Research Report: The Future of Conversational AI and the Potential of Conversation Modeling

A comprehensive research work on chatbots, providing deeper insights and actionable solutions or further research directions regarding chatbot technology, moving towards building experts in the field.

Research Report: The Future of Conversational AI and the Potential of Conversation Modeling

Author: KMG Research Team

Version: 1.0 | May, 2025

Foreword

Recent advancements in artificial intelligence, particularly large language models (LLMs), have completely transformed how we interact with technology. However, a significant gap remains between impressive demos and reliable, practical applications. This report analyzes the current challenges in conversational AI and proposes Conversation Modeling – implemented via the open-source platform Parlant – as a breakthrough solution that can help organizations overcome these limitations.

At KMG, we are committed to researching and developing AI solutions that have practical applications, bringing real value to businesses and individuals. This report is the result of in-depth research into conversational AI models and the potential of Conversation Modeling in shaping the future of human-computer interaction.

Executive Summary

Traditional chatbot systems have undergone multiple generations of development, from simple rule-based systems to complex AI-powered platforms. While large language models (LLMs) like GPT-4 and Claude have made significant strides in text generation capabilities, deploying reliable and consistent conversational AI still faces numerous challenges. Businesses are grappling with several serious issues: from inconsistency in responses, lack of control, "hallucination," to high deployment and maintenance costs.

Our research indicates an emerging solution: Conversation Modeling. This is a novel approach that focuses on creating a structured framework for interactive AI, enabling businesses to precisely shape how AI converses with customers without sacrificing flexibility and naturalness. Parlant, an open-source platform, is leading in this field by offering detailed control through a system of conditional guidelines, glossary terms, and context variables.

Our analysis suggests that this model has the potential to make a major leap forward in practical conversational AI deployment. For organizations seeking conversational AI solutions, we believe that Conversation Modeling, implemented through Parlant or similar platforms, offers the most effective path to achieving reliable, consistent, and scalable conversational AI in a production environment.

1. Assessment of the Current State of Conversational AI
1.1 Historical Development

The field of conversational AI has undergone significant changes since ELIZA – the first chatbot – was created in 1966. Its development history can be divided into key periods:

• Rule-based Era (1960s-1990s): Systems like ELIZA and A.L.I.C.E relied on simple patterns and rigid response rules.

• NLU-ML Era (2010s): With the advent of platforms like Dialogflow and Rasa, ML models were applied to understand user intent, but still relied on structured conversation flows.

• LLM Era (2020s-present): Models like GPT, Claude, and Gemini have brought natural and flexible text generation capabilities, but often lack the control and reliability needed for enterprise applications.

• Emerging Era - Conversation Modeling (2024+): This is a new approach that combines the natural text generation capabilities of LLMs with the structure and control of rule-based systems.

1.2 Current Challenges of Conversational AI

Despite the incredible advancements of LLMs, deploying them in real-world enterprise environments still faces significant challenges:

1.2.1 Challenges in Quality and Reliability

• Inconsistency: LLMs often produce inconsistent responses across different interactions, even with the same input.

• "Hallucination": LLMs tend to generate inaccurate or fabricated information, especially when asked about topics outside their training data.

• Lack of precise control: Current prompt engineering methods are insufficient to reliably ensure LLMs adhere to business rules and policies.

• Difficulty in accurate data retrieval: Combining LLMs with enterprise databases remains challenging, especially when precise information needs to be queried from complex data sources.

1.2.2 Challenges in Deployment and Operations

• High cost: Fine-tuning and operating LLM models incur significant costs, especially when processing large volumes of interactions.

• Lack of security: LLMs often require sensitive data to be sent outside the enterprise system.

• Difficulty in improvement: When errors or issues are found in LLM responses, adjustments often require re-fine-tuning the entire model, a costly and non-guaranteed process.

• Scale and performance: LLMs often struggle to handle interactions with many users simultaneously, leading to high latency and poor user experience.

