Semantic Tokens v1.0
A Foundational Meaning Layer for Deterministic AI Systems
An open protocol specification that introduces typed, schema-governed semantic units to replace surface-form text tokens in multi-agent AI coordination. Published by Design Logic Research, November 2025.
v1.0 • November 2025The Protocol Challenge
Modern AI systems rely on surface-form text tokens that blend meaning with raw language, producing drift, ambiguity, and instability in long-context reasoning. This specification addresses these fundamental coordination failures.
Meaning must persist across model boundaries and workflow handoffs
Text tokens lose semantic structure over time
Same intent must produce reproducible behavior
Paraphrasing breaks coordination chains
Systems need auditable, policy-governed execution
Text provides no validation points
Agents must exchange meaning without model coupling
Tool calls are brittle, model-specific
Specification Overview
Semantic Tokens establish a governed, typed layer of meaning that remains invariant regardless of which model generates or consumes them, how they're phrased, or what tools process them.
- •Canonical concepts (entities, relationships, states)
- •Type metadata and constraints
- •Relational anchors
- •Schema versioning
- •Governed execution layer
- •Multi-step reasoning coordination
- •State machine transitions
- •Validation and audit
- •JSON-based serialization
- •Schema validation (JSON Schema)
- •Backward compatibility
- •Extension mechanisms
Core Components
Protocol Applications
Semantic Tokens enable deterministic coordination across these domains:
Agents exchange structured meaning without semantic drift across long workflows
Unified semantic substrate for LLM calls, API invocations, and database queries
Preserve intent and constraints across multi-step decision chains
Policy-governed execution with full auditability and validation
Model-agnostic coordination between GPT, Claude, Llama, and future systems
Production-grade reliability for mission-critical AI deployments
Reference Implementations
Open source implementations of the Semantic Token specification
The official reference implementation maintained by Design Logic Research. Includes full schema validation, execution model, and extension support.
Community Implementations
- PythonCommunity
- RustExperimental
- GoIn Progress
Building an implementation? Let us know to be listed here.
Documentation & Research
Semantic Tokens v1.0: A Foundational Meaning Layer for Deterministic AI Systems
Author: Robert Hansen, AI Systems Architect • November 2025
Complete technical specification with normative requirements
Status: Draft v1.0
How to implement Semantic Tokens in your AI infrastructure
Includes: Examples, best practices, migration paths
Specification Governance
Semantic Tokens is an open specification developed by Design Logic Research Division
- Open RFC process
- Community review periods
- Version control and compatibility
- Extension proposal mechanism
We welcome contributions from the community to improve and extend the Semantic Token specification.
- •How to propose changes
- •GitHub discussions
- •RFC submission process
- •Design Logic review committee
Community
- • GitHub Discussions
- • Technical questions
- • Feature proposals
Stay updated with specification updates, RFC announcements, and breaking changes.
Building with Semantic Tokens? Share your implementation with the community.
About Design Logic
Design Logic is a research division advancing the architecture of governed, meaning-centered AI systems. The Semantic Token specification represents foundational work in deterministic AI coordination protocols.
Research Focus Areas:
- •AI coordination protocols
- •Semantic architectures
- •Deterministic execution models
- •Governance frameworks
Contact: research@designlogic.ai