# Adaptive Auto-Harness: Sustained Self-Improvement for Agentic System Deployment on Open-Ended Task Streams

arXiv: https://arxiv.org/abs/2606.01770 · HTML: https://mlsanity.com/paper/2606.01770/

## Key facts

- Cited by 27 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 13 of its 18 references that are in the map.
- First posted on arXiv 1 Jun 2026.

## Placed in

- LLM agents and tool use / [Software engineering coding agents](https://mlsanity.com/agents/software-engineering-coding-agents/)
- Synthetic data and self-improvement for LLMs / [Self-evolving LLM agents](https://mlsanity.com/synth/self-evolving-llm-agents/)

## Builds on (most cited first, 13 of 13)

- [Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer](https://mlsanity.com/paper/1701.06538/) (2017)
- [SWE-bench: Can Language Models Resolve Real-World GitHub Issues?](https://mlsanity.com/paper/2310.06770/) (2023)
- [Domain-Adversarial Training of Neural Networks](https://mlsanity.com/paper/1505.07818/) (2015)
- [Learning to Prompt for Continual Learning](https://mlsanity.com/paper/2112.08654/) (2021)
- [Dark Experience for General Continual Learning: a Strong, Simple Baseline](https://mlsanity.com/paper/2004.07211/) (2020)
- [Do We Really Need to Access the Source Data? Source Hypothesis Transfer for Unsupervised Domain Adaptation](https://mlsanity.com/paper/2002.08546/) (2020)
- [DualPrompt: Complementary Prompting for Rehearsal-free Continual Learning](https://mlsanity.com/paper/2204.04799/) (2022)
- [Meta-Harness: End-to-End Optimization of Model Harnesses](https://mlsanity.com/paper/2603.28052/) (2026)
- [FutureX: An Advanced Live Benchmark for LLM Agents in Future Prediction](https://mlsanity.com/paper/2508.11987/) (2025)
- [Continual Harness: Online Adaptation for Self-Improving Foundation Agents](https://mlsanity.com/paper/2605.09998/) (2026)
- [Training Language Model Agents to Find Vulnerabilities with CTF-Dojo](https://mlsanity.com/paper/2508.18370/) (2025)
- [PolyBench: Benchmarking LLM Forecasting and Trading Capabilities on Live Prediction Market Data](https://mlsanity.com/paper/2604.14199/) (2026)
- [Reinforcement Learning for Individual Optimal Policy from Heterogeneous Data](https://mlsanity.com/paper/2505.09496/) (2025)

