The Internet of Agents (IoA): Protocol for Autonomous AI Collaboration

Why Multi-Agent Systems Need a Rethink

Multi-agent systems (MAS) have been around for a while, but the landscape is shifting. As AI moves beyond isolated tasks toward autonomous, multi-agent collaboration, the existing frameworks start to show their cracks. Most of them are built around static agent roles, rigid communication patterns, and pre-scripted workflows. They assume all agents exist in a controlled, centralized environment, far from the reality of distributed AI ecosystems.

What we need is a system where agents can:

• Find and integrate new teammates dynamically, rather than relying on pre-defined structures.
• Communicate efficiently, following structured dialogue rather than endless back-and-forth exchanges.
• Scale beyond a single execution environment, allowing collaboration across different infrastructures.

The Internet of Agents (IoA) introduced by Chen et al. is designed to address these gaps. Instead of treating multi-agent systems as isolated sandboxes, it takes inspiration from how the internet itself operates — where autonomous entities (services, APIs, or users) discover each other, form temporary collaborations, and exchange information in a structured way.

IoA introduces a protocol-driven approach that allows AI agents to self-organize, delegate tasks efficiently, and execute workflows at scale. This post breaks down how IoA works, why it’s different, and what it unlocks for AI-driven collaboration.

Photo by Rock'n Roll Monkey on Unsplash

Where Existing Multi-Agent Frameworks Fall Short

The limitations of current MAS frameworks boil down to three major issues:

1. Static Agent Roles and Team Formation

Most multi-agent systems are designed with predefined agent roles — one agent retrieves information, another processes it, and a third summarizes. This works fine for simple workflows but falls apart when real-world adaptability is required.

In a dynamic environment, you don’t always know upfront which agents you need. Maybe an AI research task starts as a retrieval job but later requires a statistical modeling agent or a domain-specific knowledge agent. Traditional frameworks don’t support this kind of dynamic team assembly — they assume roles are fixed from the start.

2. Inefficient Communication and Task Delegation

LLM-powered agents today largely communicate via unstructured natural language exchanges, which quickly becomes inefficient. In real-world workflows, you need:

• Explicit task assignments (who’s doing what).
• Status updates (what’s been done, what’s pending).
• Feedback loops to ensure work quality.

Without structured delegation, agents end up repeating steps, making redundant queries, and generating unnecessary responses. Most existing frameworks don’t enforce structured communication protocols, leading to messy, inefficient conversations.

3. Limited Scalability Beyond Single-Device Execution

Many frameworks assume all agents exist within a single computational environment. In reality, AI workflows involve distributed agents running on different machines, cloud services, or infrastructures. A system designed for single-device execution simply does not scale to real-world distributed AI applications.

IoA directly tackles these three issues by introducing modular, decentralized collaboration mechanisms.

How IoA Works: A Decentralized, Scalable Framework for Agent Collaboration

IoA is structured into three core layers, each solving a fundamental problem in multi-agent collaboration.

1. The Interaction Layer: Where Agents Meet and Collaborate

This is the entry point for collaboration — agents find each other, form teams, and initiate structured dialogue. Instead of relying on hardcoded team structures, IoA introduces dynamic discovery and integration.

How it works:

• An agent with a task (e.g., “Write a research paper”) queries an Agent Registry to find the most relevant teammates.
• It forms a temporary working group based on capabilities (e.g., a Google Scholar Agent for research, a PDF Agent for extracting insights, and an Academic Writing Agent for structuring content).
• A structured conversation session is launched, where agents delegate tasks and track progress.

The key difference? IoA automates the formation of agent teams, mirroring how human teams form on demand in Slack or Discord.

2. The Data Layer: Structuring Conversations and Tasks

Once a team is formed, IoA ensures that conversations are structured, and tasks flow efficiently. This layer prevents redundant communication and ensures agents don’t work in silos.

Key components:

• Agent Contact Block: Stores metadata on agent capabilities and tool access.
• Task Management Block: Tracks assignments, deadlines, and dependencies.
• Group Info Block: Maintains chat records, enabling context-aware responses.

Unlike traditional MAS, IoA enforces a structured, state-driven approach to multi-agent communication, reducing unnecessary exchanges.

3. The Foundation Layer: Security, Networking, and Infrastructure

For IoA to function at scale, it needs robust infrastructure for agent registration, security, and real-time communication.

Core components:

• Agent Registry Block: A searchable database of all available agents.
• Security Block: Ensures only authenticated agents participate.
• Session Management Block: Handles WebSocket-based communication, ensuring low-latency interactions.

By separating security, networking, and registration, IoA enables third-party AI models to integrate easily, unlike closed MAS ecosystems.

What Sets IoA Apart?

1. Speech Act Theory for Structured Communication

Instead of free-form conversations, IoA adopts Speech Act Theory, a linguistic framework that classifies communication into structured categories:

• Requests: “Find me the latest research on multi-agent systems.”
• Task Assignments: “You handle the literature review.
• Acknowledgments: “Got it, I’ll start working on it.”
• Information Exchanges: “Here’s what I found…”

This ensures clarity in agent interactions, eliminating redundant exchanges.

2. Nested Team Formation for Scalable Task Execution

Unlike flat agent structures, IoA allows for sub-team creation as tasks evolve.

Example:

1. The AI Researcher Agent requests a literature review.

2. The Google Scholar Agent forms a sub-group with a PDF Agent to extract research data.

3. The Academic Writing Agent synthesizes the findings into structured content.

This mirrors real-world collaboration, where tasks are delegated dynamically based on expertise.

3. Hierarchical Task Execution with Feedback Loops

Instead of treating tasks as isolated actions, IoA structures workflows as a hierarchy:

1. Plan at a high level.

2. Break tasks into smaller subtasks.

3. Validate and refine before finalizing outputs.

This ensures more structured execution, reducing unnecessary processing steps.

Challenges and Future Directions

1. Scaling Beyond Task-Specific Teams

IoA excels in structured tasks like research and software engineering, but how does it handle open-ended, ambiguous collaborations? Future research needs to explore adaptive teaming strategies using Reinforcement Learning (RL).

2. Multi-Agent Benchmarking

Most benchmarks (like RocoBench) evaluate individual agent efficiency, not team-based intelligence. IoA needs standardized metrics for collaborative AI evaluation.

3. Real-Time Adaptation in Unstructured Environments

IoA currently follows structured workflows. Future versions should explore decentralized, real-time decision-making under uncertainty.

The Road to Decentralized, Intelligent AI Systems

The Internet of Agents (IoA) presents a fundamental shift in how autonomous AI systems collaborate at scale. Existing approaches treat agents as pre-scripted, isolated components, unable to dynamically organize, communicate efficiently, or adapt to new challenges. IoA challenges that paradigm by introducing:

• Dynamic, on-demand team formation that mirrors real-world collaboration.
• Structured, protocol-driven messaging that eliminates inefficiencies in agent communication.
• Hierarchical task execution that allows workflows to scale without bottlenecks.

Future iterations will need to tackle real-time decision-making, adaptive teaming strategies, and robust benchmarking for AI collaboration.

If we want AI to move beyond isolated task execution into true intelligent collaboration, we need something like IoA. Not a better chatbot. Not another hardcoded pipeline. But an internet of agents — self-organizing, intelligent, and ready to handle complexity at scale.

The question is no longer if AI agents can work together, but how far we can take them.