introduces enormous opportunities, from automated IT operations to supply chain optimization. But it also brings new challenges. Left unchecked, autonomous systems can behave unpredictably, expose organizations to compliance risks, or create gaps in accountability.

Implementing agentic AI at scale requires more than just powerful models. It demands a thoughtful framework of best practices to ensure systems remain safe, reliable, and aligned with organizational goals. The following best practices establish the guardrails that transform agentic AI from a promising capability into a trusted, enterprise-ready solution.

1. Trusted, Flexible Data Foundation

Before launching agentic AI initiatives, align with your peers on the specific business use cases, the expected ROI, and the data sets required to support them.

It is also critical to establish a few foundational principles that can guide your data foundation approach. The most effective strategies tend to reflect these five core ideas:

• Continuous ingestion, data quality management, and mobilization for different types of data: For fresh, reliable data to serve AI agents on a continuous basis, the data ingestion and quality management need to be continuous, too. It also requires ongoing monitoring of data freshness and quality—not just during onboarding, but as a built-in, automated process to prevent silent drift over time. The true business context for AI means maintaining always-current, trusted profiles across all critical business data—customers, products, suppliers, and more with their preferences, relationships, and interactions across systems. Any agent built on siloed data—in a single application, for example—lacks the full context to get it right.
• Built-in automation: Manual data quality methods don’t scale. Look for a solution that continuously improves data quality using AI-augmented automation.
• Speed to value: If it takes months or years to prepare data, your AI initiatives stall. That’s why a DIY approach for getting data ready—for integration, unification, or data quality—isn’t the answer. It’s slow, costly, and puts you behind competitors already moving fast with agentic AI.
• Unified, flexible data model: Your data model should support your enterprise with a unified approach, rather than a siloed view of your apps or business processes. You also need the agility to add new data, attributes, and relationships as your business and AI evolve.
• Collaboration by design: Because agentic AI is infused into business workflows, the best solutions should be designed to connect business and IT teams—enabling shared governance, fast iteration, and aligned outcomes. A highly technical UI and the need for coding with specialized skills limit the necessary involvement of the business teams. Any use case should also be collectively tracked for specific KPI improvements to demonstrate ROI and to continuously refine goals, as well as related data and AI requirements.

2. Coordination and Orchestration

Effective orchestration is what transforms agentic AI from an experimental technology into a dependable enterprise solution. By carefully managing how and when AI agents interact with systems, tools, workflows, and people, orchestration ensures that actions are aligned with business priorities and carried out in a structured, accountable way. This includes setting rules for task delegation, defining escalation paths for exceptions, and coordinating across multiple platforms so that AI-driven processes don’t operate in silos.

This coordination reduces risks by preventing conflicting actions, increases transparency by documenting agent decisions and outcomes, and provides a foundation for governance by embedding oversight into the operational fabric. With orchestration in place, organizations gain clearer oversight of agentic AI operations, making it easier to track performance, audit decision-making, and intervene when human judgment is required. Over time, robust orchestration also improves scalability, allowing enterprises to safely expand agentic AI use across different departments and business functions without losing control or accountability.

3. Governance and Accountability

Strong governance frameworks are essential for deploying agentic AI responsibly. These frameworks define clear roles and responsibilities for stakeholders, ensuring that business leaders and technical teams understand their part in overseeing AI systems. It also sets ethical guidelines for system usage, such as establishing boundaries around fairness, bias mitigation, and acceptable decision-making authority. In addition, governance establishes compliance requirements with regulations at both the industry and regional level, reducing the risk of legal or reputational harm.

Regular audits, monitoring mechanisms, and clear accountability structures go further by creating transparency in how AI agents operate. These measures make it possible to trace actions back to their origin, evaluate whether outcomes align with organizational policies, and intervene when necessary. Together, these safeguards not only help businesses maintain trust and ethical standards in AI adoption, but also provide a strong foundation for scaling agentic AI into enterprise environments with confidence.

4. Human Oversight

Human-in-the-loop practices bring critical oversight to AI-driven processes. By involving people in key checkpoints such as validating major outputs, escalating complex issues, or approving high-impact actions, organizations balance automation with accountability. This oversight not only acts as a safeguard against technical errors but also ensures that nuanced business rules, ethical considerations, and contextual factors are applied where AI may fall short. This approach helps catch errors and maintain alignment with human judgment and business context.

Over time, human feedback also strengthens AI systems by creating adaptive learning loops that improve performance and reliability, as validated corrections and real-world insights are fed back into the models. By embedding humans at critical decision points, organizations build trust in agentic AI and ensure that outcomes remain both technically accurate and socially responsible.