## Built on by (most cited first, 27 of 27)

- [Recursive Harness Self-Improvement](https://mlsanity.com/paper/2607.15524/) (2026)
- [Evo-Harness: Context-to-Harness Skill Compilation for Self-Evolving Agents](https://mlsanity.com/paper/2608.15071/) (2026)
- [JIT-Agent: Scaling Harness Intelligence via Just-in-Time Harness Evolution](https://mlsanity.com/paper/2608.25593/) (2026)
- [RRSI: Regularized Recursive Self-Improvement of Agent Harnesses](https://mlsanity.com/paper/2609.24972/) (2026)
- [AutoDesign: Meta-Harness Optimization for Long-Horizon Agentic Design](https://mlsanity.com/paper/2608.13560/) (2026)
- [Hierarchical Self-Improvement: A Framework for Task-Specific Evolvable Agent Harnesses](https://mlsanity.com/paper/2608.08466/) (2026)
- [Verify Smarter, Evolve Further: Efficient Harness Evolution through Behavior-Aware Verification](https://mlsanity.com/paper/2608.27311/) (2026)
- [Harness Continual Learning: Continual Adaptation Beyond Model Parameters](https://mlsanity.com/paper/2608.19013/) (2026)
- [Graph Engineering in the Era of LLM Agents: From Individual Intelligence to System Intelligence](https://mlsanity.com/paper/2608.21156/) (2026)
- [Belief-Calibrated Optimization: An Explicit World Model for Agentic Optimization](https://mlsanity.com/paper/2609.01861/) (2026)
- [Agents in the Large: Perception-Centered Architecture for Persistent Agents](https://mlsanity.com/paper/2608.30478/) (2026)
- [RobustSGPO: Search-Space Control for Agent Harness Evolution](https://mlsanity.com/paper/2609.09646/) (2026)
- [Harness Evolution as Learning: Approximation, Generalization, and Optimization Limits of Self-Improving Personal Agents](https://mlsanity.com/paper/2609.36892/) (2026)
- [HM-ROUTER: Joint Model and Harness Routing for Agentic Systems](https://mlsanity.com/paper/2609.32213/) (2026)
- [RLHarness: Co-evolving Procedural Skills with Reinforcement Learning for Long-horizon Multimodal Reasoning](https://mlsanity.com/paper/2609.32326/) (2026)
- [PluginRSI: Recursive Improvement of Agent Harnesses with Reusable Plugins](https://mlsanity.com/paper/2609.32423/) (2026)
- [Self-Evolving Harness on Multiple Tasks with the Agent as Its Own Optimizer](https://mlsanity.com/paper/2609.38372/) (2026)
- [Adaptive-GEPA: Make Your Harness Fit Heterogeneous Requests](https://mlsanity.com/paper/2609.38762/) (2026)
- [Learning from Research: Toward Lifelong Agent Harness Evolution](https://mlsanity.com/paper/2609.40169/) (2026)
- [ReLiveGym: Evaluating Long-Lived Agents over Weeks of Replayed Reality](https://mlsanity.com/paper/2610.00710/) (2026)
- [ServeLearnBench: How Well Can Agents Self-Improve from Serving Experience?](https://mlsanity.com/paper/2610.07792/) (2026)
- [Recurrent Harness for Agentic Continual Learning](https://mlsanity.com/paper/2610.12668/) (2026)
- [Harness^2: Recursive Agent Harnessing for an Open World](https://mlsanity.com/paper/2610.14224/) (2026)
- [HarnessNet: Feedback-driven Hierarchical Joint Harness Evolution for Multi-agent Systems](https://mlsanity.com/paper/2610.15221/) (2026)
- [EAU: Environment-Agent-User Co-evolution for Training User-Centric Agents](https://mlsanity.com/paper/2610.16523/) (2026)
- [VideoHarness-SRSI: Structured Recursive Self-Improvement for Video Understanding Harness](https://mlsanity.com/paper/2610.19424/) (2026)
- [TimeRSI: Towards Self-Improved Agentic Deployment for Time-Series Foundation Models](https://mlsanity.com/paper/2610.19725/) (2026)

## Abstract (arXiv)

Auto-harness systems such as A-Evolve, GEPA, and Meta-Harness improve LLM agents by optimizing prompts, skills, tools, memories, and supporting infrastructure from execution feedback, but they are typically evaluated on fixed offline benchmarks. Real deployments instead present open-ended task streams: histories grow without a fixed endpoint, heterogeneous tasks require different harnesses, and problem distributions shift over time. These challenges make a single repeatedly and densely updated harness brittle, causing performance degradation as accuracy peaks early and then declines. This motivates sustained harness construction with task-wise adaptation. We introduce Adaptive Auto-Harness, a framework and system for such streams. The framework decomposes the gap to an oracle harness into evolution loss and adaptation loss. The system addresses these losses with a stateful multi-agent evolver, a harness tree with solve-time routing, and human-steering hooks for cases where history lacks the needed signal. Across prediction-market, security-competition, and event-forecasting streams, Adaptive Auto-Harness outperforms five existing auto-harness baselines and ablations attribute gains to better construction, routing, or targeted human steering. Code is available in hrefhttps://github.com/A-EVO-Lab/a-evolve/tree/release/adaptive-auto-harnessLink.