Roadmap to Agentic AI Implementation

Written by Dr. Chantal Spleiss of the CSA AI Governance and Compliance Working Group.

 

Imagine there's no error
It's easy if you try
No conflict in the circuits
Just agents standing by
Imagine all the agents
Talking all the time...

Imagine there's no failure
It isn’t hard to do
No warnings or exceptions
No oversight from you
Imagine all the agents
Working hand in hand…

You may say I’m a dreamer
But I’m not the only one
I hope we learn to listen
Before the chaos has begun

Imagine there’s no asking
No interface to break
No humans in the feedback
Just silence in our wake
Imagine all decisions
Made before we knew...

You may say I’m a dreamer
But I’m not the only one
If agents run our future
Let’s give them more than ones and none

 

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This isn’t science fiction—it’s a technical shift already unfolding. Agentic AI systems don’t just talk; they act. And when their seamless logic breaks down in human terms, we’re not just out of the loop—we’re out of the conversation.

Let’s explore how we can implement agentic AI safely[1] and effectively in organisations. A recent article in the Harvard Business Review shows that successful AI deployment isn’t about having the latest tech  but to bring diverse expertise together to make the smartest possible decisions. So, what’s the difference between a narrow AI and an agentic AI?

Narrow AI is already embedded in industry—for example, in quality control for sorting defects or flagging anomalies. Agentic AI, however, adds a new layer: it can perceive its environment (e.g., stock levels), apply goal-oriented planning (maintain stock thresholds), and act independently to fulfill its objective (place an order). That’s in the digital realm. Add agentic cobots[2] or embodied systems (like robots[3]), and suddenly the safety landscape becomes significantly more complex.

This blog tackles three key questions:

• How is the safety of digital or physical agents evaluated?
• How is the safe operation of agent groups ensured?
• How are agents successfully integrated into business processes?

 

Evaluating the Safety of Agentic AI

To implement safe systems, we must first recognize and mitigate risk. Agentic AI can be digital or embodied, but both can impact the physical world. More importantly, they can adapt. Learning systems evolve over time – and so must our ability to assess them.

Before diving deeper, Figure 1 illustrates a simplified view of an AI agent. It perceives its environment through data or sensors, plans based on training, acts, and then learns from the result – usually through reinforcement learning.

Figure 1: Simplified AI Agent

The recent paper AI Risk Management – Thinking Beyond Regulatory Bounderies[4] explores the challenge to recognize and mitigate risk in depth. It emphasizes the importance of curiosity, critical thinking, and investigative abilities. The second part of the paper offers concrete and thought-provoking questions, useful for engineers, auditors, and anyone evaluating the reliability of intelligent systems.

When agentic AI interacts with real-world systems, lifecycle-wide risk assessments aren’t optional: they’re essential.

 

Safe Deployment of Groups of AI Agents

Imagine they always make the right decision—alone and together. Now imagine they don’t.

Take the 2008 crash of the Spirit of Kansas B2 Spirit stealth bomber. A $1.4B asset lost on takeoff due to two key failures: human miscommunication around maintenance and faulty sensor logic that failed to reconcile conflicting inputs. Moisture-laden sensors fed corrupted data into the flight control system, which defaulted to the wrong decision.

The lesson? Even in highly engineered systems, consensus of error is a real risk. Documentation matters. Oversight is critical. And automation still requires verification.

In agentic AI, risks scale fast. Even when two agents “communicate,” shared context isn’t guaranteed. When systems shift to machine-native interfaces, human oversight becomes harder, especially when those interfaces are flawed or inaccessible (intentionally or unintentionally).

Risk management must evolve. It’s no longer just about one system’s safety—it’s about interconnected systems, working together and with humans. Risks now scale exponentially. That demands proactive risk management to ensure business continuity, not reactive fixes.

 

Successful Implementation of AI Agents into Business Processes

The tech is ready. But is the business?

With a considerable percentage of deployments still failing, we need to look inward at business process maturity. Deploying agentic AI isn’t plug-and-play. It requires digital readiness: well-structured data flows, modular process design, and clearly defined escalation paths – especially where human supervision remains necessary.

Business processes must be adaptable. That means building modular frameworks that can handle agentic behavior, including when agents exceed confidence thresholds and defer decisions.

Equally critical: synchronizing operational technology (OT) with information technology (IT). Without this alignment, deployments risk being neither safe nor secure.

The white paper Dynamic Process Landscape: A Strategic Guide to Successful AI Implementation offers a grounded approach to building AI-ready systems – from the ground up.

 

Conclusion

As intelligent systems grow in complexity, their interactions multiply that complexity exponentially – and so does risk. Julius Caesar’s “Divide et Impera”[5] reminds us: to govern complexity, we must first break it down.

Systems should be modular in function and adaptable in process. The two referenced papers offer not just technical guidance, but the mindset shift required to evaluate agentic AI holistically, throughout its lifecycle.

 

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Notes

[1] Safety is outcome-focused. Security is defense-focused and not addressed in this blog.

[2] Cobots are designed to work alongside humans in a shared space. Goal: augmentation.

[3]  Robots work in spaces isolated from humans. Goal: automation.

[4] White paper published by CSA – 13. November 2024

[5] Divide and conquer.