Behind the AgentFlow: How we build a scalable agentic AI system for smart conversations

In this blog, we’ll walk you through how SleekFlow developed AgentFlow—a specialized AI tool that enables you to build teams of AI agents to qualify contacts, nurture leads, and retain customers.

Challenges of implementing Agentic AI

Most AI chatbots today:

• Struggle with complex, multi-step conversations, leading to poor user experiences.

• Don’t easily connect or interact with existing business systems and data.

• Provide inconsistent and unreliable answers, reducing trust and effectiveness.

These problems occur because of architectural limitations in how they are built:

• Single-agent bottleneck: Lacks flexibility for handling complex, multi-step tasks and adapting to scenarios outside of their original training.

• Vector database over-reliance: Struggles with ambiguous or varied user inputs, failing to maintain context and consistency throughout interactions.

• Single LLM implementation: Introduces vulnerabilities in error handling and cost efficiency, making them difficult to reliably scale across a business.

How does SleekFlow’s AI Engine work?

SleekFlow AI addresses these challenges by carefully engineering a multi-agent architecture combined with a hybrid Graph-Vector Retrieval-Augmented Generation (RAG) system and multiple large language models.

Stage 1: Query Processing

Advanced data processing

SleekFlow’s AI leverages advanced data processing techniques to handle complex file formats that go beyond the capabilities of traditional OCR systems. The AI can process spreadsheets with nested data structures, and visual-heavy documents, such as leaflets, which include images and tables. This enables the AI to extract structured information more accurately and efficiently, even when dealing with non-standard or multi-modal data.

The AI supports a wide range of document types, including Docs, Excel files, Websites, and PDFs. It uses data parsing algorithms to extract information from these documents, ensuring context-aware responses.

In the future, SleekFlow plans to expand support for additional databases and third-party integrations, further enhancing the system’s flexibility in handling diverse data formats.

Hybrid retrieval-augmented generation

To further optimize performance, SleekFlow’s AI leverages vector databases for fast, scalable data retrieval, particularly in RAG tasks. These databases allow the system to quickly access relevant information, enabling dynamic and contextually relevant responses.

In parallel, graph databases are used to model and understand complex relationships between data points, providing the AI with a richer, more comprehensive understanding of the knowledge base.

This combination of specialized models and advanced databases allows SleekFlow’s AI to deliver highly accurate, context-aware responses that outperform traditional AI systems, making it uniquely suited to tackle the complexity of real-world customer interactions.

Integrate with Multiple LLMs

SleekFlow’s AI system integrates a variety of models from multiple providers, including 4o, 4o-mini, and Gemini. Each model is selected based on its specific capabilities in processing different types of data and tasks. This approach ensures that the most suitable model is assigned to each interaction, enhancing performance by utilizing the strengths of each model for optimal results in areas such as natural language understanding (NLU) and data retrieval.

Stage 2: Response Generation

How SleekFlow’s AI Agents reduce hallucinations

Hallucinations can happen when the AI forgets important details, especially when handling long context. SleekFlow's approach to minimizing hallucinations is multi-faceted:

• Utilizing advanced models for complex queries:

  Advanced models like OpenAI o3-mini are employed to handle complex queries with higher precision. These models are fine-tuned for specific tasks, enabling the AI to process and understand nuanced user input. Unlike general-purpose models, is a larger and stronger model, with a deeper understanding of diverse texts. This allows it to handle complex scenarios more effectively and generate better responses in challenging conversational situations.

• Task-specific agents for improved accuracy:

  Each agent in the system has a specific task, following a modular approach to reduce the likelihood of context loss. For example, one agent may be responsible for retrieving factual data, while another focuses on generating responses based on that data. This division of labor allows the AI to perform role-based processing, ensuring that each agent stays focused on a narrow domain of expertise. By limiting the scope of each agent’s responsibilities, we reduce the potential for conflicting outputs and ensure that complex customer queries are handled efficiently with minimal context errors.

• Independent reviewers for quality control:

  An independent reviewer is integrated into the system to cross-check responses before they are sent to the customer. This reviewer agent analyzes the AI’s output for factual accuracy, coherence, and language consistency. It verifies that the response aligns with company knowledge, ensuring that there are no discrepancies or misinterpretations. Additionally, the reviewer ensures that the AI does not overgeneralize or hallucinate information, making the final output more reliable and trustworthy for customers.

• Addressing knowledge gaps in real-time:

  Detecting missing knowledge is another key aspect of the system. When the AI identifies gaps in its knowledge, it does not attempt to generate a potentially inaccurate response. Instead, the system records the missing knowledge and flags it for future updates. This approach ensures that the AI maintains accuracy, capturing knowledge gaps without providing unreliable information.

• Refining prompts for consistent results:

  Prompt refinement is an ongoing process that plays a crucial role in enhancing the AI’s output consistency. Prompts are carefully designed and tested to ensure they are clear, precise, and unambiguous. Through feedback loops and real-time testing, prompts are adjusted to account for edge cases and specific scenarios that may not have been previously anticipated. This process helps improve the AI’s ability to interpret user queries accurately, leading to more consistent responses across various interaction types.

