AI agents are no longer just tools — they’re transformative team members reshaping how businesses engage with their customers.

Their ability to automate inquiries, deliver real-time solutions, and enhance team productivity makes them indispensable tools for modern businesses. But the key to unlocking their full potential lies in effective management .

As someone who has managed AI agents firsthand, I’ve witnessed how these solutions revolutionize day-to-day workflows and unlock new opportunities for businesses to thrive. And, when managed effectively, AI agents offer more than cost savings — they become strategic assets that drive customer satisfaction, employee empowerment, and even revenue growth.

But this shift isn’t automatic; it requires thoughtful management, robust planning, and a clear understanding of the AI agent’s role in the organization .

I’m sharing six essential insights for mastering the art and science of AI agent management. From treating your AI agent like a dynamic team member to leveraging automation for strategic gains, these lessons are your path to successful AI customer service .

1. Understand the AI agent's role

An AI agent is your frontline solution for automating repetitive customer service tasks while maintaining high quality. But it’s not just about handling FAQs anymore; AI agents can address nuanced, multi-step inquiries that once required human intervention.

This capability allows businesses to scale service efficiently. With capabilities like nuanced reasoning and omnichannel support , AI agents reduce operational costs while ensuring scalable, quality customer service​​.

That being said, from my own personal experience, the impact of AI agents goes beyond cost savings. They're strategic assets designed to resolve complex customer inquiries and elevate service standards. They ensure scalability without compromising customer experience — a critical factor in industries with seasonal demand spikes or rapid growth.

For example, AI agents can help FinTech companies manage high-volume, complex interactions during IPOs or regulatory changes, keeping CSAT high while freeing human agents to handle urgent escalations.

Takeaway: When businesses recognize the AI agent as a strategic asset rather than a support tool, they shift their focus to proactive improvements, aligning AI outputs with overarching goals like revenue growth and customer loyalty.

2. Empower teams through automation

Automation isn’t about replacing jobs — it’s about enhancing them. In my experience, AI agents liberate human agents from mundane tasks, enabling them to focus on complex tickets or career growth. This redistribution of workload can transform a support team into a hub of innovation.

For instance, after introducing Ada, one of my previous employers restructured the CX team, creating new roles for AI managers and product specialists.

This reallocation of resources boosts morale and creates opportunities for professional development and allows employees to develop specialized skills, like analyzing unresolved conversations for insights or fine-tuning the AI to handle new scenarios.

Takeaway: Teams that embrace automation can reallocate resources to high-impact areas, such as strategic planning or customer relationship management, which directly contribute to long-term business success.

3. Automate with precision

Automation is only as effective as its accuracy. In my role, I’ve seen how well-designed AI agents can confidently manage even sensitive inquiries, such as account troubleshooting or subscription changes. AI agents excel at interpreting nuanced inquiries, seamlessly integrating with tech stacks to provide personalized, contextualized resolutions​​.

However, achieving this requires more than just deploying AI — it demands precise configurations.

Take Wealthsimple , which achieved a 24% improvement in Automated Resolution (AR) by switching from a scripted bot to a generative AI agent. This leap wasn’t accidental — it was the result of constant iteration and leveraging Ada's reasoning engine for more sophisticated response handling. The biggest win? Customers didn’t just get answers; they got actionable, personalized resolutions.

Takeaway: Automation done poorly can create friction or distrust. Precision ensures customers receive relevant, accurate responses, building confidence in the technology while delivering measurable ROI.

4. Knowledge management is key

Maintaining an accurate, well-organized knowledge base is the linchpin of AI success. During my tenure as an AI manager, I’ve spent countless hours refining this component. The payoff? When AI has a robust source of truth, it can resolve inquiries faster and with greater accuracy, minimizing escalations.

For example, I’ve implemented systems where domain experts and product teams regularly update the knowledge base. This collaboration ensures the AI stays aligned with current products, policies, and customer expectations. Additionally, generative AI capabilities reduce manual work by automatically learning from these updates.

Takeaway: Proactive knowledge management isn’t just about efficiency — it’s about avoiding costly mistakes. Every outdated or missing article can lead to incorrect responses, negatively impacting both customer satisfaction and brand reputation.

5. Treat the AI agent like an employee

One of the most valuable lessons I’ve learned is to treat the AI agent as a dynamic, trainable member of the team. This mindset shifts how organizations approach AI management — from setting clear onboarding processes to tracking metrics like AR and CSAT as performance indicators.

In my past role as an AI manager, we held coaching sessions for the AI agent, analyzing transcripts to identify where it underperformed and updating its configurations accordingly. This iterative approach resulted in measurable improvements, including a 10-point increase in CSAT and significant labor savings.

Metrics like Automated Resolution Rate (AR), CSAT , and ROI dashboards are crucial for gauging success. Regular coaching sessions and performance reviews ensure your AI agent continues to align with business goals and brand voice​​.

Takeaway: Viewing your AI agent as an employee encourages accountability and continuous improvement. It ensures the AI evolves in step with your business, driving ongoing value.

6. Build trust through transparency

Transparency in how AI functions is critical, both for customers and internal stakeholders. One of the challenges I’ve faced is addressing hallucinations — when AI generates incorrect or irrelevant answers.

The solution? Explaining how the AI processes information and involving cross-functional teams in the refinement process.

For example, as an AI manager I helped establish feedback loops where product specialists tested the AI's responses against real scenarios. This approach not only improved the AI’s accuracy but also reassured stakeholders that the system was reliable and safe.

Takeaway: Trust is the foundation of successful AI adoption. Transparency builds confidence among customers and employees, reducing resistance to AI-driven change and enabling smoother implementation.

Harnessing the full potential of your AI agent

Managing an AI agent effectively can revolutionize customer service, driving efficiency and enhancing customer satisfaction.

As an AI manager, I’ve experienced the dual challenge of maximizing technology while fostering collaboration across teams. Managing an AI agent isn’t a technical task — it’s a strategic role that requires communication, creativity, and a results-driven mindset.

The future of customer service will see AI managers play a pivotal role in blending automation with human expertise, setting new benchmarks for efficiency and innovation. By mastering these six areas, businesses can position their AI agents and their customer service teams for unparalleled success.