1.3 Current Methods and Their Limitations

Currently, organizations typically use one of the following methods to build conversational AI:

• Prompt Engineering and RAG:

  • Advantages: Easy to start, flexible.

  • Limitations: Limited control, low accuracy (~70%), no guarantee of consistent behavior.

• Fine-tuning LLM:

  • Advantages: Improved performance in specific domains.

  • Limitations: High cost, lack of runtime control, difficult to adjust and improve.

• Flow-based Systems (Botpress, Rasa):

  • Advantages: High control, consistency.

  • Limitations: Lacks flexibility, rigid user experience, high development cost.

• General RAG Solutions:

  • Advantages: Can connect with enterprise data.

  • Limitations: Low accuracy (65-70%), insufficient for critical applications, lack of behavior control.

These limitations have led to an unfortunate reality: many enterprise conversational AI projects fail to progress beyond the proof-of-concept stage or encounter failure when deployed in real-world environments.

2. The Emergence of Conversation Modeling
2.1 Conversation Modeling - A New Paradigm for Conversational AI

Conversation Modeling is a new approach emerging as a solution to the aforementioned challenges. It defines a structured framework for shaping AI interactions, allowing precise control over AI behavior without sacrificing the flexibility and naturalness of large language models.

2.1.1 Core Principles

Conversation Modeling is based on the following fundamental principles:

• Conditional Guidelines: Instead of trying to control every aspect of AI responses (as in flow-based systems) or providing vague instructions (as in prompt engineering), Conversation Modeling uses condition-action pairs. This allows the AI to dynamically adapt to different conversational situations while adhering to specific guidelines where appropriate.

• Semantic Control: Through glossary terms, Conversation Modeling ensures the AI accurately understands important terms within the business context.

• Structured Personalization: Through context variables, the AI can adjust responses based on user information without affecting core logic.

• Guided Tool Integration: Tools are linked to specific guidelines, ensuring they are used only when appropriate and in a manner consistent with business policies.

2.1.2 Position in the Technology Landscape

Conversation Modeling strikes a unique balance between existing methods:

• It offers flexibility like pure LLMs but with the control of rule-based systems.

• It allows detailed control like fine-tuning but with much lower cost and complexity.

• It supports natural conversation like generative models but with significantly higher reliability.

2.2 Parlant - An Open-Source Implementation of Conversation Modeling

Parlant is an open-source platform that implements the principles of Conversation Modeling. Designed from the ground up to address the challenges of conversational AI in enterprise environments, Parlant provides a solid foundation for building reliable AI agents.

2.2.1 Core Architecture

Parlant is built upon the following key components:

• Engine: The core of Parlant, responsible for coordinating other components and generating appropriate responses.

• Glossary Store: Stores and manages domain-specific terms and definitions.

• Guideline Matcher: Identifies and applies relevant guidelines based on the current conversation context.

• Tool Caller: Calls external tools (such as APIs or databases) when necessary.

• Message Composer: Generates the final response, ensuring compliance with selected guidelines.

2.2.2 Key Components of a Conversation Model

A conversation model in Parlant includes:

• Agent Identity: Describes the agent's mission, personality, and characteristics.

• Guidelines and Relationships: Precisely shape the agent's behavior. Relationships allow guidelines to override, depend on, or clarify each other.

• Glossary Terms: Important or industry-specific terms the agent needs to understand.

• Global and User-Specific Variables: Provide context (e.g., language, time) and personal information (e.g., subscription plan, preferences).

• Tools (Integrated APIs): Integrate real-world actions into the conversation model.

• Utterance Templates: Optional feature for absolute control over how the agent speaks, completely eliminating "hallucination."

2.3 Comparison with Other Solutions

To properly evaluate the value of Conversation Modeling and Parlant, we compared this method with other popular approaches:

2.4 Advantages of the Conversation Modeling Approach

Through our research, we have identified several key advantages of Conversation Modeling compared to other methods:

2.4.1 Technical Advantages

• Better Control: Guidelines allow precise control over agent behavior without fine-tuning the model.

