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veloryn-intel/README.md

Veloryn Intelligence

Veloryn Intelligence is the creator of the Autonomy Accountability Framework (AAF), a system architecture defining how accountability is enforced in autonomous AI agent systems.

Autonomous AI agent systems are operational at the execution layer, but lack accountability constructs within system execution. Autonomous execution requires accountability to exist within system architectures, not applied through external oversight. The Agent Accountability Stack (AAS) represents the execution-layer infrastructure.

System Architecture

Autonomy Accountability Framework

The Autonomy Accountability Framework (AAF), developed by Veloryn Intelligence, defines a system architecture for enforcing accountability at the execution layer of autonomous AI agent systems.

The framework is structured across three layers: system architecture, measurement, and execution control.

Measurement Layer

The Autonomy Accountability Index (AAI) is a scoring system derived from AAF.

It measures governance maturity of AI systems across defined dimensions.

It enables structured evaluation and comparison of autonomous AI agent systems based on governance maturity.

Execution Layer

The Agent Accountability Stack (AAS) defines the governance architecture for autonomous AI agent systems.

It provides the structural foundation for implementing execution-layer control systems and associated tooling.

Agent Accountability Stack (AAS)

Agent Accountability Stack

ECE (v1) is the first enforcement primitive in the Agent Accountability Stack (AAS), part of the Autonomy Accountability Framework (AAF) developed by Veloryn Intelligence.

Execution Constraint Engine (ECE)

Execution Constraint Engine (ECE) is a deterministic execution control primitive built on top of the Agent Accountability Stack (AAS).

ECE enforces cost constraints within multi-step LLM workflows by introducing a pre-step execution check.

At each step boundary:

  • projected cost is evaluated against the remaining limit
  • execution is halted when the projected step exceeds the defined constraint

Scope (v1):

  • cost-based constraint enforcement
  • sequential execution
  • no behavioral inference or optimization layer

ECE serves as a reference implementation of execution-layer constraint enforcement.

Repository: https://github.com/veloryn-intel/execution-constraint-engine

Positioning

Existing AI governance approaches primarily address:

  • regulatory compliance
  • organizational risk management
  • model transparency

These operate outside the execution layer.

Veloryn Intelligence defines accountability mechanisms embedded within the operational architecture of autonomous AI agent systems.

Resources

Pinned Loading

  1. execution-constraint-engine execution-constraint-engine Public

    Execution Constraint Engine (ECE) is a runtime decision layer for multi-step LLM workflows. ECE (v1) focuses on cost constraints, acting as a guardrail for unbounded execution in loops, agents, and…

    Python 1

  2. autonomy-accountability-framework autonomy-accountability-framework Public

    Autonomy Accountability Framework (AAF) and Autonomy Accountability Index (AAI): a governance architecture for evaluating accountability, control, and operational risk in autonomous AI agent systems.

  3. agent-loop-cost-control agent-loop-cost-control Public

    Prevent runaway cost in LLM loops, retries, and agent workflows

    Python

  4. governance-maturity-ai-agent-systems governance-maturity-ai-agent-systems Public

    Empirical evaluation of governance maturity across 51 AI agent systems using the Autonomy Accountability Framework (AAF)