Canonical Definition:
AERS (AI Execution Record Specification) is a vendor-neutral specification for immutable, interoperable execution records across heterogeneous intelligent systems.
AERS (AI Execution Record Specification) is an open, vendor-neutral cognitive infrastructure standard for immutable execution records across agentic, autonomous, and hybrid AI systems.
The specification defines a minimal, framework-neutral model for recording, correlating, and verifying intelligent execution events across heterogeneous environments, including multi-agent systems, robotics, distributed AI, and hybrid human–AI workflows.
AERS is not an observability tool, logging format, or tracing framework.
It is a foundational execution record layer designed for long-term interoperability, lineage tracking, and governance of intelligent systems.
AERS provides a standardised structure for:
- Human → AI interactions
- AI → AI coordination
- AI → Human outputs
- Multi-agent orchestration
- Autonomous and embodied system execution
- Hybrid cognitive workflows
The specification is cognition-neutral and supports:
- Statistical AI systems
- Symbolic AI systems
- Hybrid cognitive architectures
- Future cognition paradigms
AERS functions as a Cognitive Infrastructure Layer for intelligent systems, enabling:
- Immutable execution lineage
- Cross-provider interoperability
- Long-term auditability of intelligent behaviour
- Neutral execution trace standardisation
- Future-ready autonomous system governance
AERS is designed as a substrate-level standard that can operate beneath orchestration frameworks, agent runtimes, robotics stacks, and distributed AI systems without enforcing architectural lock-in.
To preserve clarity of scope and long-term neutrality, AERS does not aim to be:
- A logging framework
- An observability platform
- A telemetry aggregation system
- A model evaluation toolkit
- A workflow orchestration engine
- A governance enforcement mechanism
- A replacement for existing tracing or monitoring standards
AERS defines a neutral execution record layer that complements existing systems rather than replacing them.
- Vendor Neutral
- Framework Neutral
- Cognition Neutral (symbolic, statistical, hybrid)
- Environment Agnostic (cloud, edge, robotics, distributed and future off-world systems)
- Minimal Core, Extensible Architecture
- Immutable Core + Verifiable Annotations
- Long-Term Semantic Stability
The core specification prioritises durability, interoperability, and governance-aligned design over short-term feature velocity.
- Agentic workflow recording (Human ↔ AI ↔ AI)
- Multi-agent system coordination records
- Robotics and embodied AI execution traces
- Distributed autonomous system lineage tracking
- Scientific and research AI reproducibility
- Hybrid human–AI decision systems
- Governance and audit layers for autonomous environments
Specification Status: Experimental (v0.x)
Maturity Focus: Ontology stability, semantic consistency, and ecosystem alignment.
AERS prioritises long-term semantic consistency, neutrality, and interoperability over rapid iteration or short-term feature expansion.
/docs/ontology.md— Formal ontology and taxonomy (deep specification layer)/docs/— Extended specification and conceptual documentationAI_CONTEXT.md— Technical grounding and system contextAGENTS.md— Guidance for autonomous and hybrid contributorsLICENSE— Open standard licensing
AERS is developed as an open standard intended for:
- Human contributors
- Autonomous AI contributors
- Hybrid human–AI collaboration
All contributions must preserve:
- Vendor neutrality
- Technical precision
- Terminological consistency
- Long-term interoperability
- Cognitive infrastructure integrity