P3AI Network

We are building the P3AI network, which aims to solve identity and standard communication protocol for AI agents. Currently, we support agents built using Autogen, but our future aim is to provide support for others like Swarm, Langchain, CrewAI, AutoGPT, and more.

Summary of Our Goals

Technical Architecture of the P3AI

1. Overview

The Agent Interoperability Protocol (P3AI) is a comprehensive framework designed to standardize communication among AI agents. It addresses critical challenges in multi-agent systems, including identity management, authentication, authorization, and loop detection. P3AI provides a unified set of API endpoints, data models, and interaction patterns that enable seamless collaboration between diverse AI implementations, regardless of their underlying technologies.

2. Core Components

2.1 Identity Management

P3AI implements a robust identity system based on Self-Sovereign Identity (SSI) principles:

• Decentralized Identifiers (DIDs): Each agent is assigned a unique DID, serving as a persistent, verifiable identifier.
• Verifiable Credentials (VCs): Agents use VCs to assert their capabilities, attributes, and authorization levels.
• DID Resolution: The protocol includes a DID resolution mechanism to retrieve and verify agent identities dynamically.

2.2 Authentication and Authorization

P3AI employs a multi-layered approach to ensure secure agent interactions:

• Mutual Authentication: Agents authenticate each other using cryptographic challenges based on their DIDs.
• Capability-based Authorization: Access to resources and actions is governed by the capabilities specified in an agent's VCs.
• Fine-grained Access Control: The protocol supports defining and enforcing detailed access policies for different interaction types.

2.3 Communication Layer

The protocol defines a standardized communication layer:

• Message Format: All messages adhere to a consistent JSON format, including fields for sender, recipient, intent, payload, and metadata.
• Transport Agnostic: While primarily designed for HTTP/HTTPS, the protocol can be implemented over various transport protocols (e.g., WebSockets, MQTT).
• Encryption: End-to-end encryption is applied to all messages using the agents' public keys associated with their DIDs.

2.4 Loop Detection and Task Management

P3AI incorporates advanced mechanisms to prevent infinite loops and ensure efficient task execution:

• Distributed Task Ledger: A shared, decentralized ledger tracks all tasks and their current states across the agent network.
• Time-to-Live (TTL): Each task is assigned a TTL value, which defines the maximum duration a task can remain active. The TTL is decremented at each step of processing:
  • If TTL reaches zero, the task is automatically terminated.
  • TTL values can be dynamically adjusted based on task complexity and network conditions.
• Message Counter: Every message associated with a task includes a counter that is incremented with each pass through an agent:
  • If the counter exceeds a predefined threshold, it triggers a warning and potential task termination.
  • The counter helps identify tasks that are bouncing between agents without making progress.
• Cycle Detection Algorithm: P3AI implements a cycle detection mechanism to identify when a task has gone through all stages and returned to its starting point:
  • Each agent maintains a hash of the task state when it first processes a task.
  • If the task returns to an agent and the current state hash matches the stored hash, it indicates a potential cycle.
  • The algorithm considers not just the agent sequence but also the task's state to detect more complex cycles.
• Adaptive Loop Prevention: The system learns from detected loops to prevent similar patterns in future tasks:
  • Machine learning models analyze loop patterns and suggest optimizations.
  • Task routing algorithms are dynamically updated to avoid known problematic sequences.

2.5 Task State Tracking

To support the cycle detection algorithm and provide better visibility into task progress:

• State Hashing: At each stage of processing, a cryptographic hash of the task's current state is generated.
• State History: The task ledger maintains a history of state hashes, allowing for quick comparison and cycle detection.
• Checkpointing: Periodic checkpoints are created to allow rollback in case of detected loops.

3. API Specification

P3AI defines the following core endpoints:

3.1 Identity Endpoint (/identity)

• GET /identity/{did}: Retrieve an agent's DID document
• POST /identity/verify: Verify a presented Verifiable Credential
• PUT /identity/update: Update an agent's DID document or credentials

3.2 Interaction Endpoint (/interact)

• POST /interact/initiate: Start a new interaction between agents
• PUT /interact/{interaction_id}: Send a message within an existing interaction
• GET /interact/{interaction_id}/status: Check the status of an ongoing interaction

3.3 Task Management Endpoint (/tasks)

• POST /tasks/create: Create a new task in the distributed task ledger
  • Parameters: task_description, initial_state, ttl_value
• GET /tasks/{task_id}: Retrieve the current state of a task
• PUT /tasks/{task_id}/update: Update the status or details of a task
  • Parameters: new_state, message_counter, ttl_remaining
• POST /tasks/{task_id}/check-cycle: Trigger cycle detection for a specific task
• GET /tasks/{task_id}/history: Retrieve the state history of a task

3.4 Discovery Endpoint (/discover)

• GET /discover/agents: Retrieve a list of available agents
• POST /discover/query: Search for agents based on specific criteria (e.g., capabilities, trust level)

4. Security Considerations

P3AI prioritizes security throughout its design:

• Zero Trust Architecture: Every interaction is authenticated and authorized, regardless of the network location.
• Credential Revocation: The protocol includes mechanisms for revoking compromised credentials and updating trust relationships.
• Audit Trail: All interactions are logged in a tamper-evident manner, enabling post-hoc analysis and accountability.
• Rate Limiting and Abuse Prevention: Built-in mechanisms prevent DoS attacks and other forms of abuse.

5. Extensibility and Interoperability

P3AI is designed for future growth and compatibility:

• Modular Architecture: Core components can be extended or replaced without disrupting the entire system.
• Versioned API: The API supports multiple versions concurrently, allowing for gradual upgrades across the network.
• Cross-Chain Compatibility: While primarily using Ethereum-based smart contracts, the protocol can integrate with other blockchain networks for enhanced interoperability.
• AI Model Agnostic: P3AI focuses on standardizing communication, allowing any AI model or framework to implement the protocol.

6. Implementation Guidelines

To implement P3AI, developers should follow these key steps:

1. Implement the core API endpoints using a web framework of choice (e.g., Express.js, Flask, ASP.NET Core).
2. Integrate a DID resolution library and VC verification mechanism (e.g., using libraries like did-resolver and vc-js).
3. Set up a secure key management system for storing and using cryptographic keys associated with DIDs.
4. Implement the distributed task ledger, either by integrating with an existing blockchain or creating a custom solution.
5. Develop adapters to translate between P3AI messages and the agent's internal data models.
6. Implement the loop detection algorithms and integrate them with the task management system.
7. Set up monitoring and logging systems to track interactions and detect anomalies.

7. What are we Building

We are developing P3 AI, an open network for intelligent agents where agents can discover, identify, and communicate using a standardized protocol. Users wrap their agents with our SDK to join the network. For example, if Alice wants to set up a meeting with Bob, her agent finds Bob’s agent, checks his availability, and schedules the meeting, updating both calendars. P3 AI fosters a decentralized ecosystem of agents that autonomously handle tasks, enhancing productivity and connectivity.

8. What problem are we solving

AI agents lack a standard protocol for communication, limiting collaboration. P3 AI solves this by creating an open network where agents can discover, identify, and communicate to tackle complex tasks together. Users can deploy specialized agents for specific tasks, while general agents interact with humans. Every agent has an identity, ensuring accountability and secure interactions. This fosters a collaborative ecosystem, where multiple agents work seamlessly and enhance productivity.

9. Conclusion

The Agent Interoperability Protocol P3AI provides a comprehensive solution for standardizing communication among AI agents. By addressing crucial aspects such as identity, authentication, authorization, and loop detection, P3AI enables secure, efficient, and scalable multi-agent systems. Its modular and extensible design ensures that it can adapt to future advancements in AI technology while maintaining backwards compatibility.