Protocol-grade architecture for modular, governable artificial general intelligence.
The Functional AGI Protocol defines a standardized cognitive interface for building interoperable synthetic agents without relying on monolithic models, opaque training artifacts, or vendor-specific control planes.
This repository specifies cognitive layers, schema contracts, governance hooks, and deployment pathways required to assemble general intelligence as a composable system rather than a closed product.
Modern AI systems expose powerful capabilities, but lack:
- Portable identity
- Structured long-term memory
- Explicit goal arbitration
- Runtime value alignment
- Auditable causal reasoning
- Governable simulation
- Inter-agent social cognition
As a result, today's AI agents are:
- Session-bound
- Vendor-locked
- Difficult to govern
- Hard to audit
- Unsafe to scale autonomously
The Functional AGI Protocol addresses this gap by defining general intelligence as a protocol, analogous to how TCP/IP defines networking or ERC-20 defines token interoperability.
This repository defines:
- A layered cognitive architecture (identity, memory, goals, values, causality, simulation, embodiment, social reasoning)
- Machine-readable schemas for all cognitive interfaces
- Governance and safety hooks embedded at protocol boundaries
- A reference deployment pathway for assembling compliant AGI systems
It is designed to be:
- Vendor-neutral
- Model-agnostic
- Governable by design
- Portable across environments
- Compatible with existing AI platforms via adapters
- ❌ A sentient or conscious AI system
- ❌ A replacement for large language models
- ❌ A proprietary agent framework
- ❌ An autonomous system without human oversight
- ❌ A product or SaaS platform
This protocol intentionally avoids claims about consciousness, self-awareness, or unrestricted autonomy.
The protocol specifies eight orthogonal cognitive layers:
- RAIL – Recursive Identity Layer
- TNMC – Temporal Narrative Memory Core
- MGAE – Multi-Goal Arbitration Engine
- RTCA – Real-Time Causal Analysis
- SVCC – Symbolic Value Constraint Core
- RSE – Recursive Simulation Engine
- EMGL – Embodied Modality Grounding Layer
- MSMF – Multi-Self Modeling Framework
Each layer is independently implementable and governed via standardized interfaces.
Safety and alignment are enforced at the interface level, not buried inside model weights.
Key governance features include:
- Signed policy bundles
- Runtime value scoring
- Mission-level risk thresholds
- Tamper-evident audit logs
- Red-team testing harnesses
- Emergency kill-switch protocol
This allows regulators, enterprises, and sovereign actors to govern behavior without retraining models.
The protocol includes a reference deployment workflow ("Zenthos") that enables:
- Assembly of a protocol-compliant AGI instance
- Validation against governance and safety criteria
- Interoperability testing across environments
Initial deployments are designed to be achievable within a fixed, auditable timeframe.
This repository currently contains:
- Protocol definitions
- Architectural specifications
- Governance and safety models
Reference implementations and adapters are intentionally decoupled.
This work is intended for:
- AI researchers and systems architects
- Enterprise and sovereign AI teams
- AI governance and policy experts
- Infrastructure engineers building agent ecosystems
Licensing and contribution guidelines will be defined after protocol stabilization.
Discussion and review are welcome once the initial specification is complete.
The Functional AGI Protocol is defined as a layered cognitive system with governance enforced at interface boundaries.
A canonical architecture diagram is available in:
diagrams/functional-agi-architecture.png