Agentic AI: Balancing Risk With Innovation

Artificial intelligence (AI) is evolving into something far more autonomous than merely a useful tool. Known as agentic AI, these systems can reason, plan and act independently. They are being used across industries to drive automation, efficiency and real-time decision-making. However, a question remains: How much power should enterprises delegate to AI, and where do we as humans draw the line?

Understanding agentic AI decision-making

When determining how much autonomy AI should have, differentiate between various levels of AI decision-making:

• Rule-based AI: Operates strictly on predefined logic and does not adapt beyond its programmed parameters
• Predictive AI: Uses data-driven models to anticipate outcomes but still relies on human intervention for execution

Autonomous AI agents: Acts independently, making and executing decisions based on their reasoning and learned experiences

The decision-making spectrum includes:

1. Fully manual AI: Provides insights, but human oversight is required for every action
2. Human-in-the-loop AI: Suggests decisions, and humans approve or override them
3. Fully autonomous AI: Makes and implements decisions without human intervention

It is important to evaluate your industry, security needs and regulatory environment when selecting the appropriate level of AI autonomy. Consider also that AI’s ability to self-learn and adapt to new data complicates governance further. You will want to also reflect on how much flexibility you allow AI models when refining their decision-making processes without human intervention.

The risks of giving AI too much decision-making power

When determining how much decision-making power to give to agentic AI, there are significant risks to consider: 

1. Security risks

One of the biggest risks of autonomous AI agents is the potential to become prime targets for cyber threats. Without proper oversight, attackers could manipulate AI-driven decisions to tap into confidential information, exploit vulnerabilities and disrupt operations. The risk is especially significant if bad actors take control over critical AI processes. Strong cybersecurity standards and processes are paramount if you allow AI to take on more operational responsibilities.

2. Compliance challenges

In highly regulated industries like healthcare and finance, AI-driven decisions must comply with laws (e.g., GDPR, HIPAA). The challenge here is AI’s evolving ability to interpret and comply with complex legal frameworks without human oversight. As regulatory AI frameworks emerge globally, there is an even larger need to document AI decision-making processes and ensure compliance with relevant security standards. 

3. Data integrity and privacy concerns

Any decision made by AI must be transparent and traceable to prevent data misuse. To ensure ethical data handling and compliance with privacy standards, these systems must be designed with built-in safeguards. Consider also how you’ll monitor the use of personal data in AI decision-making. 

4. Unintended consequences

There is a risk that fully autonomous AI agents may generate outcomes or deliverables that are difficult to predict or justify. This can cause ethical issues when AI makes a decision that affects customers, employees, stakeholders or the community. Say, for example, an AI-driven hiring system autonomously filtered out candidates based on flawed data sets. This could introduce an unintentional bias and lead to reputational and legal risks.

The case for controlled autonomy: security, compliance, and observability

Frameworks can enable AI innovation without compromising security and compliance. The following best practices are useful for unlocking agentic AI’s capabilities and maintaining your obligations to stakeholders and regulators.

1. Security-first AI governance

To take a security-first approach to AI governance, implement the following:

• Stringent access controls
• Encryption
• Monitoring mechanisms 
• Integrated AI security frameworks
• Continual assessment AI behavior
• Use of identity and access management (IAM) solutions

2. The role of explainability

AI decisions should be explainable so your organization can understand how and why your AI system came to a conclusion. Leveraging transparent AI models will help mitigate risks by ensuring accountability and traceability. Consider using explainability models, such as SHAP (Shapley Additive Explanations) and LIME (Local Interpretable Model-Agnostic Explanations) to better understand AI decision-making processes.

3. Observability and risk assessment

AI observability tools provide visibility into AI-driven decisions. By tracking agent behavior, you can detect anomalies, prevent bias and refine AI performance. Lean on continuous AI risk assessment frameworks, such as NIST’s AI Risk Management Framework. 

4. Human oversight models

You’ll also want to pre-define scenarios where human intervention is mandatory to maintain control. You can also adopt hybrid governance models where AI makes preliminary decisions, but final approvals remain with humans. Make sure your AI governance is adaptive. That way you can adjust oversight levels based on evolving regulatory landscapes and enterprise risk tolerance.

Future-proofing AI decision-making: Balancing innovation and control

It’s crucial to adopt forward-looking governance strategies, such as those outlined below.

1. Evolving AI governance frameworks: Regulatory bodies across the globe are establishing AI governance standards. Proactively monitor and align with emerging regulations to ensure compliance and prevent legal risks.
2. Automating AI oversight: As there are more advancements in AI-driven auditing and monitoring tools, there is room to automate some oversight functions. However, consider where human oversight will still be required to maintain transparency. 
3. Open-source AI solutions: If your organization is looking for greater control over AI systems, you may prefer open-source AI platform such as SUSE AI. These solutions provide enhanced transparency, security and flexibility, allowing you to tailor AI governance models to specific needs.

As you continue to refine your AI governance approach, access Forrester’s AI Predictions for 2025 to explore key trends shaping the future of AI decision-making.