Introducing Parlant: An Open Source Framework for Trusted AI Agents

Problem: Why Current AI Agents Are Approaching Failure

If you've ever designed and used an LLM Model-based chatbot in production, you've experienced the frustration of agents who fail to perform tasks reliably. These programs are often not repeatable and struggle to complete tasks as intended, often go off topic and deliver a poor customer experience.

Conventional strategies for addressing these challenges have their limitations.

One common approach is to use long and complex instructions. While this may reduce unwanted behavior, it never fully prevents the agent from going off-topic, even slightly, which can have a significant impact and may introduce significant delays. Whenever new corner conditions arise, adjusting the distance information can inadvertently create an additional edge. cases, resulting in a fragile system.

Another commonly used method is to use guardrails. While this can work, it's usually a drastic measure because it forces the chatbot to shut down immediately upon detection of any violation or deviation, which damages the overall user experience.

The impact is significant:

• Destroyed trust: Users quickly lose confidence in the bot—and your product—when the answers are clearly incorrect or incomplete.
• Compliance risks: A chatbot that makes unauthorized or erroneous statements can be a legal and financial risk.
• Lost sales: If the agent deviates from the target script, conversion and sales become difficult.
• Lost customers: An unprofessional or misleading chatbot can effectively drive customers away.

Some Real World Examples where these issues are important:

• A customer service agent from a bank or financial service company that provides inconsistent advice.
• A sales agent on an e-commerce site that incorrectly describes or sets prices.
• A health services agent offering unproven medical suggestions.

A New Open Source Approach: Parlant

Parlant introduce a dynamic control system which ensures that agents follow your specific business rules. It does this by matching and applying the right combination of guidelines for each situation. Here's a quick look at how it works:

1. Content Evaluation:

When the chatbot needs to respond, Parlant checks the context of the conversation and loads the appropriate guidelines (rules you define for your specific use cases).

2. Guidelines for Conduct:

These guidelines shape the chatbot's tone, style, and allowed content. Parlant also continuously reviews as new information emerges.

3. Ways of self-criticism:

Before making a final response, Parlant runs a self-critique process, ensuring that the response accurately conforms to the standardized guidelines.

How Parlant Works

Parlant's main components include Instructionsa List of wordsa Unity Checkerand a Tool Service.

Let's check them out:

1. Instructions

Here are the guidelines very powerful customization feature in Parlant. They decide how your chatbot should respond to certain situations. Parlant only inserts instructions relevant to the LLM context in real time, keeping the interaction clean and efficient.

What are the Guidelines?

Guidelines allow you to shape agent behavior in three main ways.

First, they help deal with unwanted out-of-the-box responses that are confusing or incomplete.

Second, they ensure consistent behavior across interactions.

Third, they direct the agent to stay focused on the target behavior (no pun intended!)

For example, a natural response to a request to book a room might be, “Sure, I can help you book a room. When will you stay?” While this answer is valid, guidelines can be used to make it more enthusiastic. A reworked answer might be, “I'm so glad you chose our hotel! Let's find you the best room for your needs. When will you stay?”

Structure of Guidelines
Each guide has two parts:

The situation: A prefix or condition (eg, “It's a holiday”).

Action: An instruction (eg, “Give a discount”).

Answer from Parlant:

When it's a holiday, Then give a discount.

2. Ensuring Compliance

Parlant ensures guidelines for internal consistency and provides visibility into agent decision-making. This eliminates confusion or conflict when multiple guidelines can be active at the same time.

3. Glossary

A glossary defines any special terms or domain-specific language that the chatbot needs to be aware of. This helps keep the terms consistent throughout the conversation.

4. Tool service

The Tool Service allows the chatbot to call external APIs or third-party tools, such as browsing e-commerce product categories or retrieving a user's order history. This ensures that your agent can act on real data rather than relying solely on internal model training.

Bonus Features: Guardrails and Content Rating

Even with strict guidelines, it may be necessary to add another layer of protection. Parlant also includes services such as OpenAI's Omni Moderation to pre-filter dangerous or sensitive content, ensuring a safe interaction. In domains such as mental health or legal advice, it is best to refer users to an expert. Parlant guides can automatically redirect users to human agents when these topics arise, maintaining compliance and user well-being.

Customers may also try to harass or manipulate the agent. Parlant's content filtering helps keep conversations respectful and ensures that the chatbot stays written in controversial situations. Finally, some users try to “hack” the agent or expose its internal rules. Parlant offers a “paranoid” mode, which includes Lakera Guard, to maintain the intended parameters of the chatbot and prevent unauthorized behavior.

The Agent Development Journey has evolved!

Parlant lets you do just that build and refine your chatbot step by step. You start with basic guidelines, then change your agent as you learn more about your customers' needs and behaviors. Because it's open source, you can also use community contributions and best practices 😉

It allows you to convert the Guardrail into a flexible guide … very powerful!

Try it and experience a a new level of control in LLM based chatbot development.

How to install Parlant?

I all source codelicensed under Apache 2.0, available on GitHub. We encourage you to check it out…these will be the growing trends of 2025. We always love open source projects, so a friendly reminder that a simple “Star” in the repository goes a long way in showing your support!

Here are the links :

Thanks to the Parlan team for the thought leadership/Resources for this article. Parlan team supported us in this content/topic.

Jean-marc is a successful AI business executive .He leads and accelerates the development of AI-powered solutions and started a computer vision company in 2006. He is a well-known speaker at AI conferences and has an MBA from Stanford.