• High Modularity: Each guideline is an independent unit that can be added, modified, or deleted without affecting the entire system.

• High Explainability: Parlant provides detailed feedback on why the agent chose a specific response.

• Smarter Tool Integration: Tools are linked to guidelines, ensuring they are used at the right time and in the right way.

• "Jailbreak" Protection: Input moderation and output checking help prevent misuse attempts.

2.4.2 Business Advantages

• Reduced Deployment Cost: No costly fine-tuning required, Parlant allows for rapid deployment at lower costs.

• Improved Customer Experience: More consistent and reliable agents lead to better user experience.

• Reduced Risk: Detailed control helps mitigate the risk of inappropriate or misleading responses.

• Faster Improvement Cycles: Guidelines can be easily updated without re-training the model.

• Better Scalability: Modular structure allows for easy expansion as needs grow.

3. Potential and Practical Applications
3.1 Ideal Use Cases

Conversation Modeling and Parlant are particularly suitable for the following scenarios:

3.1.1 Customer Support and Service

• Customer support chatbots: Parlant enables building customer support chatbots that are knowledgeable about company policies and procedures, while also capable of natural conversation.

• Product and service consultation: Agents can guide customers to select suitable products, adhering to the company's sales principles.

• FAQ and self-service: Answering frequently asked questions consistently, while also capable of handling variations of the same question.

3.1.2 Professional Services

• Medical consultation: Agents can provide accurate and consistent medical information, while knowing when to escalate to human experts.

• Legal support: Providing basic legal information, strictly adhering to regulations and limitations.

• Financial advice: Guiding customers on financial products, ensuring compliance with strict regulations.

3.1.3 Training and Education

• Personalized learning assistants: Supporting learners with content tailored to their level and interests.

• Dialogue simulation: Creating realistic dialogue scenarios for language training or communication skills.

• Process guidance: Guiding new employees or customers through complex procedures.

3.2 Integration with Existing Systems

One of Parlant's main advantages is its ability to integrate with existing enterprise systems:

3.2.1 Integration with Data Sources

• Relational databases: Connect with SQL databases to retrieve accurate information.

• Unstructured data stores: Integrate with document storage systems like SharePoint or Google Drive.

• APIs and microservices: Call external services to perform specific functions.

3.2.2 Integration with Communication Channels

• Websites and mobile apps: Embedded widgets allow integration of agents into existing interfaces.

• Messaging platforms: Connect with WhatsApp, Messenger, Telegram, etc.

• CRM systems: Integrate with Salesforce, HubSpot, etc., to access and update customer information.

3.3 Scalability and Customization

Parlant's modularity allows for extension and customization in many ways:

3.3.1 Technical Extension

• Module system: Replace or customize core components to meet specific needs.

• Storage customization: Change the storage backend from JSON files to scalable databases like MongoDB.

• Integration with different LLM models: Use OpenAI, Anthropic, or open-source models like Llama.

3.3.2 Industry-Specific Customization

• Specialized glossary: Build a comprehensive dictionary of terms specific to each domain (medical, legal, technical, etc.).

• Regulatory guidelines: Create guidelines to ensure compliance with specific industry regulations.

• Industry-specific tools: Integrate with systems and tools unique to each field.

4. Innovation and Outlook
4.1 Future Directions of Conversation Modeling

Based on our research and analysis, Conversation Modeling will evolve in several notable directions:

4.1.1 Technical Advancements

• Improved Reasoning Capabilities: Develop mechanisms for agents to perform more complex reasoning while still adhering to guidelines.

• Multimodal Integration: Expand the ability to process not just text but also images, audio, and video.

• Multi-agent Models: Allow multiple agents with different roles to work together to solve complex problems.

