Unlock Business Value with AI Agents: Doing It The Right Way

AI agents are quickly becoming a crucial part of enterprise workflows, giving you the power to boost decision-making speed and drive efficiency like never before. But with these benefits come with somewhat “new” responsibilities. To truly capture the AI agents’ benefits while managing the risks, businesses organizations will need to update how to think about oversight, governance, and responsible AI practices.

By adopting a transparent approach to managing AI agents, businesses organizations can stay aligned with broader digital strategies, maintain compliance, and keep trust at the core of their operations.

Why AI Agents Are Gaining Momentum?

AI agents have captured the attention of many businesses, and for good reason. The promises are AI agents can act as a sophisticated digital workforce, capable of handling complex tasks on their own. Many companies are already putting them to work across applications and platforms. This means change is happening not just at the workflows level, but the very nature of workforce management.

Solutions like Salesforce AgentForce are leading the way, helping businesses create and manage these autonomous AI agents, operate across different domains (e.g. sales, customer services, etc.) more easily. However, as you give AI agents more autonomy, their potential risks increase too. That’s why keeping a strong “human at the helm” approach is more important than ever. Unlike traditional systems, AI agents dynamically adapt to changing environments, meaning hardcoded logic isn’t just impractical; it can be impossible.

Now that adoption is moving beyond early interest, businesses face a critical question: “How do we scale AI agents responsibly, without compromising on privacy, governance, or trust?”

Thus, the responsible use of AI holds the key to unlocking the greatest long-term value and impact. And that applies fully to AI agents. Many existing AI programs weren’t built for an agent-driven world, so it’s urgent that businesses evolve their practices to meet these new realities. All the while still enabling innovation and business growth.

Challenges Businesses Should Prepare For And How to Address Them:

1. Protecting Against Data Exposure

Because AI agents operate autonomously, keeping direct oversight over every action they take can be tough. That autonomy may open the door to risks like accidental data leaks.

For example. an AI agent helping customers with upcoming flight inquiries. To do its job, it needs access to personal data like booking details, payment methods, and IDs. Without careful control, the agent might mistakenly share sensitive information through external searches or platforms, putting privacy and your company’s reputation at risk.

So, as a business leader, what should you do in this situation? Here are some of the factors to consider:

• If an agent needs to perform web searches, for example, make sure it doesn’t have access to customer data while doing so.
• Set up regular monitoring with tools to flag and escalate any suspicious data handling to a human supervisor.
• Implement data anonymization techniques and strictly limit access rights across multi-agent systems.
• Don’t skip user testing, red teaming, and frequent audits to ensure compliance with data privacy guidelines.

You might even want to create a “security specialist” agent dedicated to reviewing external data interactions.

2. Avoiding Over-reliance on Automation

As AI agents become more capable, it’s easy for employees to lean too heavily on them, or even feel pushed toward depending on them due to new incentive structures. That can weaken human oversight over time.

Imagine your ticketing system where AI agents handle refund requests. If your team starts trusting the agents too much, especially under pressure for speed, the teams might stop doing even basic reviews, allowing errors or fraud to slip through unnoticed.

What to pay attention to:

• Design AI agents to flag certain decisions for human review. For example, set a rule that any refund over $200 must be approved by a human.

• Regularly compare agent decisions to human ones to catch any drift or quality issues.

• Run periodic training and user testing so your team knows how to collaborate effectively with AI, not just hand off responsibility to it.

• As workflows evolve, assess whether agent deployment could shift job roles unintentionally.
• Invest in re-skilling and training employees to become effective AI agent managers, ensuring they stay critical to the process.

3. Preventing “Temporary” Solutions from Becoming Permanent

It’s tempting to use AI agents as bridges between modern platforms and old legacy systems, but you don’t want to let short-term fixes become permanent crutches. For instance, your agent connects modern help desk software to an aging ticket booking system. It works for now, but without a plan to upgrade that outdated infrastructure, you still risk locking your operations into inefficient systems long-term.

Hers’s what you can do:

• Ensure your agentic solutions fit into a broader digital transformation plan, rather than just patching up old systems.
• Build “retirement plans” into the design of your AI agents, with clear milestones that phase out temporary fixes over time.

The Path Forward:

AI agents offer extraordinary potential to revolutionize how you work, but only if you manage them wisely. By proactively addressing risks, adapting your governance, and keeping human oversight firmly in place, you can unlock real, sustainable value from this exciting technology.

Remember: it’s not just about what AI agents can do. It’s about how you choose to use them, responsibly, strategically, and always with a clear eye on the bigger picture.

