Event-Driven Agent Mesh
A streaming AI solution
What is an Agent?
An AI agent is an autonomous system that performs tasks on behalf of a user or system. These agents range from simple chatbots to advanced decision-making systems, making them essential in modern AI applications.
What is an Agent Mesh?
AI agent mesh is the orchestration and coordination of multiple autonomous agents, allowing them to collaborate on tasks, solve complex problems, and respond to real-time data.
An agent mesh is a cutting-edge architectural approach that takes ideas from event-driven architecture (EDA) and builds a network of autonomous, specialized agents—each designed to handle a specific task independently. An agent mesh allows these agents to work collectively, dynamically adapting to real-time data. This structure is particularly powerful for applications requiring rapid decision-making, large-scale automation, or high-throughput data processing, as agents can share information, respond to events, and reassign tasks within the mesh as conditions change.
Unlike traditional multi-agent systems, which involve multiple agents collaborating on tasks, an agent mesh introduces an event-driven architecture where agents can join or exit fluidly, scaling as needed to meet workload demands. This adaptability makes the agent mesh ideal for breaking down complex tasks into manageable components handled by agents that can specialize and optimize each step of a workflow.
Relation to Event Driven Architecture (EDA)
If you are familiar with event-driven orchestration of microservices like SAGA, then you already understand the advantages this approach gives, such as:
Decoupling: Microservices separated concerns, enabling independent updates.
Scalable: Scale only the services that need it.
Faster Development: Teams could work on isolated services independently.
Resilience: Issues in one service didn't bring down the entire system.
In this approach, microservices interact asynchronously over a streaming platform like Apache Kafka. This interaction is a predefined and hardcoded workflow that flows through Kafka topics. Changing the workflow logic will require a rewrite.
Unlike static orchestration of microservices (SAGA), an event-driven Agent Mesh allows LLMs to enable dynamic, real-time orchestration of agents, optimizing workflows and adapting to changing conditions. How LLM Orchestrates Agents:
Task Decomposition: LLM breaks high-level tasks into smaller, actionable subtasks for agents.
Agent Assignment: Assign each subtask to the best-suited agent (e.g., data retrieval, analysis, decision-making).
Workflow Coordination: LLM defines task sequences, ensuring agents execute in the correct order based on dependencies.
Real-Time Adaptation: Monitors agent outputs and dynamically adjusts workflows, triggering new agents as conditions evolve.
Event-Driven Execution: Uses an event-driven model to activate agents as subtasks are completed.
Scaling an agent mesh with an event-driven architecture and Kafka can greatly improve flexibility, responsiveness, and scalability. Kafka’s distributed, high-throughput messaging capabilities align well with an agent mesh's dynamic, modular nature, where agents often need to process and respond to real-time events.
Agent Mesh with LlamaIndex
Example Workflow with Event-Driven Agent Mesh using Kafka
Let’s consider a real-time data processing workflow:
Data Ingestion: A data ingestion agent consumes data from a source and publishes it to a "data-ingest" Kafka topic.
Data Cleaning: A data-cleaning agent consumes messages from the "data-ingest" topic, processes them, and publishes the cleaned data to a "data-cleaned" topic.
Feature Extraction and Enrichment: A feature engineering agent consumes the cleaned data, extracts features, enriches it, and publishes it to a "feature-data" topic.
Model Inference: An inference agent consumes enriched data, applies an ML model, and publishes predictions to an "inference-results" topic.
Result Aggregation: A reporting agent aggregates results and publishes a final report to a "report" topic.
Each agent independently subscribes to relevant topics and processes messages as they arrive, without the need for direct interaction with other agents.
Advantages of Using Kafka in an Event-Driven Agent Mesh
Horizontal Scalability: Kafka’s distributed architecture allows the agent mesh to handle high-throughput demands, ensuring that more agents or consumers can be added without bottlenecks.
Low Latency: Kafka supports high-speed message delivery, enabling real-time event processing and rapid response across agents in the mesh.
Loose Coupling: Agents communicate through Kafka topics rather than direct dependencies, enabling independent updates and scalability.
Event Persistence: Kafka’s durable message storage guarantees that no data is lost in transit, which is critical for workflows that require high reliability or transactional integrity.
By leveraging Kafka, an agent mesh can become a robust, scalable system that reacts to events with minimal latency and achieves complex processing tasks through decentralized, loosely coupled agents that scale effortlessly in response to changing demands. This combination of event-driven architecture with Kafka enables the agent mesh to achieve high availability, reliability, and elasticity, which are essential in modern, data-intensive applications.
Isn't it overkill? How many messages would a few agents actually sent between each other? A PostgreSQL-like solution could offer lower latency and be much easier to query imo