• Hierarchical Memory: Develop more sophisticated memory mechanisms, allowing agents to retain information across multiple sessions and contexts.

4.1.2 Methodological Advancements

• Automated Guidelines: Develop tools to automatically detect and suggest guidelines based on conversation data.

• Learning from Feedback: Mechanisms allowing agents to learn from user feedback and improve over time.

• Automated Testing: Tools to automatically test and evaluate agent performance in different scenarios.

• Participatory Design Methods: Tools enabling non-technical experts to directly participate in the agent building process.

4.2 Convergence with Other Technologies

We believe Conversation Modeling has the potential to converge with many other technologies, creating more powerful solutions:

4.2.1 Convergence with RAG and Vector Databases

Current Retrieval Augmented Generation (RAG) systems often focus on retrieving accurate information but lack the ability to control how that information is presented and used. The combination of RAG and Conversation Modeling can bring tremendous benefits:

• Guided RAG: Guidelines can direct how the agent searches, uses, and interprets information from vector databases.

• Structured Queries: Instead of direct conversion from natural language to complex SQL queries, Conversation Modeling allows breaking down complex requests into structured parts.

• Controlled Result Interpretation: Ensure information is presented in a way consistent with organizational policies.

4.2.2 Integration with Multi-Agent Systems

Multi-agent systems like AutoGen and CrewAI can benefit from Conversation Modeling:

• Specialized Agents: Each agent in the system can be shaped by separate, specific guidelines.

• Structured Coordination: Guidelines can define how agents interact and coordinate with each other.

• Workflow Integration: Agents can be integrated more powerfully into the organization's workflows.

4.2.3 Combination with Multimodal Generative AI

As generative AI continues to evolve into other modalities such as images, audio, and video, Conversation Modeling can expand to control multimodal interactions:

• Guidelines for Multimodal Content: Shape how agents create and use images and audio in conversations.

• Integration with Multimodal Tools: Connect with image, video, and voice generation tools to create richer experiences.

• Controlled Multimodal Interaction: Ensure generated content adheres to organizational standards and policies.

4.3 Shaping the Future of Interaction Design

Conversation Modeling is not just a new technology but a new approach to interaction design. We believe it can bring significant changes to how we design and build interactive systems:

4.3.1 From Flow Design to Guideline Design

Traditionally, chatbot design focused on defining specific conversation flows. Conversation Modeling shifts from this model to a new one:

• Guideline Design: Instead of defining every possible step, designers define general principles and guidelines.

• Contextual Design: Focus on understanding and reacting to context rather than fixed steps.

• Principle-based Design: Define core values and principles of the system rather than specific scenarios.

4.3.2 Empowering Non-Technical Experts

One of the greatest advantages of Conversation Modeling is its ability to empower non-technical experts to participate in the agent building process:

• Intuitive Tools: Develop user interfaces that allow experts to create and manage guidelines without technical knowledge.

• Knowledge Transfer: Tools to convert expert knowledge into structured guidelines.

• Sharing Community: Platforms that allow sharing and reuse of guidelines across organizations and domains.

4.3.3 Moving Towards Transparency and Explainability

Conversation Modeling promotes transparency and explainability in conversational AI:

• Decision Traceability: The ability to trace why an agent made a specific decision.

• Behavior Explanation: The ability to explain how the agent is applying guidelines in a specific situation.

• Compliance Verification: Tools to test and ensure agent compliance with policies and regulations.

5. Challenges and Considerations

While Conversation Modeling and Parlant offer significant benefits, they still face several important challenges and considerations:

5.1 Technical Challenges
5.1.1 Complexity of Guidelines

As the number of guidelines increases, issues of conflict and complexity may arise:

• Conflicts between guidelines: Different guidelines may conflict or create contradictory requirements.

• Managing large numbers of guidelines: As the system grows, managing and maintaining hundreds or thousands of guidelines can become challenging.

