What Is RPC?

Remote Procedure Calls, often shortened to RPC, allow one piece of software to call a function or method in another piece of software that is running somewhere else. From the caller’s perspective, it feels like a local function call, but behind the scenes, the call is sent over the network. The remote system processes the request and returns results, acting as if both systems were part of the same codebase. This approach can reduce the complexity of network communication for developers, who can write code that looks local even though it may be talking to a service across different machines or environments.

RPC can sound similar to having SDKs or API wrappers in your code. From a developer’s standpoint, a remote function call can look a lot like a local function call. The difference lies in how these calls are implemented under the hood. In a microagent setting, RPC can be a powerful communication pattern, especially for scenarios that require immediate results, strong data contracts, or high performance. Below are four specific situations where RPC truly excels.

When to Use RPC for Microagent Communication

1. Real-Time Processing

Why It Matters

When your microagents need immediate results, a request-response model like RPC is often the best fit. A function call with a prompt reply allows any agent to proceed quickly to the next step.

Example Use Cases

• Payment Validation: A Payment Validation Microagent can request an authorization check from a Banking Microagent. It receives an approval or denial in real time so the user is not left waiting.
• AI Model Inference: A Speech Processing Microagent can send audio data to a Language Model Microagent, receiving text output in seconds rather than minutes or in uncertain time windows.

Benefits

• Immediate Feedback Loop: Great for tasks that cannot wait for asynchronous workflows.
• Straightforward Error Handling: If something goes wrong, the caller knows right away and can attempt retries or alternative logic.

2. Remote Device Control

Why It Matters

Some microagents manage external devices, such as IoT sensors or robotic arms. You may want the client or orchestrating agent to issue commands without learning each device's specific logic.

Example Use Cases

• Smart Home Agents: A Home Control Microagent can call turnOnLights or setThermostat without having to handle specialized protocols.
• Industrial Equipment: A Factory Control Microagent can issue commands like startConveyor or readSensorData to device-specific agents, reducing complexity in the main controller.

Benefits

• Minimal Client Complexity: The remote logic stays on the server side.
• Consistent Call Patterns: Whether you are controlling lights or robotic welders, a single function invocation pattern can apply.

3. Strict Control with Schema

Why It Matters

RPC frameworks like gRPC typically use Protocol Buffers for data serialization. Others like SOAP rely on WSDL for contract definitions. These schemas ensure consistent data exchange and can auto-generate client and server stubs in various programming languages.

Example Use Cases

• Financial Systems: Accounting or ledger microagents can share transaction data with a Reporting Microagent in a precise, validated manner. This avoids data mismatches.
• Healthcare Data: A Patient Management Microagent can securely provide medical records to a Specialist Microagent, ensuring fields are typed and consistent.

Benefits

• Clear Versioning: New fields can be added with minimal risk of breaking older clients.
• Multi-Language Stubs: Code generation can produce client libraries for Java, Python, and more.

4. High Performance

Why It Matters

Technologies like gRPC use binary serialization instead of text-based formats. They also take advantage of HTTP/2, allowing multiple requests to share a single connection.

Example Use Cases

• Machine Learning Model Serving: A Model Inference Microagent can efficiently receive large datasets from a Preprocessing Microagent without text parsing overhead.
• Real-Time Gaming or Streaming: Multiple microagents handling user sessions or media streaming can benefit from low-latency calls.

Benefits

• Smaller Payloads: Reduces network bandwidth and speeds up communication.
• Connection Multiplexing: Multiple calls can share one channel, improving throughput and lowering latency.

VS an API Wrapper

When you look at a simple API wrapper around a REST endpoint, it can feel similar to an RPC approach at first glance: you write a function call in your code, and it returns data as if it were local. Under the surface, however, the experience and implementation differ in some important ways. Below, we walk through the code examples for both a REST-based “ApiWrapper” and a simple XML-RPC service to highlight these distinctions.

1. REST API Wrapper: Code Walkthrough

How the Wrapper Works

import requests

class ApiWrapper:
    def __init__(self, base_url):
        self.base_url = base_url.rstrip('/')

    def get_user(self, user_id):
        url = f"{self.base_url}/users/{user_id}"
        response = requests.get(url)
        response.raise_for_status()
        return response.json()

This

ApiWrapper

class builds and sends an HTTP GET request to a specific URL, then interprets the response as JSON. Here’s what’s happening under the hood:

1. Constructing URL Paths: The wrapper manually assembles the endpoint (

   /users/{user_id}

   ) and calls the correct HTTP method (GET in this case).
2. HTTP-Specific Details: Error handling relies on

   response.raise_for_status()

   , which raises exceptions for HTTP error codes like 404 or 500.
3. Manual JSON Parsing: Once data is received, the wrapper explicitly calls

   response.json()

   to parse text-based JSON into a Python dictionary.

On the Server Side (Using Flask)

@app.route('/api/users/<int:user_id>', methods=['GET'])
def get_user(user_id):
    user = users.get(user_id)
    if user:
        return jsonify(user)
    else:
        abort(404, description="User not found")

A typical REST endpoint must handle HTTP verbs, status codes, and text formats (JSON, XML, etc.). If your code changes from JSON to XML, you’ll need to adjust both the server logic and the client calls or parsing routines.

