Designing a High-Performance Chat Service for Multi-Agent Systems: Catio's Perspective

Introduction

In the rapidly evolving landscape of artificial intelligence, multi-agent systems have emerged as a powerful paradigm for solving complex problems and making sophisticated decisions. These systems, composed of multiple interacting AI agents, require a robust and efficient means of communication to function effectively. This blog introduces a specialized chat service designed to meet the unique demands of multi-agent systems, with a particular focus on Catio's innovative approach to system architecture recommendations.

Unlike human-centric chat platforms, agent communication requires high-speed message processing, the ability to handle complex hierarchical relationships, and support for context sharing across different levels of the agent hierarchy. Traditional chat services fall short in meeting these requirements, potentially limiting the effectiveness and efficiency of multi-agent systems.

Catio's approach to multi-agent systems revolves around a purpose-built architecture comprising a chief architect, multiple staff architects, and specialized retriever agents. This structure allows for a distributed yet coordinated approach to problem-solving, where each agent contributes its expertise to generate optimal system architecture recommendations. The importance of seamless and efficient communication between these agents cannot be overstated—it forms the backbone of the entire system's functionality and performance.

This blog post delves into the intricacies of designing a chat service tailored for agentic conversations. We explore key requirements, propose a system architecture, and discuss performance optimization techniques and scalability considerations. Drawing upon extensive experience in building high-scale messaging systems, we apply this knowledge to the cutting-edge domain of AI agent communication. In all, we aim to equip AI and ML infrastructure engineers, as well as technical leaders in AI-focused startups, with essential insights for leveraging multi-agent systems to solve complex business problems.

Background and Context

To fully appreciate the innovative chat service design proposed in this whitepaper, it is crucial to understand the landscape of multi-agent systems in AI and the specific challenges they present in inter-agent communication.

Multi-Agent Systems in AI

Multi-agent systems (MAS) represent a pivotal shift in artificial intelligence, moving from single, monolithic AI entities to complex networks of interacting intelligent agents. These systems are designed to solve problems that are too large or complex for a single agent to handle effectively.

At its core, a multi-agent system consists of multiple autonomous entities (agents) that interact with each other to achieve individual or collective goals. These agents can be heterogeneous, with different capabilities, knowledge bases, and objectives. The power of MAS lies in their ability to distribute complex tasks, parallelize problem-solving, and leverage diverse expertise – much like human organizations.

The significance of multi-agent systems in AI is profound. These systems enable:

Expert Precision: Each agent processes intelligence tailored to its specific domain, allowing MAS to synthesize highly specialized knowledge into accurate and optimized outcomes.
Scalability: By distributing tasks across multiple agents, MAS can tackle problems of increasing complexity and scale.
Robustness: The distributed nature of MAS provides redundancy and fault tolerance, as the system can continue functioning even if individual agents fail.
Flexibility: MAS can be dynamically reconfigured to adapt to changing environments or requirements.
Emergence: Complex behaviors and solutions can emerge from the interactions of simpler agents, leading to innovative problem-solving approaches.

For a deeper discussion on Multi-Agent Systems, read our earlier blog posts: Supercharging AI with Multi-Agent Systems and Multi-Agent Systems Fundamentals - a Personal Experience.

Catio's implementation of multi-agent systems for system architecture recommendations exemplifies the power and sophistication of this approach. The Catio system comprises several key agent types:

Chief Architect: This agent acts as the primary orchestrator, coordinating the efforts of all other agents and synthesizing their inputs into a coherent, non-contradictory set of recommendations.
Staff Architects: These are specialized agents, each with deep expertise in a specific domain such as security, databases, or networking. They provide domain-specific insights and recommendations.
Knowledge Retrievers: These specialized agents access and process relevant information from customers' business documents and Catio's knowledge base. They provide essential context and background to inform the architects' decision-making processes.
Architecture Retrievers: These agents excel at recalling customers' current architectures and adapting previous solutions. This enables the system to learn from past experiences and apply that knowledge to new customer scenarios.