• Optimizing performance: Evaluating a large number of guidelines in real-time can impact performance.

5.1.2 Scalability Challenges

Scaling Parlant for large-scale applications poses several challenges:

• Performance with many users: Ensuring low latency when serving thousands or millions of users simultaneously.

• Efficient storage and retrieval: Managing large volumes of session data and user information.

• Cost optimization: Balancing response quality and language model API costs.

5.1.3 Integration with Complex Databases

Accurate information retrieval from complex databases remains a challenge:

• Generating accurate queries: Conversation Modeling does not directly solve the problem of converting natural language to complex SQL.

• Handling complex data: Working with complex structured data such as images, audio, or multi-layered relational data.

• Query performance: Ensuring efficient queries in large databases.

5.2 Organizational and Process Challenges
5.2.1 Skill Requirements and Training

Adopting Conversation Modeling requires certain skills and training:

• Understanding LLMs: Need to understand how LLMs work to create effective guidelines.

• Conversational design skills: Ability to design natural and effective conversational experiences.

• New development processes: Need to establish processes for developing, testing, and improving guidelines.

5.2.2 Ethical and Legal Considerations

Like all AI technologies, Conversation Modeling also raises several ethical and legal considerations:

• Data security: Ensuring user information is protected and processed according to regulations.

• Transparency: Informing users when they are interacting with AI and how their information is used.

• Bias avoidance: Ensuring guidelines do not create or amplify biases.

5.2.3 Knowledge Management

Converting organizational knowledge into guidelines requires effective knowledge management:

• Expert knowledge extraction: Mechanisms to gather knowledge from experts and convert it into guidelines.

• Ensuring consistency: Maintaining consistency among guidelines as they evolve over time.

• Version control: Systems to track changes and manage versions of the conversation model.

5.3 Solutions and Mitigation Strategies

To address the above challenges, we propose several solutions and strategies:

5.3.1 Incremental Development Approach

Instead of trying to build a perfect agent from the outset, an incremental development approach should be adopted:

• Start small: Focus on a small set of core guidelines for the most common scenarios.

• Expand gradually: Add new guidelines as gaps or issues are discovered.

• Continuous improvement: Use real interaction data to improve existing guidelines.

5.3.2 Supporting Tools and Processes

To support the development and management of Conversation Models, supporting tools and processes need to be developed:

• Intuitive interfaces: Tools allowing intuitive management of guidelines, glossary, and context variables.

• Automated testing system: Tools to automatically test agent performance in different scenarios.

• Interaction analysis: Systems to analyze real interactions to identify problems and improvement opportunities.

5.3.3 Database Query Optimization Strategies

To address the challenge of accurate information retrieval, Conversation Modeling can be combined with specialized methods:

• Guided conversational methods: Use guidelines to guide the agent to ask specific questions to gather necessary information before querying.

• Specialized tools for SQL: Integrate specialized tools to convert natural language to SQL.

• Combining RAG and Conversation Modeling: Use RAG to retrieve information and Conversation Modeling to shape how the information is used.

6. Roadmap for Adopting Conversation Modeling

For organizations to successfully adopt Conversation Modeling, we propose a roadmap divided into specific phases:

6.1 Initial Exploration and Experimentation
6.1.1 Assessing Suitability

Not every application is suitable for Conversation Modeling. The first step is to assess suitability:

• Needs analysis: Identify conversational scenarios requiring high control and consistency.

• ROI assessment: Estimate potential benefits versus deployment costs.

• Scope definition: Define an initial scope focusing on a specific domain or function.

6.1.2 Proof of Concept

Deploy a small proof of concept to test feasibility and effectiveness:

• Set up Parlant: Install Parlant and connect to an LLM (e.g., OpenAI, Anthropic).

• Build a basic conversation model: Create a small set of guidelines, a glossary, and context variables for a specific scenario.

• Test and evaluate: Test the agent with a small group of users and collect feedback.

