RUNE: High-Performance Authorization & Configuration Engine

Abstract

As AI agents become increasingly autonomous, organizations face a critical challenge: how to grant agents the freedom to act effectively while ensuring they operate within safe, well-defined boundaries. Traditional authorization systems fall short—they're either too slow for real-time agent decision-making or lack the expressiveness needed for complex agent scenarios.

RUNE is a principled authorization and configuration system that combines Datalog-based configuration rules with Cedar-style authorization policies. It delivers sub-millisecond authorization decisions at throughput exceeding 5 million operations per second, using lock-free data structures and zero-copy memory management.

RUNE enables organizations to define both what agents can do (authorization) and how agents should behave (configuration) in a single, coherent framework. With Python bindings and a compact single-binary deployment (~10MB), RUNE integrates seamlessly into modern AI agent architectures including LangChain, AutoGPT, and Claude Code.

Key Features

• Dual-Engine Architecture: Novel combination of Datalog and Cedar for unified authorization and configuration
• Lock-Free Performance: Epoch-based memory reclamation enables true parallelism without locks
• Sub-Millisecond Latency: Parallel policy evaluation delivers authorization decisions in <1ms
• High Throughput: Exceeds 5 million operations per second on modern hardware
• Production-Ready: Comprehensive benchmarks, error handling, and deployment tooling
• Easy Integration: Python bindings, single binary (~10MB), zero runtime dependencies

Architecture Overview

RUNE's dual-engine architecture separates two distinct concerns:

• Datalog Engine: Derives configuration facts through logic programming, enabling composable and context-aware behavioral guidelines
• Cedar Engine: Evaluates authorization policies using Amazon's proven Cedar Policy Language for declarative, analyzable permissions

Both engines operate in parallel against a lock-free fact store, with results merged into unified authorization decisions. This design achieves the expressiveness of complex policy systems with the performance characteristics of in-memory data structures.

Use Cases

Code Generation Agents

Control which files agents can read/write, enforce coding standards, apply rate limits on API calls, and provide environment-specific rules (stricter in production vs. development).

Data Access Agents

Grant fine-grained database access, enforce row-level security policies, apply query cost limits, and derive permissions from organizational hierarchies.

Infrastructure Automation

Authorize infrastructure changes, enforce approval workflows, apply resource quotas, and provide context-aware configuration (region-specific rules, compliance requirements).

Getting Started

Explore the full technical details in the Technical Whitepaper, which covers system design, implementation details, performance evaluation, and production deployment strategies.

For AI agent developers, the Agent Integration Guide provides practical examples and best practices for integrating RUNE into your agent architecture.

View the source code and contribute on GitHub.