The conventional chat interface has become the default way we interact with large language models (LLMs). However, for AI agents designed to handle specific, complex tasks, relying only on natural language is not always the best user experience. A simple chat window can place a heavy mental burden on the user, requiring them to craft a perfect prompt for highly structured work. This approach also favors those with strong language skills, making the tool less inclusive. For agents managing multi-step processes or working with structured data and external systems, a dedicated User Experience (UX) that moves beyond the simple chat window is often necessary. The choice of interface is a critical decision that determines the system’s overall effectiveness.
To select the best interface for your AI agent, you must evaluate the use case from the user’s perspective. Ask yourself: How much mental effort is required to ask for something? How predictable are the outcomes? How do we balance flexibility with minimizing user errors? Does the interface build user trust and control, making the agent feel like an intuitive and reliable tool?
The answers to these questions will guide you to the most appropriate user experience model. You should consider the following interaction patterns when designing an agentic system, each presenting distinct trade-offs:
Interaction models for AI agents
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Purely prompt-driven interaction
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Description: This is the conventional chat interface where the user has complete freedom to type any request in natural language.
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Pros: Offers maximum flexibility, making it ideal for exploratory, creative, or one-off tasks.
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Cons: Places the full cognitive load on the user to craft a perfect prompt, which often leads to ambiguity and unreliable outcomes. This model is poorly suited for structured, repeatable tasks.
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Example: A user in an Enterprise AI chat types, “Brainstorm some taglines for our new cloud security product.”
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Guided conversational interface
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Description: The agent uses a prompt-based interface but actively guides the user by suggesting a menu of options or clarifying questions, similar to a phone’s Interactive Voice Response (IVR) system.
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Pros: Reduces ambiguity by providing clear, predefined choices, which helps steer the user toward a successful outcome.
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Cons: Can feel restrictive and inefficient for expert users who know exactly what they want. The interaction can become cumbersome if it requires navigating through multiple menu levels.
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Example: A travel booking bot asks, “Are you looking for a flight, hotel, or rental car?” and presents three buttons for the user to click.
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Form-based and action-driven UI
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Description: This model moves away from chat entirely, using a dedicated interface like a web form with fields, dropdowns, and a static set of one-click action buttons. It is highly effective for well-defined, repeatable tasks where inputs are structured and outcomes must be consistent.
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Pros: Delivers highly predictable and reliable results by guiding user input. It minimizes errors by acting as a guardrail and requires almost no user training.
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Cons: It is inflexible by design and cannot handle any task or variation that has not been explicitly built into the interface.
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Example: An internal procurement site where an employee fills out a form with fields for “Item,” “Quantity,” and “Business Justification.” Clicking “Submit” triggers an agent that automatically routes the request for approval.
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Dynamic action suggestions
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Description: This hybrid model combines a natural language prompt with an agent that dynamically suggests relevant, one-click action tiles based on the user’s input. It functions like code autocompletion (IntelliSense) for business tasks, anticipating user intent and surfacing the appropriate tools in real time.
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Pros: Blends the flexibility of natural language with the reliability and speed of structured actions, accelerating workflows by guiding users to the correct tool at the right moment.
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Cons: Its effectiveness depends entirely on the agent’s ability to accurately interpret user intent. Irrelevant or incorrect suggestions can frustrate the user and disrupt the workflow.
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Example: As a sales representative types notes into their CRM after a client call, “Follow up with John about the contract,” the agent dynamically suggests buttons like “[Schedule Follow-up Meeting]” or “[Draft Renewal Email].”
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Ambient and proactive interfaces
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Description: The agent often has no primary interface. It operates in the background, observing context (e.g., calendar, location, app usage) and proactively surfaces information or actions through subtle notifications or widgets. The interaction is minimal and event-driven rather than command-driven.
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Pros: Requires zero cognitive load from the user as it anticipates needs and integrates into their environment without demanding attention.
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Cons: Can feel intrusive if not designed with strict privacy and user control in mind. Irrelevant or poorly timed proactive suggestions can be highly disruptive.
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Example: An agent observes that a calendar meeting has just ended. Without any prompt, it sends a desktop notification: “Would you like me to draft a follow-up email with the key action items discussed?”
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Figure 1: Each UX choices offer different levels of Predictability and demand different levels of Cognitive Load from the user
The choice of an agent’s interface is not a simple either/or decision between chat and a graphical UI, but a strategic design choice along a spectrum. Each model represents a different set of trade-offs—balancing flexibility against reliability, and user control against cognitive load. The key to building an effective agent lies in deeply understanding the user’s task, the structure of the required inputs, and the desired predictability of the outputs. By moving beyond the default chat window and deliberately selecting—or even combining—these interaction patterns, you can create agents that are not just powerful, but also intuitive, trustworthy, and seamlessly integrated into the workflows they are meant to improve.