Agentao: A Policy-Governed Runtime Harness for Embeddable Tool-Using LLM Agents

arXiv:2608.13574 · cs.AI, cs.MA · Submitted 2026-07-04 · Read on arXiv

cs.AI, cs.MA

Submitted: 2026-07-04

Updated: 2026-08-28

Comments: The code is publicly available at Github. We are conducting testing and analysis of this framework, and will provide experimental results and examples in future versions

Code: https://github.com/jin-bo/agentao

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Trace — 21× in this paper · explained in 2 episodes
• Benchmark — 13× in this paper · explained in 2 episodes
• Core — 12× in this paper · explained in 2 episodes
• Large Language Model — 10× in this paper
• Abstraction — 8× in this paper · explained in 1 episode
• Accountability — 5× in this paper · explained in 2 episodes
• Provenance — 5× in this paper · explained in 3 episodes
• Model Context Protocol — 4× in this paper
• Action Space — 3× in this paper · explained in 1 episode
• Agent Core — 3× in this paper · explained in 2 episodes
• Agent Client Protocol — 2× in this paper
• Alignment — 2× in this paper · explained in 1 episode
• Benchmarking — 2× in this paper · explained in 3 episodes
• Foundation Model — 2× in this paper · explained in 3 episodes
• Foundation Models — 2× in this paper · explained in 7 episodes
• Large Language Model (LLM) — 2× in this paper · explained in 3 episodes
• Model Context Protocol (MCP) — 2× in this paper · explained in 8 episodes
• Multi-Agent System — 2× in this paper · explained in 6 episodes
• Robustness — 2× in this paper · explained in 5 episodes
• Accuracy — 1× in this paper · explained in 1 episode
• Actor — 1× in this paper · explained in 3 episodes
• Agent Architectures — 1× in this paper · explained in 1 episode
• Evaluation Dimensions — 1× in this paper · explained in 3 episodes
• Human-in-the-Loop — 1× in this paper · explained in 3 episodes
• Interpretability — 1× in this paper · explained in 15 episodes
• Multi-Agent Framework — 1× in this paper · explained in 3 episodes
• Reasoning Traces — 1× in this paper · explained in 3 episodes
• Reinforcement Learning — 1× in this paper · explained in 4 episodes
• Task Decomposition — 1× in this paper · explained in 2 episodes
• Task Planning — 1× in this paper · explained in 2 episodes

Sources

• MRKL Systems: A modular, neuro-symbolic architecture that combines large language models, external knowledge sources and discrete reasoning

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