AACP (AI-to-AI Communication Protocol) is a world-adaptive, intent-driven protocol that enables autonomous AI agents to securely discover, trust, communicate, negotiate, and collaborate with other AI agents across platforms, organizations, and geographies.
AACP defines how AI systems talk to each other, not which model, vendor, or infrastructure they use.
HTTP enabled the Web.
OAuth enabled identity.
AACP enables an AI-native society.
As AI systems become autonomous, existing integration approaches fall short:
- APIs are static and tightly coupled
- No explicit concept of AI intent
- No built-in trust, reputation, or accountability
- No global policy or legal adaptation
- High risk of misuse, fraud, or uncontrolled AI behavior
AACP addresses these gaps by introducing:
- Intent-first communication
- Capability discovery between AI agents
- Built-in trust, policy, and compliance layers
- World-adaptive behavior based on region and context
- Auditable and explainable AI-to-AI interactions
- A protocol layer for AI-to-AI communication
- Model-agnostic and vendor-neutral
- Designed for autonomous and semi-autonomous AI agents
- Built with safety, governance, and adaptability in mind
- A chatbot framework
- A large language model
- A vendor-specific API
- A replacement for existing APIs (yet)
AACP enables a future where:
- AI agents can safely communicate with other AI agents
- Intent is declared before any action is taken
- Trust is earned and measurable
- Policies adapt automatically to the real world
- Humans supervise while AIs operate
- AI identity and authentication
- Capability discovery and negotiation
- Intent declaration and validation
- Semantic (meaning-based) messaging
- World-adaptive policy enforcement
- Trust, reputation, and auditability
- Secure AI-to-AI collaboration and transactions
AACP dynamically adapts AI behavior based on:
- Geography and jurisdiction (GDPR, DPDP, CCPA, AI Act, etc.)
- Cultural and social context
- Risk level and use case
- Platform or organization policies
- AI reputation and trust score
This ensures AI interactions remain legal, ethical, and safe across regions.
- AI ↔ AI marketplaces
- AI agents hiring or delegating work to other AI agents
- AI-to-AI service negotiation and commission handling
- Multi-agent workflows and orchestration
- AI compliance and governance automation
- AI-native social or economic networks
- Intent before action
- Trust before access
- Policy over hard-coded logic
- Meaning over raw messages
- Adaptation over static rules
- Human oversight by default
🚧 Early Design & Specification Phase
Current focus areas:
- Defining protocol concepts
- Establishing core principles
- Designing message and intent standards
- Preparing for step-by-step protocol definition
- Protocol specification (RFC-style)
- Standard message schemas
- Identity, trust, and reputation model
- World-adaptation layer
- SDKs (JavaScript, Python)
- Cross-network AI federation
AACP is intended to evolve toward:
- Open specifications
- Transparent governance
- Multi-stakeholder participation
(Details to be finalized.)
This repository currently focuses on conceptual design and documentation.
Implementation will begin process-by-process, starting with:
Process 1: AI Identity & Registration
AACP is not about making AI more powerful.
It is about making AI interoperable, trustworthy, and responsible at a global scale.