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Glossary

Inter-Process Communication (IPC)

Inter-Process Communication (IPC) is a set of programming interfaces that allow different processes to exchange data and synchronize execution, using mechanisms like shared memory, message queues, pipes, or sockets.
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AGENT COMMUNICATION PROTOCOLS

What is Inter-Process Communication (IPC)?

A foundational concept in multi-agent system orchestration, enabling autonomous software agents to collaborate.

Inter-Process Communication (IPC) is the set of programming interfaces and mechanisms that allow independent, concurrently running processes to exchange data and synchronize their execution. In the context of multi-agent systems, IPC provides the essential channels for autonomous agents to share information, delegate tasks, and coordinate actions, forming the backbone of agent communication protocols. Core IPC mechanisms include shared memory, message queues, pipes, and sockets.

For effective agent orchestration, IPC must be reliable, low-latency, and support patterns like publish-subscribe or request-response. It is the underlying transport for higher-level protocols like FIPA ACL or gRPC. Choosing the right IPC mechanism is critical for system performance, influencing fault tolerance, state synchronization, and overall orchestration observability in distributed AI architectures.

INTER-PROCESS COMMUNICATION

Key IPC Mechanisms

Inter-Process Communication (IPC) enables data exchange and synchronization between isolated processes. The following mechanisms form the foundational toolkit for building distributed and multi-agent systems.

01

Shared Memory

Shared Memory is an IPC mechanism where multiple processes access the same region of physical memory, allowing for extremely high-speed data exchange. It is the fastest IPC method because it avoids kernel involvement and data copying after the initial setup.

  • Key Concept: Processes map a common memory segment into their own address space.
  • Synchronization Required: Since memory is shared, processes must use semaphores, mutexes, or other synchronization primitives to prevent race conditions and ensure data consistency.
  • Use Case: Ideal for high-performance computing, real-time systems, and when large volumes of data need to be passed frequently between co-located processes.
02

Message Queues

Message Queues provide asynchronous, decoupled communication where messages are stored in a queue by a sender and later retrieved by a receiver. Messages are typically handled in a First-In-First-Out (FIFO) order, though some systems support prioritization.

  • Core Features: Messages are discrete packets with a defined structure. The queue acts as a buffer, decoupling the timing of production and consumption.
  • Reliability: Many message queue implementations offer persistence, ensuring messages survive process or system restarts.
  • Use Case: Foundational for Message-Oriented Middleware (MOM), task distribution in microservices, and implementing reliable communication patterns between agents.
03

Pipes & Named Pipes (FIFOs)

Pipes are a unidirectional IPC channel, typically used for communication between a parent and child process. Data written to the write end is read from the read end. Named Pipes (or FIFOs) exist as a file in the filesystem, allowing unrelated processes to communicate.

  • Anonymous Pipes: Created with the pipe() system call, exist only for the lifetime of the processes, and are usually used for related processes.
  • Named Pipes (FIFOs): Created with mkfifo, have a persistent presence in the filesystem, enabling communication between any processes that know the pipe's name.
  • Use Case: Pipes are classic in Unix/Linux for connecting the standard output of one command to the standard input of another (ls | grep). Named pipes are used for simple, persistent inter-process data streams.
04

Sockets

Sockets are a generalized network IPC interface that can facilitate communication between processes on the same machine (Unix Domain Sockets) or across a network (Network Sockets using TCP/UDP). They provide a full-duplex, bidirectional communication channel.

  • Unix Domain Sockets: Extremely efficient for local IPC, using the filesystem as a namespace for addressing.
  • Network Sockets: Enable distributed systems. Protocols like TCP provide reliable, ordered delivery, while UDP offers connectionless, best-effort datagrams.
  • Use Case: The backbone of networked applications, client-server architectures, and is the underlying transport for higher-level protocols like gRPC and WebSocket connections.
05

Remote Procedure Call (RPC)

Remote Procedure Call (RPC) is a protocol that allows a program to execute a function or procedure on another address space (another process or machine) as if it were a local call. It abstracts the complexities of network communication.

  • Mechanism: The client calls a local stub function, which marshals parameters into a message, sends it to the server, waits for the response, unmarshals it, and returns the result.
  • Modern Implementations: Frameworks like gRPC (using HTTP/2 and Protocol Buffers) and JSON-RPC provide structured, efficient RPC.
  • Use Case: Fundamental for building distributed systems, microservices, and client-agent interactions where a synchronous request-response semantic is required.
06

Semaphores & Mutexes

Semaphores and Mutexes are synchronization primitives, not data-transfer IPC mechanisms per se, but are critical for safe IPC. They control access to shared resources (like shared memory or a queue) to prevent race conditions.

  • Semaphore: A counter used to control access to a pool of resources. Operations are wait() (decrement) and signal() (increment).
  • Mutex (Mutual Exclusion): A binary lock that ensures only one thread or process can enter a critical section of code at a time.
  • IPC Role: When using Shared Memory or any shared state, these primitives are mandatory to ensure data integrity and coordinated access between concurrent processes.
CORE PROTOCOLS

IPC Mechanism Comparison

A technical comparison of foundational Inter-Process Communication (IPC) mechanisms used for data exchange and synchronization between autonomous agents or processes.

Feature / MetricShared MemoryMessage QueuesPipes (Named & Anonymous)Sockets (Local/Network)

Communication Model

Direct memory access

Structured message passing

Unstructured byte stream

Structured message or byte stream

Synchronization Required

Speed (Latency)

< 1 µs

10-100 µs

50-200 µs

100 µs - 10 ms

Data Structure

Raw bytes / objects

Typed messages

Byte stream

Messages or streams

Inherent Decoupling

Built-in Persistence

Cross-Machine Capability

Typical Use Case

Ultra-low-latency data sharing

Asynchronous task/job processing

Linear shell/process pipelines

Networked agent or service communication

INTER-PROCESS COMMUNICATION (IPC)

Frequently Asked Questions

Inter-Process Communication (IPC) is the set of programming interfaces that allow different processes to exchange data and synchronize execution. This FAQ addresses its core mechanisms, role in multi-agent systems, and key protocols.

Inter-Process Communication (IPC) is a set of programming interfaces that allow separate, concurrently running processes to exchange data and synchronize their execution. It works by providing standardized mechanisms for processes, which have isolated memory spaces, to share information. Core IPC mechanisms include shared memory (a region of RAM accessible by multiple processes), message passing (exchanging discrete packets via queues or pipes), and synchronization primitives (like semaphores or mutexes) to coordinate access. In a multi-agent system, IPC is the foundational layer enabling autonomous agents, often running as distinct processes or on different machines, to collaborate by sending requests, sharing results, and negotiating tasks.

Prasad Kumkar

About the author

Prasad Kumkar

CEO & MD, Inference Systems

Prasad Kumkar is the CEO & MD of Inference Systems and writes about AI systems architecture, LLM infrastructure, model serving, evaluation, and production deployment. Over 5+ years, he has worked across computer vision models, L5 autonomous vehicle systems, and LLM research, with a focus on taking complex AI ideas into real-world engineering systems.

His work and writing cover AI systems, large language models, AI agents, multimodal systems, autonomous systems, inference optimization, RAG, evaluation, and production AI engineering.