The Need for Specialized Chat Services

While this multi-agent approach offers numerous advantages, it also presents significant challenges in inter-agent communication. Traditional chat systems, designed primarily for human-to-human or human-to-AI interactions, prove inadequate for the demands of multi-agent systems. These limitations include:

Insufficient throughput: Most chat services are not designed to handle the high message volumes with very heavy payloads generated by AI agents operating at machine speeds.
Lack of hierarchical querying: Traditional systems often struggle with complex, nested conversation structures common in multi-agent interactions.
Inadequate context management: The ability to clone and share conversation contexts across different agent interactions is typically not supported in standard chat platforms.
Limited scalability: Many existing solutions do not scale well to handle the potentially enormous number of concurrent conversations in a large multi-agent system.

To address these limitations, agentic conversations require specialized chat services with unique capabilities:

Ultra-high-speed message processing: Support for hundreds of large message sends per entity per second.
Hierarchical query support: The ability to traverse and query complex conversation trees efficiently.
Advanced context management: Mechanisms for cloning and sharing conversation contexts across different agent interactions.
Scalable architecture: A design that can efficiently handle a growing number of agents and conversations without performance degradation.

In the following sections, we will delve into the technical details of designing and implementing such a specialized chat service, drawing on extensive experience in high-scale messaging systems and applying this knowledge to the cutting-edge domain of AI agent communication.

Key Requirements for the Chat Service

Designing a high-performance chat service for multi-agent systems requires addressing several key requirements. These are essential for supporting the complex, rapid interactions typical of AI agent communications. This section outlines three critical requirements for our proposed chat service: rapid message processing, support for hierarchical queries, and the ability to clone and share contexts.

High-Speed Message Processing

A critical requirement for our chat service is the ability to handle high-speed message processing. The system must support hundreds of large message sends per entity per second, reflecting the nature of AI agent interactions that occur at machine speeds far surpassing human communication rates.

The importance of high-speed message processing in multi-agent systems is paramount. It is fundamental to:

Real-time decision making: Agents must rapidly exchange information and make decisions to respond to changing conditions or efficiently solve complex problems.
Effective collaboration: High-speed communication enables multiple agents to work together seamlessly, sharing insights and coordinating actions with minimal delays.
System responsiveness: Rapid message processing ensures the overall system remains responsive, even when handling complex queries or managing multiple concurrent conversations.

However, achieving this level of performance presents several technical challenges:

Network optimization: Minimizing latency and maximizing throughput in message transmission is crucial, potentially requiring the implementation of efficient protocols and optimized network configurations.
Database performance: The underlying database must handle high write and read loads, often necessitating advanced indexing strategies and query optimization techniques.
Concurrency management: With multiple agents simultaneously sending and receiving messages, the system must effectively manage concurrent operations to prevent conflicts and ensure data consistency.
Resource allocation: Balancing system resources to handle high message volumes while maintaining overall system stability presents a significant challenge.

Hierarchical Query Support

The second key requirement for our chat service is robust support for hierarchical queries. This feature is crucial due to the transitive property often observed in agent communications, where information flows through multiple levels of agent interactions.

The transitive property in agent communications refers to how information or context passes along a chain of agent interactions. For instance, if agent A communicates with agent B, and agent B then communicates with agent C, there may be scenarios where agent C needs to access or understand the context of the original A-B interaction. This creates a hierarchical structure in conversations that the chat service must navigate and query efficiently.

Use cases for hierarchical queries in multi-agent systems include:

Context propagation: Enabling downstream agents to access relevant context from earlier conversations in the decision-making chain.
Audit trails: Allowing the system to trace the flow of information or decision-making processes across multiple agent interactions.
Complex problem solving: Supporting scenarios where a problem is decomposed and distributed across multiple agents, requiring the aggregation and analysis of results from various levels of the agent hierarchy.

Implementing hierarchical query support presents several technical challenges:

Efficient data structures: Designing database schemas that effectively represent and store hierarchical relationships.
Query optimization: Developing algorithms for efficient traversal and searching of conversation trees.
Scalability: Ensuring that hierarchical queries remain performant as conversation hierarchies grow in depth and breadth.

Context Cloning and Sharing

The third critical requirement for our chat service is the ability to clone and share subsets of chats for context sharing. This feature is essential for maintaining coherence and efficiency in multi-agent communications.