6.2 Development and Expansion
6.2.1 Building a Comprehensive Conversation Model

Based on the proof of concept results, develop a more comprehensive conversation model:

• Expert knowledge acquisition: Interview and work with experts to identify necessary guidelines.

• Glossary construction: Create a complete dictionary of industry-specific terms.

• Context variable design: Determine information to store about users and sessions.

6.2.2 Integration with Existing Systems

Connect the agent with enterprise systems and data:

• Build tool services: Create tools to connect with APIs, databases, and internal systems.

• Set up authentication and authorization: Ensure the agent only accesses data appropriate to the user's permissions.

• Integrate with communication channels: Connect the agent with websites, applications, and messaging platforms.

6.3 Deployment and Continuous Management
6.3.1 Production Deployment

Carefully deploy the agent into a production environment:

• Comprehensive testing: Test the agent in various scenarios.

• Phased rollout: Start with a small user group and gradually expand.

• Monitoring and feedback: Set up systems for monitoring and feedback collection.

6.3.2 Continuous Improvement

Establish a continuous improvement process based on data and feedback:

• Interaction analysis: Analyze real interactions to identify gaps and issues.

• Guideline updates: Adjust and add guidelines to improve performance.

• Performance optimization: Improve performance and reduce costs over time.

7. Conclusion and Recommendations
7.1 Summary of Key Findings

Through in-depth research into conversational AI and Conversation Modeling, we have drawn several key findings:

• Need for control and consistency: Organizations need a balanced approach between the natural language generation capabilities of LLMs and the control and consistency of traditional systems.

• Potential of Conversation Modeling: Conversation Modeling represents a significant leap forward, allowing precise control over AI behavior without sacrificing flexibility.

• Role of Parlant: As an open-source implementation of Conversation Modeling, Parlant provides a solid foundation for building reliable AI agents.

• Business Benefits: Conversation Modeling offers significant benefits in cost, quality, and risk management compared to existing methods.

7.2 Vision for the Future

We believe that Conversation Modeling and Parlant represent an important step forward in the development of conversational AI. In the future, we predict:

• Increased adoption: More and more organizations will adopt Conversation Modeling to build reliable AI agents.

• Ecosystem development: An ecosystem of tools, libraries, and services will develop around Conversation Modeling.

• Standardization: Standards and best practices will emerge as the industry matures.

• Technology convergence: Conversation Modeling will converge with other technologies such as RAG, multi-agent systems, and multimodal AI.

7.3 Recommendations for Stakeholders

Based on our analysis, we offer several specific recommendations:

7.3.1 For Business Leaders

• Assess opportunities: Identify areas within the organization that can benefit from reliable conversational AI.

• Invest in capabilities: Build internal capabilities in Conversation Modeling and conversational AI.

• Establish governance: Develop governance processes to ensure AI agents comply with organizational standards and policies.

7.3.2 For Technical Experts

• Explore Parlant: Implement a proof of concept to understand the capabilities of Conversation Modeling.

• Integrate with existing infrastructure: Evaluate how Parlant can integrate with existing systems and data.

• Contribute to open source: Consider contributing to the Parlant project to improve the platform.

7.3.3 For Industry Experts

• Share domain expertise: Work with technical experts to translate industry knowledge into guidelines.

• Identify use cases: Identify specific scenarios within the industry that can benefit from conversational AI.

• Establish standards: Develop standards and best practices for conversational AI in the industry.

7.4 Final Remarks

Conversational AI is at a crossroads. While large language models have brought significant advancements, deploying them in real-world enterprise environments still faces many challenges. Conversation Modeling, implemented through Parlant, offers a promising path to overcome these challenges.

At KMG, we are committed to continuing research and development in this field, and we invite all stakeholders to join us on this journey to shape the future of conversational AI.

References
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OpenAI (2024). GPT-4 Technical Report. https://openai.com/research/gpt-4
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