Increasing Predictability and Trust of AI Agents in Customer Support

Customer support is evolving. The growing use of AI agents has shifted how businesses operate, promising efficiency, cost savings, and round-the-clock availability. But there’s one significant roadblock that companies face when using AI in customer-facing roles: trust.  

AI agents have a reputation for being unpredictable. They lack the consistency, judgment, and context-driven decision-making that humans instinctively bring to the table. Especially in customer support, where trust is key, unpredictability undermines an agent's utility. The less you trust what it can do, the less responsibility you're willing to give it.

This post explores why predictability is critical in AI-powered customer support, common challenges businesses face, and how solutions like Duckie are paving the way to reliable, trustworthy AI operations.

‍

‍

The Problem With Unpredictability in AI Agents  

AI is inherently unpredictable. The technology relies on probabilistic algorithms and models that are trained to respond based on historical data. While this allows for remarkable flexibility and adaptability, it also introduces uncertainties. When the AI behaves unexpectedly, even minor discrepancies can erode trust.

Predictability and Trust Are Interconnected

Trust in AI is tightly bound to predictability. This is particularly critical in customer-facing organizations where AI solutions interact directly with users. Errors or erratic behavior don’t just reflect poorly on the technology but can actively harm the customer's experience.  

Consider this example:

A fintech company's AI support agent misinterprets a customer's question about "disputing a transaction" as a request to "refund the transaction." This leads to an unauthorized refund being processed, causing confusion and potential financial complications for both the customer and the company.

‍

Why Trust Impacts Utility  

Here’s the cycle of trust and utility in AI agents:

Higher Trust = Expanded Use Cases

Businesses are more likely to integrate an AI into their systems and assign it greater responsibility when they can anticipate its actions reliably. Trusting the performance of the AI translates into leveraging it for complex tasks, rather than reserving it for surface-level queries.

Lower Trust = Restricted Functionality

Conversely, if the AI appears unreliable, you keep it on a short leash. This limits its utility and generates bottlenecks that results in a restricted number use cases within customer support organizations.

‍

Considering the “Human-in-the-Loop” Strategy  

Many organizations introduce a human-in-the-loop model, where human operators oversee AI and intervene when necessary. While this approach lessens catastrophic failure, it’s only a partial solution. It creates inefficiencies in the long term and does not scale well as customer volume increases.

‍

The Core Question  

Given these challenges, the primary question arises:

How can you increase the predictability of an AI agent’s behavior so that trust and utility rise simultaneously?

The answer lies in forward-thinking design principles, and play a crucial role in the way we are building Duckie.

‍

‍

How Duckie Solves Unpredictability Through Smart Design

At Duckie, we spent 12 months working with our customers to understand the problem and develop a unique set of frameworks to address the issue of unpredictability in AI  support agents. We realized that building our platform around customization, control, and transparency, organizations can design and deploy their own AI agents with a high degree of trust. As a result, we have seen the Duckie handle an immense range of use cases - from simple Q&A to executing tasks within CRMs and databases—all while maintaining consistent and reliable results.

‍

Agentic Workflow

At the core of Duckie is an agentic workflow designed to let customer support teams ensure predictable patters of behavior. Rather than letting the AI operate freely and potentially surprising users, Duckie's workflow let's organizations define structural rules and conditions for how an AI agent interprets, processes, and acts on customer requests.

This includes:

• Multi-Step Flows - Strategically guiding the AI through clear, step-by-step workflows that reduce variability in outcomes.
• Action Control - Defining boundaries within which the AI operates, ensuring that actions are taken only when specific conditions are met.

‍

Low-Level Customization

Coupled with Duckie's agentic workflow is prompt customization, which gives customers full control to fine-tune the agent's reasoning for both guiding decisions and taking actions. Prompting forms the foundation for shaping how the AI thinks and acts, and Duckie's prompt customization lets users fine-tune their AI to met their specific needs.

Here’s how customizable prompting works:

1. Customers can create prompts with specific context, embedding the unique nuances of their use case into the AI.
2. Prompts can be adjusted and refined over time, improving both response accuracy and decision-making reliability.
3. Users can implement safeguards against ambiguity, ensuring outputs consistently align with their brand guidelines and desired outcomes.

‍

Tool Approval

For sensitive actions like accessing customer data or performing critical tasks, Duckie provides the option to require explicit approval from the customer support team. This flexible approach to human-in-the-loop is designed to balance efficiency with security. When using tool approval, organizations can identify high risk points in their AI agents and nullify uncertainty

Duckie offers a flexible tool approval process designed to balance efficiency and security. For sensitive actions like accessing customer data or performing critical tasks, Duckie provides the option to require explicit human approval. By enabling tool approval, businesses gain greater control, ensuring sensitive operations are safeguarded through human oversight when needed.

‍

‍

Why Predictability Matters More Than Ever  

The growing reliance on AI in customer support makes predictability a non-negotiable. Customers expect fast resolutions, clear communication, and data protection. An AI agent that provides consistent, controlled, and trusted outputs will not only meet but exceed these expectations.  

Increasing predictability also unlocks the full potential of AI:

• Automating repetitive tasks without hesitation.
• Scaling operations confidently.
• Building lasting trust with customers.

AI that is both predictable and efficient is the ultimate competitive advantage.

‍

‍

Unlocking Consistency and Confidence With Duckie  

Predictability is the foundation of trust. Without it, even the most advanced AI agents can’t deliver their full utility to your business. Duckie is leading the charge by providing customer support organization with the tools to build agentic workflows, safeguard sensitive actions, and give companies full control over outcomes.

Whether you’re managing customer service, streamlining operations, or improving response times, Duckie enhances your ecosystem by creating AI you can actually trust.

Want to transform how you use AI in customer support? Experience Duckie firsthand and discover how we’re making AI predictable one step at a time.