Align Your AI Agent Strategy with Responsible AI Principles

At first glance, it might seem like the autonomous nature of AI agents conflicts with the principles of Responsible AI. But in reality, Responsible AI is exactly what makes the rapid growth and scalable deployment of AI agents sustainable. By putting in place clear approval paths, testing standards, and monitoring practices, you create an environment where innovation and responsibility go hand in hand.

Building a responsible agentic system starts with setting clear operational guidelines, not only for the AI agents themselves but also for the humans who design, interact with, and oversee these systems throughout their lifecycle.

To keep your AI agents aligned with your company’s values and goals as they evolve, you’ll need strong stakeholder engagement, active feedback loops, and ongoing human oversight.

Here are five key tactics, plus some essential technical controls, that can help you align your AI agent strategy with Responsible AI practices:

1. Evolve Your AI Governance to Include Agent Oversight

Your AI agents shouldn’t be governed in isolation. Instead, treat them as an integral part of your overall AI governance framework.

• Create a dedicated function within your governance structure to perform “horizon scanning”, actively identifying emerging technologies like AI agents that may challenge existing policies.

• Look for ways to streamline and accelerate governance so that managing risk becomes a strategic advantage, not a roadblock.

• Pay close attention to areas where new technologies are causing friction, and be ready to adjust your governance practices to keep pace with change.

By embedding agent oversight into your governance framework, you ensure consistent, scalable management without slowing innovation.

2. Build a Risk Management Strategy for AI Agents

Not all AI agents carry the same level of risk. You’ll want to factor an agent’s autonomy and potential impact into your risk tiering and prioritization structures.

• Apply greater governance rigor to high-autonomy, high-impact agents, while allowing faster adoption of low-risk agents.

• Clearly define the attributes that would trigger an agent’s inclusion into your centralized AI inventory, for instance, whether it’s shared across teams or operates independently.

• Track usage, access, and performance metrics for critical and high-risk agents.

• Agree on evaluation criteria for agent performance and define a structured process for iterative testing and scaling.

Taking a proactive approach to agent risk management helps you balance speed and safety at every stage.

3. Establish a Strong Infrastructure to Support Responsible AI Work

• Protecting sensitive information and critical systems is essential when deploying AI agents.

• Use data anonymization techniques like masking or tokenization to prevent agents from leaking sensitive information.

• Deploy Data Loss Prevention (DLP) tools to monitor and block unauthorized data transmissions. Set up alerts that escalate any suspicious behavior to a human supervisor.

• Require multi-factor authentication (MFA) for agents accessing critical systems, and ensure access is granted by a human only when necessary.

• Implement role-based access controls to limit what agents can see and do.

• Always follow the principle of least privilege, giving agents only the minimum access needed to accomplish their tasks.

By securing your infrastructure, you’ll empower AI agents to work efficiently without introducing unnecessary risk.

4. Implement Rigorous Testing and Monitoring

AI agents aren’t “set it and forget it” tools, they need ongoing monitoring and testing to stay aligned with your goals.

• Use real-time anomaly detection to catch unexpected behavior immediately.

• Set up continuous monitoring to track long-term trends and detect performance drift over time.

• Conduct regular security audits to ensure agents are staying compliant with your security and data policies.

• Practice AI red teaming and user testing to simulate real-world attacks and discover vulnerabilities before they cause problems.

• Integrate automated testing into your development lifecycle so issues are caught early and often.

• Maintain detailed logs of agent activities and regularly review them to detect unauthorized access or anomalies.

Constant testing and monitoring create a safety net that protects both your organization and your customers.

5. Keep Humans in the Loop

No matter how advanced AI agents become, human oversight remains critical, especially when decisions carry significant consequences.

• Deploy AI agents in environments where they work alongside humans, not replace them.

• Define clear escalation paths for decisions that need human review and intervention.

• Regularly compare AI decisions to human decisions to spot gaps, biases, or inefficiencies.

• Use these insights to fine-tune escalation thresholds and improve collaboration between humans and AI over time.

Keeping humans “at the helm” ensures you maintain control, accountability, and trust as AI agents take on more responsibility.

Moving Forward: Responsible Growth with AI Agents

By implementing these strategic practices and technical controls, you’ll position your AI agents to operate securely, efficiently, and in alignment with Responsible AI principles.

This approach not only helps you mitigate risk, it also enhances your agents’ performance, supports innovation, and builds long-term trust with your employees, customers, and broader stakeholders.

In the fast-evolving world of AI, responsibility isn’t just a safeguard, it’s a competitive advantage.