# Towards an Agent Operating System - Lessons from Classical and Cloud OS

arXiv: https://arxiv.org/abs/2607.25076 · HTML: https://mlsanity.com/paper/2607.25076/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in Software engineering coding agents, one of 16 directions in LLM agents and tool use.
- Builds on 34 of its 61 references that are in the map.
- First posted on arXiv 27 Jul 2026.

## Placed in

- LLM agents and tool use / [Software engineering coding agents](https://mlsanity.com/agents/software-engineering-coding-agents/)

## Builds on (most cited first, 30 of 34)

- [AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation](https://mlsanity.com/paper/2308.08155/) (2023)
- [MemGPT: Towards LLMs as Operating Systems](https://mlsanity.com/paper/2310.08560/) (2023)
- [Large Language Models Cannot Self-Correct Reasoning Yet](https://mlsanity.com/paper/2310.01798/) (2023)
- [Why Do Multi-Agent LLM Systems Fail?](https://mlsanity.com/paper/2503.13657/) (2025)
- [Frontier Models are Capable of In-context Scheming](https://mlsanity.com/paper/2412.04984/) (2024)
- [AgentSpec: Customizable Runtime Enforcement for Safe and Reliable LLM Agents](https://mlsanity.com/paper/2503.18666/) (2025)
- [Memory OS of AI Agent](https://mlsanity.com/paper/2506.06326/) (2025)
- [Where LLM Agents Fail and How They can Learn From Failures](https://mlsanity.com/paper/2509.25370/) (2025)
- [AIOS: LLM Agent Operating System](https://mlsanity.com/paper/2403.16971/) (2024)
- [We Have a Package for You! A Comprehensive Analysis of Package Hallucinations by Code Generating LLMs](https://mlsanity.com/paper/2406.10279/) (2024)
- [SagaLLM: Context Management, Validation, and Transaction Guarantees for Multi-Agent LLM Planning](https://mlsanity.com/paper/2503.11951/) (2025)
- [EverMemOS: A Self-Organizing Memory Operating System for Structured Long-Horizon Reasoning](https://mlsanity.com/paper/2601.02163/) (2026)
- [Taming Various Privilege Escalation in LLM-Based Agent Systems: A Mandatory Access Control Framework](https://mlsanity.com/paper/2601.11893/) (2026)
- [ReliabilityBench: Evaluating LLM Agent Reliability Under Production-Like Stress Conditions](https://mlsanity.com/paper/2601.06112/) (2026)
- [AI Harness Engineering: A Runtime Substrate for Foundation-Model Software Agents](https://mlsanity.com/paper/2605.13357/) (2026)
- [Building Effective AI Coding Agents for the Terminal: Scaffolding, Harness, Context Engineering, and Lessons Learned](https://mlsanity.com/paper/2603.05344/) (2026)
- [Human-Written vs. AI-Generated Code: A Large-Scale Study of Defects, Vulnerabilities, and Complexity](https://mlsanity.com/paper/2508.21634/) (2025)
- [AI Agent Systems: Architectures, Applications, and Evaluation](https://mlsanity.com/paper/2601.01743/) (2026)
- [AgentCgroup: Understanding and Controlling OS Resources of AI Agents](https://mlsanity.com/paper/2602.09345/) (2026)
- [Crab: A Semantics-Aware Checkpoint/Restore Runtime for Agent Sandboxes](https://mlsanity.com/paper/2604.28138/) (2026)
- [AgenTRIM: Tool Risk Mitigation for Agentic AI](https://mlsanity.com/paper/2601.12449/) (2026)
- [InfiAgent: An Infinite-Horizon Framework for General-Purpose Autonomous Agents](https://mlsanity.com/paper/2601.03204/) (2026)
- [Verify Before You Commit: Towards Faithful Reasoning in LLM Agents via Self-Auditing](https://mlsanity.com/paper/2604.08401/) (2026)
- [AgentCompass: Towards Reliable Evaluation of Agentic Workflows in Production](https://mlsanity.com/paper/2509.14647/) (2025)
- [AC4A: Access Control for Agents](https://mlsanity.com/paper/2603.20933/) (2026)
- [Options, Not Clicks: Lattice Refinement for Consent-Driven MCP Authorization](https://mlsanity.com/paper/2605.11360/) (2026)
- [Uncertainty-Aware, Risk-Adaptive Access Control for Agentic Systems using an LLM-Judged TBAC Model](https://mlsanity.com/paper/2510.11414/) (2025)
- [ClawVM: Harness-Managed Virtual Memory for Stateful Tool-Using LLM Agents](https://mlsanity.com/paper/2604.10352/) (2026)
- [AgentOS: From Application Silos to a Natural Language-Driven Data Ecosystem](https://mlsanity.com/paper/2603.08938/) (2026)
- [Deontic Temporal Logic for Formal Verification of AI Ethics](https://mlsanity.com/paper/2501.05765/) (2025)

## Built on by (most cited first, 1 of 1)

- [L3M: A Verifiable Harness and Runtime for Self-Extending and Self-Improving Agents](https://mlsanity.com/paper/2610.19591/) (2026)

## Abstract (arXiv)

Every major wave of platform software follows the same arc: an initial period of experimentation with competing frameworks and ad-hoc implementations, followed by the articulation of a small set of stable abstractions with well-defined semantics, and finally consolidation around those abstractions into a platform that applications can portably target. POSIX did this for classical operating systems; Kubernetes did it for the cloud. Agentic AI systems - autonomous, LLM-driven agents that plan, use tools, maintain memory, and collaborate - are currently in the experimentation phase of the third such wave. dozens of frameworks and protocols have emerged, but no community consensus exists on what the core abstractions are or what guarantees they carry. Without that consensus, agentic applications cannot be written portably, platforms cannot compose reliably, and the field cannot advance beyond prototype deployments. We argue that the path forward is to follow the prior-wave methodology: derive new agentic abstractions by extending classical OS and cloud OS primitives to stochastic, natural-language-mediated execution, specify their semantics precisely, and consolidate around them - just as POSIX and Kubernetes consolidated their respective waves.