5. Security and Compliance Safeguards

Security is a cornerstone of safe AI adoption. Protecting sensitive data requires a mix of encryption, access controls, and vulnerability testing to defend against threats. These measures help safeguard against malicious actors, prevent unauthorized access, and protect critical business information from breaches that could undermine trust.

At the same time, organizations must enforce compliance with data privacy and usage regulations to reduce ethical risks. This includes adhering to standards such as GDPR, HIPAA, or industry-specific mandates, depending on the context in which AI operates. Strong compliance practices not only prevent legal penalties but also reassure customers and partners that their data is handled responsibly.

Establishing transparent guidelines around how data is stored, shared, and used ensures both legal and ethical integrity. By embedding security and compliance safeguards into every stage of agentic AI deployment, organizations create resilient systems that protect sensitive information and foster long-term trust in their AI solutions.

6. Testing and Validation

Before deploying agentic AI in real-world scenarios, rigorous testing is critical. Systems should be evaluated across both typical and unexpected conditions to identify vulnerabilities or unintended behaviors. This includes stress testing under high-volume data loads and introducing adversarial inputs to see how the system responds to anomalies or malicious attempts at manipulation.

This validation phase allows organizations to refine models, improve resilience, and ensure that AI agents operate predictably in diverse and dynamic environments. Testing should also extend beyond technical performance to include usability, integration with existing systems, and adherence to compliance standards. By conducting multiple rounds of testing in controlled environments, businesses can uncover weaknesses early and reduce risks before full-scale deployment. Ultimately, rigorous validation provides confidence that agentic AI will act reliably, ethically, and in alignment with organizational goals once operationalized.

7. Continuous Monitoring and Evolution

Agentic AI is not a “set it and forget it” solution. Ongoing monitoring, frequent updates, and iterative improvements are needed to keep systems effective, secure, and aligned with organizational goals. Monitoring should cover not only system performance but also ethical considerations, data quality, and potential bias in outputs. Frequent updates, whether to patch vulnerabilities, integrate new tools, or refresh training data help maintain accuracy and resilience as conditions evolve.

Leveraging performance metrics, user feedback, and real-world outcomes enables businesses to refine models and adapt quickly to new challenges. This cycle of continuous learning ensures agentic AI remains a long-term driver of innovation and operational efficiency, while also reducing risks and strengthening trust among users and stakeholders.

Why Agentic AI Best Practices Matter

Implementing agentic AI without a structured set of best practices can introduce significant risks, ranging from operational errors to ethical lapses. Unlike traditional software, agentic AI systems operate with a degree of autonomy as they plan, make decisions, and execute tasks across multiple tools and platforms. Without clear guardrails, these autonomous actions can lead to unintended outcomes, misaligned priorities, or even security breaches. Best practices provide a framework for safely guiding agentic AI behavior, ensuring that it remains aligned with organizational goals, regulatory requirements, and ethical standards. By defining governance, human oversight, and accountability mechanisms, enterprises can reduce risks while maximizing the technology’s potential.

Agentic AI best practices are also critical for building trust and reliability. Organizations rely on AI to handle complex workflows and make decisions in real time, which means errors or misjudgments can have cascading effects. Through rigorous testing, validation, and continuous monitoring, businesses can identify vulnerabilities early, refine models, and adapt systems to evolving conditions. Practices such as orchestration, security safeguards, and human-in-the-loop oversight not only protect against technical failures but also ensure that AI outputs remain transparent, auditable, and ethically responsible. Ultimately, following best practices transforms agentic AI from an experimental tool into a dependable, enterprise-ready solution capable of driving innovation, efficiency, and informed decision-making.

Empowering Agentic AI with Trusted Data from Reltio

Adopting best practices for agentic AI is not just about safeguarding innovation, it’s about ensuring that AI systems are fueled by trusted, high-quality data. Agentic AI can only perform reliably when the information it reasons, plans, and acts upon is accurate, consistent, and up to date. This is where the Reltio Data Cloud, its Master Data Management (MDM) capabilities and multidomain 360-degree profiles with relationship intelligence come into play. By unifying, cleansing, and enriching data across systems, Reltio provides the trusted foundation that agentic AI needs to operate with precision and accountability. When combined with best practices, organizations can unlock the full potential of agentic AI while maintaining transparency and compliance. Building agentic AI initiatives with Reltio Data Cloud and Reltio Agentflow allows enterprises to not only innovate but do so with confidence, turning AI into a strategic partner for smarter, faster, and more effective decision-making.