How should your AI microagents communicate?

With so many approaches out there, it helps to look at four popular technologies (RPC, REST, GraphQL, and Message Brokers) each fitting different needs, from direct calls to fully event-driven systems. Below, we explore why they matter, how they work, and when to use them.

1. RPC (Remote Procedure Calls)

What It Is

RPC lets one microagent call a function or method on another microagent as if it were a local function. Frameworks like gRPC handle network details, so from a developer’s perspective, it feels like invoking a class method.

Why It’s Great

• Seamless Integration: Code-wise, it mimics local calls, easing development.
• High Performance: Tools like gRPC use binary serialization and can handle streaming, great for real-time or frequent interactions.
• Strong Contracts: Protocol Buffers enforce type safety and can evolve without breaking older clients.

Example

A Speech Processing Microagent calls a Language Model Microagent via gRPC, passing audio data for analysis. From the speech microagent’s perspective, it is just a method call returning text results in real-time.

When to Use

• Performance-Critical Tasks needing quick, function-like calls.
• Language-Agnostic Systems where multiple languages need to communicate effectively.
• Controlled Environments where you can ensure consistent interface definitions (like Protocol Buffers).

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2. REST

What It Is

REST (Representational State Transfer) is a popular style using standard HTTP methods such as GET, POST, PUT, and DELETE. Data is typically exchanged in JSON or XML, making it broadly accessible.

Why It’s Great

• Simplicity: Many developers already know how to use REST endpoints.
• Wide Adoption: Libraries, tooling, and community support abound.
• Language Neutrality: Any microagent capable of making HTTP requests can interact.

Example

A Patient Microagent in a healthcare system exposes an endpoint like

GET /patients/{id}

. A Billing Microagent calls that endpoint to fetch patient info before generating invoices, using JSON for easy data exchange.

When to Use

• Simple or Common Interactions that do not require elaborate message formats.
• Broad Client Support spanning different languages and front-end apps.
• Moderate Performance Needs where HTTP overhead is acceptable.

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3. GraphQL

What It Is

GraphQL is a query language that lets a client specify exactly what data it wants in one request, potentially consolidating data from various sources. It uses a single endpoint but offers flexible, targeted queries or mutations.

Why It’s Great

• Precise Data Retrieval: Avoid over-fetching or under-fetching—clients pick the fields they need.
• Data Aggregation: Great for fetching info from multiple microagents in one shot.
• Strong Typing: A GraphQL schema ensures consistency and clarifies what is available.

Example

A Recommendation Microagent pulling data from a User Profile Microagent and a Product Catalog Microagent can do so in one GraphQL query, retrieving exactly the data needed to generate user-specific recommendations.

When to Use

• Complex Data Requirements where you need info from multiple microagents.
• Single Endpoint Advantage simplifying queries when numerous data relationships are involved.
• Real-Time Updates if you leverage GraphQL subscriptions for reactive use cases.

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4. Message Brokers

What They Are

Message brokers handle asynchronous communication by letting microagents publish events or place messages in queues, which other agents subscribe to or consume. This decouples agents, eliminating the need for direct, point-to-point connections.

Why They’re Great

• Loose Coupling: Each microagent only needs to know event types or queue names, not who is consuming or producing.
• High Scalability: Agents can independently scale up or down to handle increased workloads.
• Event-Driven Architecture: Perfect for multi-step processes triggered by a single event, typical in AI pipelines or real-time analytics.

Example

In an e-commerce system, an Order Microagent publishes an

OrderPlaced

event, which a Payment Microagent subscribes to. After payment, a

PaymentCompleted

event triggers a Shipping Microagent to schedule delivery. Each microagent focuses on its own task, unaware of others’ inner workings.

When to Use

• Loose Coupling Priority where minimal dependencies across microagents is key.
• Highly Scalable Systems with multiple agents handling large event volumes.
• Complex or Real-Time Workflows that demand asynchronous, event-driven flows.

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Final Thoughts

Each communication style shines under different conditions:

• RPC for performance-critical, function-like calls.
• REST for simple, well-known endpoints accessible to almost any technology.
• GraphQL when you need flexible, targeted queries, often from multiple data sources.
• Message Brokers for scalable, loosely coupled, event-driven architectures.

In reality, many AI microagent systems combine these technologies. You might use REST for simpler interactions, gRPC for high-performance tasks, GraphQL to unify data retrieval, and event-driven patterns for workflow orchestration. By picking what suits each scenario best, you keep your microagent architecture efficient, maintainable, and ready to adapt as business and technology demands evolve.