Context cloning and sharing allows agents to:

Initiate new conversations with pre-existing context, reducing redundant information exchange.
Share relevant background information with other agents quickly and efficiently.
Maintain consistency across related but separate conversation threads.

Scenarios where context cloning is crucial include:

Task delegation: When a primary agent assigns subtasks to other agents, sharing the relevant context ensures all agents have the necessary background information.
Parallel processing: Multiple agents working on different aspects of the same problem can share a common context to ensure consistency in their approaches.
Expertise consultation: When an agent needs to consult with a specialist agent, cloning the relevant context allows for more efficient communication.

However, implementing context cloning and sharing also presents challenges in maintaining data integrity and relevance:

Data consistency: Ensuring that cloned contexts remain consistent with the original conversation as both potentially continue to evolve.
Relevance filtering: Developing mechanisms to clone only the most relevant parts of a conversation to avoid overwhelming agents with unnecessary information.
Version control: Managing multiple versions of cloned contexts and tracking their relationships to the original conversations.
Privacy and security: Implementing robust access controls to ensure that sensitive information is not inadvertently shared through context cloning.

These key requirements - rapid message processing, hierarchical querying, and context sharing - form the foundation of our chat service. They enable the complex, dynamic interactions essential in multi-agent systems. The following sections will explore the technical implementation of these requirements through careful system architecture design.

System Architecture Design

This section delves into two key components of our architecture: the database schema and the API design.

Database Schema

Our chat service's database schema efficiently handles multi-agent communications. It balances simplicity and power, supporting high message volumes, complex queries, and context cloning while remaining adaptable to evolving AI conversation needs.

Proposed Schema

Our database schema consists of the following key table:

agentic_chats: Stores the chat messages between various agents.

CREATE TABLE agentic_chats (
    id UUID NOT NULL PRIMARY KEY,
    recommendation_id UUID NOT NULL,
    conversation_id TEXT NOT NULL,
    chat TEXT NOT NULL,
    created_at TIMESTAMP DEFAULT NOW() 
);

This schema design offers several advantages:

Efficient chat retrieval: By using UUIDs as primary keys and including a recommendation_id, we ensure fast lookups and filtering of chats across different recommendation tasks.
Hierarchical conversation tracking: The conversation_id field allows for easy querying of chats between specific agents and represents the hierarchy in our conversation tree.
Chronological ordering: The created_at timestamp enables time-based sorting and retrieval of chat history.

Justification for Chosen Data Structure

Our choice of data structure is driven by the unique requirements of multi-agent AI communications:

UUIDs for primary keys: Enables distributed ID generation, crucial for high-throughput systems where centralized ID generation could become a bottleneck.
Recommendation_id: Allows grouping of chats related to a specific recommendation task, facilitating isolation between different tasks.
Conversation_id: Represents the hierarchy in our conversation tree, enabling prefix queries and subset cloning. For example, 'CA.IT_SA' represents a conversation between Chief Architect and IT Staff Architect, while 'IT_SA.KB' represents a conversation between IT Staff Architect and Knowledge Base.
Chat field: Stores the actual content of each message in the conversation.

Scalability and Query Considerations

Our schema design takes into account the need for efficient querying and horizontal scalability:

Prefix Queries: The conversation_id structure (e.g., 'CA.IT_SA') allows for efficient prefix queries. This enables retrieving all conversations involving a specific agent or a specific chain of agents.
Subset Cloning: The hierarchical nature of conversation_ids facilitates easy cloning of conversation subsets. For instance, we can efficiently clone all conversations under 'CA.IT_SA' for further processing or analysis.
Partitioning: The chat_history table can be partitioned by recommendation_id, allowing for efficient sharding across multiple database nodes.
Indexing strategy: We implement carefully chosen indexes to support our query patterns without overly impacting write performance. For example:‍

CREATE INDEX idx_agentic_chats_recommendation_id ON 
    chat_history(recommendation_id);
CREATE INDEX idx_agentic_chats_conversation_id ON
    chat_history(conversation_id);
CREATE INDEX idx_agentic_chats_created_at ON
    chat_history(created_at);

Denormalization: The current schema is already denormalized, with conversation details stored directly in the chat_history table, reducing the need for complex joins and improving query performance at scale.

API Design

Our API design focuses on providing a clean, efficient interface for multi-agent interactions while ensuring high performance and scalability.‍

Fast Message Appends: This endpoint allows for quickly adding new messages to a conversation between two agents within a specific recommendation context.

rpc AppendMessage (AppendMessageRequest) returns (AppendMessageResponse);

Hierarchical Querying: This endpoint retrieves chat messages between two agents for a specific recommendation, with an option to include all sub-conversations in the hierarchy.

rpc GetConversation (GetConversationRequest) returns (stream ChatMessage);

Context Management: This endpoint facilitates the cloning or sharing of conversation contexts from an initial conversation to a target conversation, with an option to include the entire subtree of conversations.

rpc CloneContext (CloneContextRequest) returns (CloneContextResponse);

‍

Design Principles: RESTful vs gRPC

For our chat service, we've opted to use gRPC exclusively, forgoing RESTful APIs completely. This decision was driven by several key factors:

Performance: gRPC's use of Protocol Buffers for serialization and HTTP/2 for transport results in significantly faster communication, crucial for high-frequency AI agent interactions.
Bi-directional streaming: gRPC's support for bi-directional streaming allows for more efficient, real-time communication between agents, essential for our use case.
Strong typing: gRPC's use of Protocol Buffers provides strong typing, reducing errors and improving overall system reliability.
Language agnostic: gRPC's language-agnostic nature allows for seamless integration across various AI agent implementations, regardless of their underlying technology stack.

While REST offers familiarity and broad compatibility, the performance gains and advanced features of gRPC make it the superior choice for our high-performance, AI-driven chat service. The initial learning curve is outweighed by the long-term benefits in scalability and efficiency.

Authentication and Security Measures

Security is paramount in our chat service design. We implement the following robust measures:

JSON Web Token (JWT) Authentication: All incoming client-side calls are authenticated using JWTs, ensuring secure and stateless authentication.
Obfuscated Customer IDs: Post-authentication, we generate obfuscated customer IDs that are only translatable within an active client session. All backend services operate with these obfuscated IDs, significantly reducing the risk of customer data exposure.

This approach of using obfuscated IDs is the gold standard for persisting customer data securely. It adds an extra layer of protection, ensuring that even if there's a data breach, the actual customer identities remain protected.

Service Mesh with mTLS: We utilize a service mesh architecture where all service-to-service communication occurs over mutual TLS (mTLS), providing end-to-end encryption and authentication between services.
Role-Based Access Control (RBAC): We implement strict RBAC policies that only allow specific services to access their corresponding databases, minimizing the attack surface.
Database Security: Our databases employ strict schemas and periodically rotated secrets. This ensures that only authorized services can access and interpret the data, further enhancing our data protection measures.
Rate Limiting: We leverage the rate-limiting capabilities provided by our service mesh to prevent abuse and ensure fair usage of our API endpoints.

Additional security measures include:

Regular security audits and penetration testing to identify and address potential vulnerabilities.
Comprehensive logging and monitoring of all API accesses and operations, enabling real-time threat detection and post-incident analysis.
Encryption of data at rest using industry-standard encryption algorithms.

Our comprehensive security measures create a secure environment for sensitive multi-agent communications, prioritizing data protection and privacy throughout our architecture. This robust framework, seamlessly integrated with our system design, forms the foundation of our specialized chat service. It addresses the unique security needs of AI communications while maintaining optimal performance and scalability. Next, we'll explore performance optimization techniques that build upon this secure base.

Scalability and Future Considerations

This section explores strategies for horizontal scaling, monitoring and analytics implementation, and potential future improvements to ensure our chat service remains at the forefront of AI communication technology.

Horizontal Scaling

This approach allows us to distribute the workload across multiple nodes, ensuring high availability and performance as the system grows.

Strategies for Scaling Across Multiple Nodes

We employ a highly scalable and efficient architecture for our chat service:

Kubernetes-based Deployment: Our stateless service runs in a Kubernetes cluster within a service mesh. This setup allows for dynamic scaling using Horizontal Pod Autoscaler (HPA) and cluster autoscaler, adjusting resources based on throughput and load on each pod.
Automated Load Balancing: Our service ingress automatically manages load balancing, ensuring optimal distribution of incoming requests across available pods.

