# autonomous AI agents

The open-source project PentAGI hands penetration testing tasks to a fully autonomous multi-agent system whose agents collaborate through a graph structure and can be self-hosted. Previously such complex attack workflows relied mainly on manual step-by-step operation or a single model, while this project ships a complete system with a Go backend, React frontend and GraphQL interface, plus Docker Compose deployment. It is released under the MIT license, and the repository has gathered about 29.3k stars and 4.4k forks.

The open-source project PentAGI hands penetration testing tasks to a fully autonomous multi-agent system whose agents collaborate through a graph structure and can be self-hosted. Previously such complex attack workflows relied mainly on manual step-by-step operation or a single model, while this project ships a complete system with a Go backend, React frontend and GraphQL interface, plus Docker Compose deployment. It is released under the MIT license, and the repository has gathered about 29.3k stars and 4.4k forks.

This work demonstrates on a live O-RAN testbed that two autonomous agents with individually correct objectives—one protecting a latency SLA and one maximizing utilization for energy efficiency—jointly drive recurring opposing excursions of the shared resource partition, and presents and proves a lightweight arbitration layer, AURA, whose three admission checks (feasibility invariants, per-variable dwell time, deadband) reduce shared-state excursion amplitude from 8.4 to 0.4 PRB and cross-slice throughput starvation from 40–55% to 0.3%, while leaving the protected slice's own latency compliance unchanged.

This work demonstrates on a live O-RAN testbed that two autonomous agents with individually correct objectives—one protecting a latency SLA and one maximizing utilization for energy efficiency—jointly drive recurring opposing excursions of the shared resource partition, and presents and proves a lightweight arbitration layer, AURA, whose three admission checks (feasibility invariants, per-variable dwell time, deadband) reduce shared-state excursion amplitude from 8.4 to 0.4 PRB and cross-slice throughput starvation from 40–55% to 0.3%, while leaving the protected slice's own latency compliance unchanged.