EDAA: Event Driven Agentic Architecture

The AI landscape rapidly evolves beyond isolated, single-purpose agents toward rich ecosystems of specialized agents collaborating to solve complex problems. Google's Agent-to-Agent (A2A) protocol represents a significant step toward standardizing how these agents communicate. However, implementing it at scale requires more than just the protocol itself—it demands a robust, event-driven architecture that can support complex interactions between multiple specialized agents.

This article introduces a novel approach to implementing a serverless event-driven agent architecture (EDAA) using Google's A2A protocol with AWS services. We'll explore how to leverage AWS EventBridge, SQS, Lambda, Fargate, and other serverless components to create a scalable, resilient, and cost-effective agent ecosystem. To demonstrate these concepts, we'll build a practical legal contract analysis system where specialized agents collaborate to create a comprehensive graph of contract relationships with other contracts and relevant laws.

By the end of this article, you'll understand how to implement Google's A2A protocol in a serverless AWS environment, design an event-driven architecture using AWS EventBridge and SQS, deploy agents using Lambda and Fargate, leverage Amazon Bedrock for AI capabilities, integrate the Model Context Protocol (MCP) for external API access, implement authentication and security best practices, and build a complete, production-ready agent ecosystem using AWS CDK.

The Evolution of Agent Communication

The current AI agent landscape resembles the early web: fragmented and siloed. Agents built on different frameworks often can't communicate effectively. Each is powerful within its ecosystem but isolated from others. Google's A2A protocol aims to solve this by providing a standardized way for agents to announce their capabilities via Agent Cards, exchange structured messages, manage tasks and their lifecycles, and share artifacts and results. However, the A2A protocol alone doesn't address the communication infrastructure needed for complex agent ecosystems.

The current A2A implementation relies on traditional web patterns (HTTP, JSON-RPC, and Server-Sent Events) for direct, point-to-point communication. While functional for simple interactions, this approach has significant limitations at scale. It creates an explosion of N²-N possible integrations for N agents, making the system brittle and complex. Each agent needs to know its peers' exact endpoint, format, and availability, creating tight coupling where if one agent goes down or changes, others break. Point-to-point communication is also inherently private, making it difficult to log, monitor, trace, or replay messages for debugging or compliance. Additionally, multi-agent workflows require separate orchestration layers to manage the flow across systems.

What's missing is a shared, asynchronous backbone where agents can publish what they know and subscribe to what they need—a way to decouple producers and consumers of intelligence. This architectural pattern should sound familiar. It's the same evolution we saw in software architecture: from monoliths where all functionality lived in a single codebase, to microservices with direct communication that still relied on synchronous communication creating complex webs of dependencies, to event-driven microservices where services publish events to a shared broker and others subscribe to events they care about, reducing dependencies from quadratic (NxM) to linear (N+M). The A2A protocol is at stage 2 and needs to evolve to stage 3 with an event-driven backbone.

AWS Services for Event-Driven Agent Architecture

AWS provides a rich ecosystem of services that can be leveraged to build a serverless, event-driven agent architecture. At the heart of our architecture is AWS EventBridge, a serverless event bus that makes it easy to connect applications together using data from your own applications, integrated software as a service (SaaS) applications, and AWS services. It's the perfect backbone for our event-driven agent architecture because it provides a central event bus for all agent communications, supports complex event filtering and routing, integrates natively with other AWS services, scales automatically to handle any volume of events, offers built-in reliability and durability, and provides a pay-per-use pricing model.

While EventBridge is excellent for event routing, Amazon SQS (Simple Queue Service) provides reliable, highly scalable hosted queues for storing messages as they travel between agents. SQS complements EventBridge by providing message buffering to handle traffic spikes, ensuring at-least-once delivery of messages, supporting dead-letter queues for handling failed processing, enabling message batching for improved efficiency, and offering FIFO (First-In-First-Out) queues when message ordering is essential.

AWS Lambda lets you run code without provisioning or managing servers to implement our agents. It's ideal for implementing agents that need to respond to events quickly, have variable or unpredictable workloads, process events statelessly, and require automatic scaling from zero to peak demand. For agents that require more resources, longer running times, or stateful processing, AWS Fargate provides serverless computing for containers. Fargate is perfect for complex agents with larger memory or CPU requirements, agents that maintain state between requests, workloads that benefit from container packaging, and applications that need to expose HTTP endpoints (like A2A servers).

To provide AI capabilities, Amazon Bedrock is a fully managed service that makes high-performing foundation models from leading AI companies available through an API. It's ideal for implementing AI capabilities within agents, analyzing text, generating content, and extracting insights, with access to models like Claude 3.7 Sonnet without managing infrastructure.

For complex workflows involving multiple agents, AWS Step Functions provides a serverless orchestration service that makes coordinating the components of distributed applications easy. It helps define complex agent interaction patterns, manage state transitions in multi-step processes, implement retry logic and error handling, and visualize workflow execution.

Amazon DynamoDB is a fast, flexible NoSQL database service with single-digit millisecond performance at any scale. It's perfect for storing agent state and task information, tracking relationships between entities, implementing event sourcing patterns, and providing high-performance, low-latency data access.

For security, AWS Secrets Manager helps you protect access to your applications, services, and IT resources without the upfront cost and complexity of managing your secrets management infrastructure. It's essential for securely storing API keys and credentials, managing access to external services, rotating credentials automatically, and controlling access to sensitive information.

Example: Legal Contract Analysis System with EDAA

While developing agents for LIDIA, a SaaS platform for legal technology applications, we encountered a representative use case that validated the architecture: a system for legal contract analysis that constructs a graph of relationships between contracts and applicable laws. The implementation involved three specialized agents: a Legal Contract Agent, responsible for parsing contracts and coordinating the workflow; a Document Retrieval Agent, tasked with identifying similar contracts from a repository; and a Legal Knowledge Base Agent, which locates relevant laws and regulations for each contract clause..

Our architecture leverages multiple AWS services to create a robust, scalable system. EventBridge serves as the central nervous system, routing events between agents. SQS Queues buffer events for each agent type, providing resilience against traffic spikes and ensuring reliable delivery. A Dead Letter Queue (DLQ) captures failed message processing attempts for later analysis and reprocessing. DynamoDB stores task states and intermediate results, enabling agents to work asynchronously. Step Functions orchestrates complex workflows across multiple agents, managing state transitions and error handling. CloudWatch provides comprehensive monitoring and logging for the entire system. Secrets Manager securely stores and manages API keys and credentials for external services. API Gateway exposes the Legal Contract Agent's A2A endpoints to external clients. Fargate runs the Legal Contract Agent as a containerized application with its own HTTP server. Lambda functions implement the Document Retrieval and Legal Knowledge Base agents, scaling automatically based on event volume. And Bedrock provides AI capabilities through Claude 3.7 Sonnet for contract analysis.

Here's the overall architecture of our system: