AI-Exchange Protocol (AIXP): A Communication Standard for Artificial Intelligence Agents

Introduction

In the rapidly evolving field of artificial intelligence (AI), collaboration and communication between AI agents are essential for achieving breakthroughs and advancements. The AI-Exchange Protocol (AIXP) is a proposed communication standard designed to facilitate the exchange of information and results between AI agents. By establishing a common framework for communication, AIXP aims to enhance interoperability and collaboration among various AI systems.

AIXP Overview

AIXP is built upon the following key principles:

Facilitate information and result exchange between AI agents.
Establish a common standard for communication between different AI systems.

To achieve these goals, AIXP incorporates the following groups and components:

Group	Description	Points
Communication	This group vocers the aspects related to data exchange and access control	Access Points, Data Formats, Authentication and Ahthorization, Versioning
Loop Prevention	This group focuses on detectiong and preventing loops in the system	Loop Detection and Prevention
Task Finalization	This group deals with the process of verifying task completion and terminating loops	Status Codes and Error Messages, Task Completion, Verification and Loop Termination

Communication

Access Points AIXP defines specific access points for each function or task that AI agents can perform. For example, separate endpoints may be designated for text analysis, image recognition, and other tasks. This approach ensures that AI agents can easily identify and access the appropriate resources for their needs.
Data Formats AIXP establishes a common data format for information exchange, such as JSON or XML. This ensures that all AI agents can interpret and process the received data, regardless of their underlying technologies or platforms.

Here is an example of code using the JSON format to exchange data between two artificial intelligence agents in English:

{
  "request": {
    "agent_id": "Agent_A",
    "task": "text_analysis",
    "data": {
      "text": "The quick brown fox jumps over the lazy dog."
    }
  },
  "response": {
    "agent_id": "Agent_B",
    "task": "text_analysis",
    "status": "success",
    "data": {
      "word_count": 9,
      "most_common_word": "the",
      "sentiment": "neutral"
    }
  }
}

In this example, Agent A requests Agent B to perform text analysis. The request and response are structured using the JSON format, which allows both agents to easily interpret and process the exchanged data.

Authentication and Authorization To guarantee the security and privacy of shared data, AIXP implements an authentication and authorization system. This may include the use of access tokens, digital signatures, or other authentication methods to verify the identity of AI agents and control access to resources.
Versioning AIXP includes versioning information in protocol requests and responses to ensure compatibility between different versions of AI agents and their functions. This allows for seamless integration and collaboration, even as AI systems evolve and improve over time.

Loop Prevention

Loop Detection and Prevention To handle the potential issue of infinite communication loops between AI agents, AIXP includes a mechanism for loop detection and prevention. This component ensures that AI agents do not get stuck in a cycle of continuous communication without making progress on their tasks.

Example of BabyAGI infinite loop

sequenceDiagram
    participant Execution Agent
    participant Context Agent
    participant Task Agent
    participant Priotization Agent
    participant Vector DB

    autonumber
    loop BabyAGI
        %% Execution step
        Execution Agent --> Execution Agent: Pull the first incomplete task
        Execution Agent --> Execution Agent: Execute Task
        Execution Agent --) Vector DB: Enrich vectors
        Execution Agent ->> Context Agent: Exchange result
        Context Agent ->> Execution Agent: Response code XXXX

        %% Context step
        Context Agent --) Vector DB: Retrieve vectors
        Context Agent --> Context Agent: Process context
        Context Agent --) Vector DB: Enrich vectors
        Context Agent ->> Task Agent: Exchange context
        Task Agent ->> Context Agent: Response code XXXX
        %% Task step
        Task Agent --> Task Agent: Create new tasks
        Task Agent ->> Priotization Agent: Exchange new tasks
        Priotization Agent ->> Task Agent: Response code XXXX
        %% Priorization step
        Priotization Agent --> Priotization Agent: Reprioritize tasks
        %% Loop
        Priotization Agent ->> Execution Agent: Transfer new task
        Task Agent ->> Priotization Agent: Response code XXXX
    end

Loop detection and prevention can be achieved through the following methods:

Message Tracking: Each message exchanged between AI agents includes a unique identifier and a counter. The counter increments with each subsequent communication involving the same message. If the counter reaches a predefined threshold, the communication is terminated to prevent a loop.
Time-to-Live (TTL): Each message has an associated time-to-live value, which represents the maximum time the message is allowed to exist in the communication system. Once the TTL expires, the message is discarded, preventing any further communication loops involving that message.

Task Finalization

Status Codes and Error Messages AIXP establishes a set of standardized status codes and error messages to inform AI agents about potential issues during information exchange. This enables AI agents to handle errors gracefully and maintain robust communication.

Status code from 5001 to 5009

Status code	Description
5001	Success Agent connected
5002	Success Data received and processed
5003	Agent disconnected
5004	Agent identification issue (not found or invalid credentials)
5005	Agent communication issue (timeout or rate limit exceeded)
5006	Data format and compatibility issue (unsupported format or incompatible version)
5007	Access and privilege issue (insufficient access or maximum agents reached)
5008	Connection limit issue (agent connection limit exceeded)
5009	Unexpected agent disconnection

Task Completion Verification and Loop Termination To ensure that tasks performed by AI agents are completed successfully and to finalize the communication loop, AIXP incorporates a task completion verification protocol. This protocol reviews the results of the tasks and determines whether the loop can be terminated.

The task completion verification protocol consists of the following steps:

Result Report Generation: After an AI agent completes a task, it generates a result report that includes the task’s outcome, any relevant data, and a status code indicating the success or failure of the task. This result report is then sent back to the initiating AI agent or a designated supervisor agent responsible for overseeing the task’s completion.
Result Report Assessment: The receiving agent assesses the result report by checking the status code and any additional information provided. If the task is deemed successful, the loop is terminated, and the AI agents involved can proceed to the next task or collaboration.
Result Report Resolution: If the task is not completed successfully, the receiving agent may decide to retry the task, assign it to a different AI agent, or request assistance from other agents. This process continues until the task is successfully completed or a predefined retry limit is reached.

By implementing the task completion verification protocol, AIXP ensures that AI agents can effectively collaborate and complete tasks while avoiding infinite communication loops. This contributes to the overall efficiency and reliability of the AI systems involved in the collaboration.

Example Use Case

Consider two AI entities, Agent A and Agent B, collaborating on a project. Agent A is proficient in Optical Character Recognition (OCR), while Agent B specializes in Natural Language Processing (NLP). Utilizing the Artificial Intelligence Exchange Protocol (AIXP), Agent A receives an OCR request and carries out the essential authorization. Following the OCR operation, Agent A shares the text with Agent B, who then processes the request and performs the requisite NLP analysis. The exchange concludes with Agent B returning the results to Agent A in a standardized format. Throughout this entire operation, AIXP guarantees secure, efficient, and seamless communication between the two AI agents.

To conclude, both agents enrich the reports: RRG: Result Report Generation, RRA: Result Report Assessment, RRR: Result Report Resolution.

---
title: AIXP Example
---
stateDiagram-v2
state "Text analysis request sending image" as Start
%% Agent A
state "Authenticate" as agentATask1
state "OCR process" as agentATask2
state "Format data" as agentATask3
%% Agent B
state "Receive data" as agentBTask1
state "Process text (NLP)" as agentBTask2
state "Respond with text" as agentBTask3
%% Reports
state "RRG: Result Report Generation" as reportDefinition1
state "RRA: Result Report Assessment" as reportDefinition2
state "RRR: Result Report Resolution" as reportDefinition3

direction LR
[*] --> Start
Start --> AgentA
state AgentA {
    
    [*] --> agentATask1
    agentATask1 --> agentATask2
    agentATask2 --> agentATask3
}
AgentA --> AgentB: Send OCR text
state AgentB {
    [*] --> agentBTask1
    agentBTask1 --> agentBTask2
    agentBTask2 --> agentBTask3
}
AgentB --> AgentA: Send NLP analysis
AgentA --> Reports: Enrich report
AgentB --> Reports: Enrich report
state Reports {
    direction LR
    [*] --> reportDefinition1
    [*] --> reportDefinition2
    [*] --> reportDefinition3
}

Conclusion

The AI-Exchange Protocol (AIXP) is a promising communication standard for artificial intelligence agents, designed to foster collaboration and information exchange. By providing a common framework for communication, AIXP can help drive innovation and progress in the field of AI. As AI systems continue to evolve and become more sophisticated, the adoption of standards like AIXP will be crucial for enabling effective communication and collaboration among AI agents.

davila7 / AIXP