Agent Design Method: How to Build Reliable and Human-Like AI Agents with Parlant

Building a powerful AI agent is fundamentally different from traditional software development because it centers on probabilistic model behavior rather than deterministic code execution. This guide provides a neutral overview of the methodology of designing both reliable and adaptable AI agents, focusing on creating clear boundaries, effective behavior, and safe interactions.

What is agent design?

Agent design refers to building an AI system that can act independently in defined parameters. Unlike traditional encodings, which specifies the exact result of the input, the agent system requires designers to articulate ideal behavior and trust the model to navigate details.

Variability of AI response

For the same input, the traditional software output remains constant. Instead, proxies based on probabilistic models produce different but appropriate responses each time. This makes effective timely and guide design crucial to human loyalty and safety.

In the proxy system, are requests like “Can you help me reset my password?” It may cause different but appropriate replies such as “Of course! Please tell me your username”, “Absolutely start to start – what is your email address?” or “I can help you. Do you remember your account ID?”. This variability is purposeful and aims to enhance the user experience by mimicking the nuances and flexibility of human conversations. At the same time, this unpredictability requires thoughtful guidelines and safeguards, so the system can respond safely and consistently in all situations.

Why clear explanations are important

Language models explain rather than literally execute them. Fuzzy guidance, such as:

agent.create_guideline(
    condition="User expresses frustration",
    action="Try to make them happy"
)

Can lead to unpredictable or unsafe behavior, such as unexpected quotes or commitments. Instead, the explanation should be specific and action-focused:

Instead, be specific and safe:

agent.create_guideline(
    condition="User is upset by a delayed delivery",
    action="Acknowledge the delay, apologize, and provide a status update"
)

This approach ensures that the actions of the model are consistent with organizational policies and user expectations.

Building Compliance: Control Level

LLM cannot be completely “controlled”, but you can still effectively guide and limit its behavior.

Level 1: Guide

Use guides to define and shape normal behavior.

await agent.create_guideline(
    condition="Customer asks about topics outside your scope",
    action="Politely decline and redirect to what you can help with"
)

Level 2: Canned Response

For high-risk situations such as policy or medical advice, use a pre-approved canned response to ensure consistency and safety.

await agent.create_canned_response(
    template="I can help with account questions, but for policy details I'll connect you to a specialist."
)

This stratification approach minimizes risk and ensures that agents never improvise in sensitive situations.

Tool call: When the agent takes action

When an AI agent takes action using tools such as APIs or features, the process involves more complexity than simply executing commands. For example, if the user says, “Schedule a meeting with Sarah next week,” the agent must explain several unclear elements: which Sarah is mentioned? In “Next Week”, should the meeting be scheduled? And which calendar?

This illustrates the parameter guessing problem, and the agent tries to infer missing details not explicitly provided. To resolve this problem, explicit purpose descriptions, parameter hints and context example design tools should be used to reduce ambiguity. In addition, the tool name should be intuitive and the parameter type is consistent, helping the agent reliably select and fill inputs. Well-structured tools improve accuracy, reduce errors and make interactions smoother and more predictable for both agents and users.

This thoughtful tool design practice is crucial to effective in real-world applications, security proxy functionality. When an AI agent performs tasks through tools such as APIs or features, the complexity is usually higher than what was initially displayed.

Agent design is iterative

Unlike static software, proxy behavior in proxy systems is not fixed. Over time, it matures through continuous observation, evaluation and exquisite cycles. The process usually begins with implementing direct, high-frequency user schemes – those “happy path” interactions that can easily predict and verify the response of the proxy. Once deployed in a secure testing environment, agent behavior is strictly monitored to find unexpected answers, user confusion or any violation of policy guidelines.

When problems are observed, agents can be systematically improved by introducing targeted rules or refining existing logic to solve problematic situations. For example, if the user repeatedly rejects a quote from UPSEL but the agent continues to propose, you can add a focus rule to prevent this behavior in the same session. Through this intentional incremental adjustment, the agent gradually evolved from a basic prototype to a complex dialogue system that was responsive, reliable and in line with user expectations and operational constraints.

Write effective guidelines

Each criterion has three key parts:

example:

await agent.create_guideline(
    condition="Customer requests a specific appointment time that's unavailable",
    action="Offer the three closest available slots as alternatives",
    tools=[get_available_slots]
)

Structured dialogue: Journey

For complex tasks, such as appointments, onboarding, or troubleshooting, simple guidelines are often insufficient. This is where the journey becomes an essential place. Journeys provides a framework for designing structured multi-step conversation flows that smoothly guide users through the process while maintaining natural conversations.

For example, a reservation flow can be initiated by creating a clear title and conditions that apply, such as when a customer wants to schedule an appointment. The journey then takes place through the states – first ask the customer about the type of service required, then check availability with the appropriate tools, and finally provide the available period. This structured approach balances flexibility and control, allowing agents to effectively handle complex interactions without losing their sense of conversation.

Example: Subscription flow

booking_journey = await agent.create_journey(
    title="Book Appointment",
    conditions=["Customer wants to schedule an appointment"],
    description="Guide customer through the booking process"
)

t1 = await booking_journey.initial_state.transition_to(
    chat_state="Ask what type of service they need"
)
t2 = await t1.target.transition_to(
    tool_state=check_availability_for_service
)
t3 = await t2.target.transition_to(
    chat_state="Offer available time slots"
)

Balancing flexibility and predictability

When designing AI agents, balancing flexibility and predictability is crucial. Agents should feel natural and dialogue rather than over-scripting, but it still has to run within safe, consistent boundaries.

If the instructions are too strict, for example, tell the agent “To be exact: ‘Our Premium Plan is $99 per month” – Interaction will feel mechanical and unnatural. On the other hand, the description is too vague, for example, “Help them understand our pricing”, which may lead to unpredictable or inconsistent responses.

The balanced approach provides a clear direction while allowing the proxy to have some adaptability, such as: “Clearly state our pricing tier, highlight that value, and ask customers to recommend the most suitable needs.”This ensures that the agent is both reliable and involved in their interactions.

Design real conversations

Designing a real conversation requires realization that, unlike web forms, conversations are nonlinear. The user may change his mind, skip the steps or move the discussion in an unexpected direction. To effectively deal with this issue, there are several key principles to follow.

• Context Save Make sure the agent tracks the information already provided so that appropriate responses can be made.
• Gradually disclosed Meaning gradually reveals options or information, rather than flooding the user at once.
• Recovery mechanism Allows agents to manage misunderstandings or biases gracefully, for example, by re-responsive or gently redirecting the conversation to be clear.

This approach helps create natural, flexible and user-friendly interactions.

Effective proxy design means starting with core functionality, with the focus before solving rare cases. It involves careful monitoring to discover any problems in the behavior of the agent. Improvements should be based on real observations, adding clear rules to guide better responses. It is important to balance clear boundaries to ensure the security of the agents while allowing natural, flexible dialogue. For complex tasks, use structured traffic called journeys to guide multi-step interactions. Finally, keep transparent about what the agent can do and how it sets appropriate expectations. This simple process helps create reliable, user-friendly AI agents.

I am a civil engineering graduate in Islamic Islam in Jamia Milia New Delhi (2022) and I am very interested in data science, especially neural networks and their applications in various fields.