Agentic Design Methodology: How to Build Reliable and Human-Like AI Agents using Parlant

Understanding the Target Audience

The target audience for the Agentic Design Methodology includes business leaders, AI developers, and product managers interested in building reliable AI agents. They often face challenges with traditional software development paradigms that do not translate well to AI, particularly in ensuring human-like interactions and adaptability in AI responses.

Key pain points for this audience include:

• Difficulty in defining clear, actionable guidelines for AI behavior.
• Challenges in managing the variability of AI responses.
• Ensuring compliance and safety in AI interactions.

Their goals involve creating AI systems that can engage users effectively while maintaining reliability and safety. They are interested in methodologies that provide structured yet flexible approaches to AI design, and they prefer clear, technical communication that includes real-world applications and examples.

What Is Agentic Design?

Agentic design refers to the construction of AI systems capable of independent action within defined parameters. Unlike traditional software development, which relies on deterministic code execution, agentic systems require designers to articulate desired behaviors, allowing the model to navigate specifics autonomously.

Variability in AI Responses

Traditional software produces consistent outputs for identical inputs. In contrast, agentic systems rely on probabilistic models, generating varied yet contextually appropriate responses. This variability enhances user experience by mimicking human dialogue but necessitates careful prompt and guideline design to ensure safety and consistency.

For example, a request like “Can you help me reset my password?” could yield several appropriate responses:

• “Of course! Please tell me your username.”
• “Absolutely, let’s get started—what’s your email address?”
• “I can assist with that. Do you remember your account ID?”

Why Clear Instructions Matter

Language models interpret instructions rather than executing them literally. Vague guidance can lead to unpredictable behavior. Instead, instructions should be concrete and action-focused:

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

This specificity ensures the model’s actions align with organizational policy and user expectations.

Building Compliance: Layers of Control

While large language models (LLMs) cannot be fully controlled, their behavior can be guided effectively through layers of compliance:

Layer 1: Guidelines

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

Layer 2: Canned Responses

For high-risk situations, use pre-approved canned responses to ensure 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 layered approach minimizes risk and ensures the agent behaves consistently in sensitive situations.

Tool Calling: When Agents Take Action

When AI agents utilize tools such as APIs, they face complexities beyond simple command execution. For instance, a request to “Schedule a meeting with Sarah for next week” involves inferring unclear elements like the specific day and time, and which Sarah is referenced. This is known as the Parameter Guessing Problem.

To mitigate ambiguity, tools should have:

• Clear purpose descriptions
• Parameter hints
• Contextual examples

Well-structured tools enhance accuracy and reduce errors, facilitating smoother interactions.

Agent Design Is Iterative

Agent behavior in agentic systems evolves through continuous observation and refinement. The design process begins with simple, high-frequency scenarios to establish baseline functionality. Monitoring the agent’s performance allows for targeted improvements to address user confusion or policy breaches.

Writing Effective Guidelines

Each guideline should consist of three key components:

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 Conversations: Journeys

For complex tasks, simple guidelines may be insufficient. Journeys provide a framework for structured, multi-step conversational flows, guiding the user through processes smoothly. For instance, a booking flow could be initiated as follows:

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

Balancing Flexibility and Predictability

Designing AI agents involves balancing flexibility and predictability. Clear instructions must allow for adaptability without sacrificing reliability. For example:

"Explain our pricing tiers clearly, highlight the value, and ask about the customer’s needs to recommend the best fit."
    

Designing for Real Conversations

Effective conversational design acknowledges the non-linear nature of human dialogue. Key principles include:

• Context preservation: The agent tracks previously provided information.
• Progressive disclosure: Information is revealed gradually.
• Recovery mechanisms: The agent manages misunderstandings gracefully.

This approach fosters natural, user-friendly interactions.

Conclusion

Effective agentic design starts with core functionalities, focusing on common tasks before addressing rare cases. Continuous monitoring and iterative improvements based on real observations are essential for developing reliable, user-friendly AI agents.