Different modes of response in SleekFlow's AI Agent

SleekFlow offers two ways of generating responses, depending on the business use case:

• Basic support: A single AI agent handles all tasks efficiently, ideal for web widgets that provide fast answers to common questions. It prioritizes speed and uses basic knowledge retrieval for quick responses, perfect for straightforward or high-volume support.

• Sales growth: A group of specialized agents collaborates within a refined workflow to handle complex queries and lead qualification. It prioritizes quality and uses advanced checks and personalization to generate thoughtful, context-aware responses, best suited for complex or sales-related inquiries.

In complex conversation, the multi-agent architecture improves efficiency by assigning clearly defined tasks to each agent, minimizing task overlap, and maximizing specialized expertise. It incorporates cross-verification mechanisms between agents, ensuring consistent output quality. Additionally, error-handling protocols are embedded to mitigate potential failures, enhancing the overall system’s reliability.

The smooth collaboration between agents is facilitated by the use of Semantic Kernel, which provides structured communication protocols for effective inter-agent interaction. This architecture is extensible, enabling customization of agent behaviors and allowing continuous testing to ensure scalability and adaptability to evolving business needs.

How SleekFlow AI generate overview-type answers

When generating overview-type answers, SleekFlow’s AI leverages a graph database to index and structure information. This allows the AI to map relationships between data points, enabling it to generate broad, contextually aware responses.

The graph-based approach ensures that the AI understands the interconnections within the knowledge base, which is crucial for delivering comprehensive answers on complex topics.

By utilizing this method, the AI is not only capable of handling simple queries but also excels in more intricate tasks, such as lead qualification and multi-step inquiries, where responses require a deep understanding of the context and related information.

This ensures that each conversation flows smoothly and efficiently, with specialized agents contributing to a well-organized, high-quality response.

Stage 3: Performance Optimization

Identifying knowledge gaps and ensuring accuracy

The AI retrieval agent utilizes advanced knowledge retrieval algorithms to identify knowledge gaps, outdated data, and conflicting information within the knowledge base. By leveraging graph database technology, the agent understands the relationships between data points and flags issues for review. This allows the system to ensure that responses are consistently accurate and up-to-date.

Human agents can also review AI-generated responses directly in the inbox, ensuring quality control. In the future, SleekFlow plans to introduce a more refined UI, providing agents with enhanced tools for streamlined review and fine-tuning of AI-generated answers. This will optimize the process of identifying and correcting errors, allowing businesses to maintain high standards of quality across AI-powered interactions.

Continuous testing and optimization

To ensure consistent, high-quality performance, SleekFlow’s AI undergoes automated testing whenever there are changes in group structure, prompts, or datasets. This allows us to establish performance benchmarks and track the AI’s optimization progress over time. During testing, any hallucinations—incorrect or irrelevant responses—are flagged and incorporated into the test suite to reduce the likelihood of future occurrences.

Furthermore, the AI’s prompt instructions are continually refined and tested to ensure clarity, effectiveness, and consistency in handling various queries. We use rigorous validation techniques to evaluate how the prompts impact the quality of responses across different scenarios, ensuring the AI consistently meets desired performance standards.

This iterative approach to testing and optimization ensures that SleekFlow’s AI evolves to become increasingly reliable and accurate, minimizing errors and improving overall performance. As a result, the AI is capable of handling complex tasks such as lead qualification and multi-step inquiries with specialized agents, ensuring seamless and contextually relevant interactions.

Security and Compliance

SleekFlow takes your data security and privacy seriously. Any information you upload to your AI knowledge base is stored securely on dedicated infrastructure, completely isolated from any large language model's (LLM) training environment.

When generating responses, SleekFlow retrieves only relevant data from your dedicated database and uses it strictly as external context in prompts—your data is never used to train or fine-tune general-purpose AI models.

Additionally, we safeguard your AI knowledge base with the same rigorous encryption standards and security measures that protect your conversations and contacts across our entire platform. Rest assured, SleekFlow employees and third-party providers have no access to your data unless you explicitly request assistance and provide permission.

With SleekFlow AI, you can trust that your data is securely managed and compliant with the latest privacy and security standards:

ISO/IEC 27001 Certification

A globally recognized standard that ensures SleekFlow has a robust Information Security Management System (ISMS) in place, effectively safeguarding sensitive data. Learn more

SOC 2 Type II Certification

This certification validates that SleekFlow's security practices are not only well-designed but also consistently maintained over time, guaranteeing the protection of customer data and ensuring platform reliability. Learn more

GDPR Compliance

We are committed to data privacy by fully adhering to the General Data Protection Regulation (GDPR), ensuring that your personal data is managed with transparency, consent, and the highest security standards. Learn more

Conclusion

SleekFlow’s AI system is built to handle the complexities of modern business communication, offering more than simple conversational automation. By utilizing multi-agent architecture and graph database technology, SleekFlow delivers scalable, reliable, and intelligent interactions.

The system is designed to optimize performance continuously, with advanced models, rigorous testing, and ongoing optimization. As a result, SleekFlow’s AI provides high-quality, personalized responses, helping businesses scale effectively while minimizing errors and ensuring seamless customer experience.