Data Partitioning and Sharding Approaches

Our data management strategy is tailored specifically for our chat service's unique requirements:

Partitioning by Recommendation ID: We partition our agentic_chats table based on recommendation_id. This approach aligns with our query patterns and optimizes performance, as our most intensive read operations always include a recommendation_id. This strategy enables parallel query processing across multiple partitions, ensuring high throughput and consistently fast response times, even as our data volume grows.
Scalable RDS Infrastructure: Our PostgreSQL instance is hosted on Amazon RDS, which allows us to dynamically scale by adding more partitions as needed. This ensures our database can handle increasing loads without compromising performance.

Maintaining Consistency in a Distributed Environment

We ensure data consistency across our distributed system through the following strategies:

Eventual Consistency: We primarily rely on eventual consistency for our distributed data. This approach allows for high performance and availability, with temporary inconsistencies resolving over time.
Optimistic Locking: For conflict resolution, we implement optimistic locking based on "created_at" timestamps. This method efficiently handles concurrent updates without the need for heavy locking mechanisms.
Partition Isolation: By design, we avoid cross-recommendation transactions. This strategy significantly reduces the need for assuming heavy locks across partitions, further enhancing system performance and scalability.

Monitoring and Analytics

Robust monitoring and analytics are essential for maintaining the health and performance of our chat service, as well as driving continuous improvement.

Implementing Robust Logging and Monitoring Systems

We have implemented a comprehensive logging and monitoring infrastructure tailored to our specific needs:

Agent Observability: We have developed an internal tool that allows us to deeply introspect conversations between agents. We use this tool to fine-tune the parameters that enable each agent to perform its task with minimal iterations and maximum accuracy.
Distributed Tracing: We utilize Jaeger for distributed tracing, allowing us to track requests across our microservices architecture. This helps us quickly identify performance bottlenecks and troubleshoot issues in real-time.
Metrics Reporting: We use Prometheus to collect and report fine-grained metrics from all our services. This enables us to monitor system health, track performance trends, and set up precise alerting thresholds.
Log Aggregation: We leverage AWS CloudWatch for centralized log aggregation across all our services. This allows us to efficiently collect, index, and analyze logs in real-time, providing valuable insights into system behavior and facilitating quick issue resolution.
Real-time Alerting: Based on the metrics from Prometheus and logs from CloudWatch, we have set up automated alerting systems. These notify our operations team of potential issues before they can impact service quality, enabling proactive problem-solving.

Key Performance Indicators for Chat Service Health

We track several KPIs to ensure optimal service performance:

API Latency: Monitor end-to-end latency for each of our core API calls:
- AppendMessage: Aim for sub-50ms latency for message appends.
- CloneContext: Target sub-100ms latency for context cloning operations.
- GetConversation: Strive for sub-200ms latency for conversation retrieval.
System Throughput: Track the number of messages processed per second, ensuring it scales linearly with the number of active AI agents.
Error Rates: Monitor the percentage of failed operations, with a goal of maintaining 99.99% success rate.
Resource Utilization: Track CPU, memory, and network usage across all nodes to identify potential resource constraints.

Future Enhancements

Our next steps focus on enhancing human interaction with our Multi-Agent System. We plan to develop a user-friendly interface where individuals can inquire about recommendations and receive clear explanations of the system's decision-making process.

To achieve this, we'll improve our system in several key areas:

Implement new APIs to facilitate human-AI conversations.
Modify the schema to incorporate a user entity.
Enhance conversation quality for a more natural and personalized experience.
Strengthen security and privacy measures.

Through these enhancements, we aim to foster more effective collaboration between users and AI, making our recommendations more comprehensible and applicable in real-world scenarios.

Conclusion

This blog post has presented a comprehensive design for an innovative chat service tailored to meet the unique demands of multi-agent AI systems. Our solution addresses the challenges of ultra-high-speed message processing, complex hierarchical relationships, and context sharing across different levels of the agent hierarchy.

