Agentic AI is more than just the next step in automation. It’s a shift toward systems that don’t just respond to commands – they reason, decide and act with a blend of analytics and embedded governance.
This marks an evolution in how we think about AI maturity: we are moving from passive tools to proactive agents that operate under guidance but with growing independence.
It’s a powerful vision, but one that demands intentional design. Autonomy in AI can’t come at the expense of trust, especially in high-stakes decisions. And that’s where accountability, robustness and privacy must move from abstract ideals to engineering principles.
Agents as orchestrators – not just executors
In agentic systems, we don’t script every step. Instead, we define the given objectives and are empowered to decide how best to achieve them. This kind of autonomy works well in low-risk domains, like marketing. But in high-stakes areas such as credit decisions or health care, the balance must shift, bringing human oversight and governance to the forefront.
That’s why governance isn’t optional – it must be built in from the start.
As agentic AI grows more capable, trust must be engineered into the system, with governance embedded as a design principle rather than added as an afterthought.
SAS’ six core principles of trustworthy AI
In the first post of this blog series, Vrushali Sawant explored the first three of SAS’ six core principles: transparency, inclusivity, and human-centricity. In this post, we turn to the remaining three: accountability, robustness and privacy and security.
Accountability: Clear ownership, clear responsibility
For agentic AI to be trusted, organizations must clearly define who is responsible when AI systems make decisions or take action. Without accountability, it becomes too easy to shift blame to “the system” when outcomes fall short.
That’s the foundation of trustworthy agentic AI. It’s not just about tracking who built a model. It’s about embedding accountability throughout the lifecycle:
- Governance frameworks that define who is responsible for agent behavior – whether it’s the developer, model owner, business lead or the organization itself.
- Model documentation and registration to show how models were developed, what data they rely on and their intended use.
- Audit trails and decision logging that trace how agents reach decisions – essential for legal, ethical, and business accountability.
- Autonomy controls that let organizations decide which tasks agents may perform independently and where human oversight is mandatory.
- Lifecycle monitoring tools that enable continuous performance review and refinement over time.
But accountability isn’t just about assigning blame – it’s about embedding responsibility, so AI systems align with organizational values and ethical standards. It means creating a culture where human judgment and ethical principles remain central, even as agents take on more responsibility.
We must not only ask: Could an agent do this? We must also ask: Should it?
Autonomous agents can handle many routine, data-driven tasks. But when decisions involve fairness, risk or human well-being, humans must remain in the loop. Autonomy should be carefully scoped, and every system must have clear rules for when and how oversight is applied.
As AI agents take on more responsibility, robustness becomes the next critical foundation, ensuring these systems can perform reliably, even as conditions shift.
Robustness: Building for the real world
Agentic AI systems operate in complex, dynamic environments where data shifts, inputs could be unexpected, and high-pressure decisions are the norm. That’s why robustness isn’t just a technical aspiration—it’s a requirement for trust.
This includes:
- Rigorous testing and validation, including edge cases and stress scenarios that simulate real-world conditions.
- Continuous performance monitoring to detect anomalies, model drift or unwanted decision patterns.
- Automated alerts and safeguards that flag deviations quickly, especially critical when agents act with limited supervision.
- Version control and rollback so that agent behavior and model logic can be traced and restored if needed.
- Adaptive learning mechanisms that allow systems to adjust to changing data while maintaining safety and alignment with goals.
Robustness means building AI that behaves safely and predictably, even as it encounters the unexpected. It allows organizations to scale AI with confidence, knowing their systems are prepared not only for what’s likely but also for what’s not.
Just as robustness ensures agentic AI can perform reliably under pressure, privacy and security ensure it performs responsibly, especially when handling sensitive data or decisions that affect individuals.
Privacy and security: Protecting what matters most
As agentic AI systems become more autonomous, they increasingly handle sensitive data and make decision that can impact individual rights, reputations, and business integrity.
To be successful, you need privacy and security features that help your organization deploy agentic AI while respecting data subject rights. For example:
- Data masking and redaction to anonymize personally identifiable information (PII) during training or decision-making.
- Data tagging and classification that labels data as “PII”, "confidential", or "internal use only", and restricts access accordingly.
- Role-based access control (RBAC) allows agents and users to access only what they’re authorized to based on roles, context, and purpose.
- Audit trails and data lineage to track who (or which agent) accessed what data, when, and why – critical for accountability and compliance.
By embedding these safeguards, we help organizations design agentic AI that is not only effective but ethically grounded and privacy-conscious. These controls reduce risk while building trust with users, regulators and regulators alike.
How SAS® Viya® supports trusted agentic AI
All of these principles – accountability, robustness, privacy – can be hard to orchestrate at scale. That’s where platforms like SAS Viya come in.
SAS Viya supports this vision by integrating deterministic models, machine learning, and large language models (LLMs) into a unified orchestration layer. This enables organizations to deploy intelligent agents that can act autonomously when appropriate, while still ensuring transparency and human oversight when the stakes are high.
SAS Viya includes mechanisms to detect bias, evaluate fairness and provide full transparency into how decisions are made. It also enables organizations to configure how much autonomy agents are granted and ensures that agents can draw from internal data sources, not public internet content. For instance, retrieval-augmented generation (RAG) setups allow agents to access enterprise-specific knowledge securely and accurately.
With Viya, you can set the rules and make sure your agents follow them.
A continuing journey: Designing agentic AI we can trust
Trustworthy agentic AI isn’t the product of good intentions – it’s the result of deliberate design. It requires embedding principles like accountability, robustness, as well as privacy and security alongside transparency, inclusivity and human-centricity into every layer of AI development and deployment.
These aren’t constraints on innovation; they’re its foundation.
As AI agents take on more complex, autonomous roles, they must be able to act with purpose, adapt with resilience, and operate with integrity. That means creating systems that don’t just generate insights or automate tasks, but ones that earn trust, reflect human value and withstand real-world scrutiny.
At SAS, we believe governance is a catalyst for innovation. With platforms like Viya, organizations can build agentic AI that is not only intelligent but also responsible, explainable and secure.
The journey is ongoing. But with the right principles in place, we can unlock the full potential of agentic AI – not just to work for us, but to work with us. Responsibly.