Key Observations

• Network Awareness: The wrapper is explicitly dealing with HTTP URLs, query parameters, error codes, and data parsing.
• Loose Contract: While you might define a schema (like OpenAPI) somewhere, the client itself depends on your knowledge of the endpoint. Any changes to the endpoint’s data format or URL can break the code unless you update the wrapper.
• Multiple Steps: Each new endpoint often requires manual code changes—adding new methods, adjusting parameters, or changing how data is serialized and deserialized.

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2. RPC via XML-RPC: Code Walkthrough

The Client Side

import xmlrpc.client

client = xmlrpc.client.ServerProxy("http://localhost:8000/")

try:
    user = client.get_user(1)
    print(f"User fetched: {user}")
except Exception as e:
    print(f"Error fetching user: {e}")

Unlike the

ApiWrapper

, there is no explicit URL building or JSON parsing here. The

xmlrpc.client.ServerProxy

object acts as if it provides a direct set of methods you can call, such as

get_user(1)

.

The Server Side

from xmlrpc.server import SimpleXMLRPCServer

def get_user(user_id):
    users = {
        1: {"id": 1, "name": "John Doe", "email": "john@example.com"},
        2: {"id": 2, "name": "Jane Smith", "email": "jane@example.com"},
    }
    return users.get(user_id, {})

server = SimpleXMLRPCServer(("localhost", 8000))
server.register_function(get_user, 'get_user')
server.serve_forever()

Here, you expose a plain Python function,

get_user(user_id)

, which the server registers. When the client calls

client.get_user(1)

, it is effectively calling the

get_user

function on the remote server, but it appears almost like a local function in your code.

Key Observations

• Function-Like Calls: You call

  get_user(1)

  as if you are calling a local function. The networking details (method, path, or JSON parsing) are hidden by the XML-RPC protocol.
• Schema/Method Enforced: In many RPC frameworks (like gRPC), the contract is specified in a file (like a

  .proto

  file), and code is auto-generated for both client and server, ensuring consistent data structures and methods.
• Less Manual Overhead: When you define a new function, you simply register it on the server, and the client automatically “sees” it as a callable method (assuming you keep your protocol definitions in sync).

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3. Core Differences to Notice

a. Network Protocol Awareness

• API Wrappers: The client class must handle HTTP details—path building, query parameters, headers, and error codes. If you add a new endpoint, you add a new method and handle the response manually.
• RPC: A framework typically masks the underlying transport. You call a method, and the framework automatically serializes arguments, sends them, and returns results, with little to no manual handling of URLs or JSON.

b. Contract Enforcement

• API Wrappers: Rely on consistent endpoint definitions (for example,

  /users/<id>

  ) plus optional external documentation or specs. Changes in format or path require manual edits to the wrapper or cause runtime errors.
• RPC: Usually demands a strict contract, often via schema definitions like

  .proto

  files for gRPC or WSDL for SOAP. The contract ensures type safety, versioning, and code generation, making it clearer and stricter.

c. Development Complexity

• API Wrappers: Often simpler to start—just write an endpoint and parse responses—but can become cumbersome when you have many endpoints or require strict data validation.
• RPC: May involve more initial setup (schema definitions, code generation), but once in place, changes are versioned and enforced at compile time in many languages, reducing surprises.

d. Tight vs Loose Coupling

• API Wrappers: By using a textual protocol like JSON over HTTP, you gain some flexibility—clients can potentially ignore extra fields or handle changes gracefully, but at the cost of manual checks and code.
• RPC: Tends to be more strongly typed and can lead to tighter coupling if the schema changes frequently. However, it also provides more robust compile-time checks if you manage versions well.

Recap

While both methods let you call a “function” that returns data, an API wrapper over REST calls is still fundamentally dealing with HTTP endpoints, often managing headers, URLs, JSON payloads, and error codes manually. RPC solutions, such as gRPC, mask these network details behind a strict schema, turning remote calls into something that appears local in code. This difference is crucial in microagent setups where real-time performance or tight data contracts matter. By recognizing how each approach handles network protocols, data serialization, and schema enforcement, you can choose the most suitable strategy for your AI microagent architecture.

Final Thoughts

RPC is a strong choice for microagent scenarios that demand real-time results, structured data exchange, or tight performance. By leveraging protocols like gRPC, you can achieve immediate feedback, device-like control of remote services, and efficient serialization via schemas. Keep in mind that RPC calls require a controlled environment to prevent coupling pitfalls. When used judiciously, RPC helps your microagents deliver real-time communication (RTC) with minimal friction, fueling the kind of agile, high-speed system modern AI applications often require.

Key Considerations

1. Controlled Environment: RPC works best when you carefully manage schemas and versioning. If schema changes happen frequently with little governance, dependency issues might arise.

2. Tight Coupling: RPC can create tighter coupling than asynchronous or event-driven systems, since a microagent changes can force updates in all consumers.

3. Complement with Other Approaches: Many systems combine RPC for immediate, high-performance requests with REST or event-driven messaging for scenarios requiring loose coupling.