Key Design Principles and Innovations

Our chat service architecture is built upon several foundational principles:

Performance: Utilizing gRPC and Protocol Buffers for high-speed communication, crucial for AI agent interactions.
Security: Implementing robust measures such as JWT authentication, obfuscated customer IDs, and mTLS for service-to-service communication.
Scalability: Employing Kubernetes-based deployment and data partitioning strategies to handle increasing loads efficiently.
Flexibility: Adopting a modular microservices architecture for easy integration of new features and adaptability to evolving AI communication needs.

Key innovations include:

Hierarchical Conversation Management: Our unique approach to storing and querying hierarchical conversation data is specifically tailored to multi-agent system requirements.
Context Cloning: Facilitating the sharing of conversation contexts across different levels of the agent hierarchy, enabling more sophisticated collaborative problem-solving.
Advanced Monitoring and Analytics: Implementing distributed tracing, metrics reporting, and log aggregation for proactive system optimization and rapid issue resolution.

This innovative chat service design lays the groundwork for the next generation of multi-agent communication systems, positioning Catio at the forefront of AI infrastructure technology.

References and Further Reading

To further explore the concepts and technologies discussed in this whitepaper, we recommend the following resources. These references provide valuable insights into multi-agent systems, distributed computing, and high-performance messaging systems that form the foundation of our chat service architecture.

Academic Papers on Multi-Agent Systems and Distributed Computing

The following academic papers offer in-depth analysis and theoretical foundations for multi-agent systems and distributed computing:

Wooldridge, M. (2009). "An Introduction to MultiAgent Systems" - This seminal work provides a comprehensive overview of multi-agent systems, their principles, and applications.
Durfee, E. H., & Rosenschein, J. S. (1994). "Distributed problem solving and multi-agent systems: Comparisons and examples" - A classic paper comparing distributed problem-solving approaches in multi-agent contexts.
Lamport, L. (1998). "The part-time parliament" - Introduces the Paxos algorithm, fundamental to maintaining consistency in distributed systems.

Industry Whitepapers on High-Performance Messaging Systems

These industry whitepapers provide practical insights into building and optimizing high-performance messaging systems:

Apache Kafka: "Kafka: The Definitive Guide" - A comprehensive guide to Apache Kafka, a distributed streaming platform that can be adapted for high-throughput AI agent communications.
RabbitMQ: "RabbitMQ for System Administrators" - Explores the architecture and best practices for implementing RabbitMQ, a robust message broker that can support complex routing scenarios in multi-agent systems.
Google Cloud Pub/Sub: "Building Scalable Pub/Sub Systems with Google Cloud" - Offers insights into designing globally distributed publish-subscribe systems, which can be adapted for large-scale AI agent networks.
Redis Labs: "Redis Streams and the Unified Log" - Discusses how Redis Streams can be used to implement high-performance, real-time data pipelines suitable for AI agent communication.

Relevant Open-Source Projects and Tools

The following open-source projects and tools can be valuable resources for implementing and extending our chat service architecture:

gRPC (https://grpc.io/) - A high-performance, open-source universal RPC framework that can be used for efficient communication between AI agents and services.
Prometheus (https://prometheus.io/) - An open-source monitoring and alerting toolkit that can be instrumental in implementing the robust monitoring system described in Section 6.2.
Jaeger (https://www.jaegertracing.io/) - A distributed tracing system that can help visualize and troubleshoot complex interactions in a multi-agent environment.

By exploring these resources, readers can deepen their understanding of the technologies and principles underlying our chat service architecture. This knowledge will be invaluable for those looking to implement, extend, or innovate upon the ideas presented in this whitepaper.

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About the Author‍

Amit Saurav is a Co-founding Lead Engineer at Catio, previously a Senior Principal and Staff Engineer at Borderless, Snap, and Amazon where he’s lead a number of 0->1 initiatives, ranging from leading the build-out of a new network observability platform, to leading the cloud native modernization of the crown jewel of Snap’s tech stack — its messaging system, to being the lead and founding engineer on Amazon’s Treasure Truck. Amit specialized in cloud architectures, MLOps and Distributed Systems. Follow Amit on LinkedIn to stay updated on his latest work and insights.

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