AGENTIC-SYSTEMSJuly 18, 20261 min read

MemoHarness: Agent Harnesses That Learn from Experience

Researchers introduce MemoHarness, an adaptive harness optimization framework that enables AI agents to learn from their own executions. By analyzing performance and storing insights in a dual-layer experience bank, it dynamically adapts to new tasks, outperforming static configurations.

Computer Science > Artificial Intelligence

Title:MemoHarness: Agent Harnesses That Learn from Experience

View PDF HTML (experimental)Abstract:An agent harness is the external control layer that turns a base LLM into an executable agent by managing context, tools, orchestration, memory, decoding, and output handling. While harness design strongly affects agent behavior, most automatic improvement methods optimize narrower artifacts such as prompts, pipelines, or workflows, and deployed agents usually reuse a single global harness for all cases. We introduce MemoHarness, an adaptive harness optimization framework that learns from its own executions. MemoHarness decomposes the harness into six editable control dimensions, stores per-case diagnoses and distilled global patterns in a dual-layer experience bank, and adapts the learned harness to each test case using retrieved experience without test-time labels, feedback, or additional search. In our evaluation across shell-agent, code-generation, and analytical-reasoning benchmarks, MemoHarness improves over the fixed harnesses we compare against and shows selective transfer to unseen suites and base models. Its additional context can also remain cost-competitive when much of the retrieved experience is cacheable. These results provide evidence that execution experience is a practical substrate for building agent harnesses that are more adaptive than a single static configuration, while leaving broader claims about statistical robustness and component attribution to future work.

Bibliographic and Citation Tools

Code, Data and Media Associated with this Article

Demos

Recommenders and Search Tools

arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs.

Source: arXiv cs.AI Recent

More in this category

agentic-systems

RegNetAgents: A Multi-Agent Framework for Cross-Network Regulatory Driver Identification in Cancer Genomics

Researchers have introduced RegNetAgents, an AI-oriented multi-agent framework designed to identify regulatory driver candidates across heterogeneous gene networks in cancer genomics. By integrating bulk tumor and single-cell data, the system offers a powerful tool for discovering therapeutic targets in breast and colorectal cancers.

agentic-systems

IMEX Interaction-Based Model Explanation

The IMEX (Interaction-Based Model Explanation) framework offers a novel approach to explainable AI by identifying key features and complex, higher-order interactions within black-box predictive models.

agentic-systems

DialogueVPR: Towards Conversational Visual Place Recognition

Researchers have introduced DialogueVPR, a novel paradigm that casts visual place recognition as an interactive, dialogue-driven reasoning process. By coupling a cross-modal retriever with an intelligent questioner called DQ-pilot, this framework effectively resolves ambiguous real-world descriptions and significantly outperforms traditional static retrieval methods.

agentic-systems

Intelligent Three Level Learning Architecture for Autonomous UAV Swarms in Search and Rescue

Researchers have proposed a novel three-level hierarchical learning architecture for autonomous UAV swarms in search and rescue operations, mimicking biological reflexes, skills, and reasoning.

agentic-systems

ToolAnchor: Anchoring Counterfactual Context to Boost Agentic Tool-use Capability

Researchers introduce ToolAnchor, a novel framework that helps LLM agents seamlessly adapt to new tools by injecting counterfactual anchor contexts, effectively overcoming the challenge of behavioral inertia.

agentic-systems

When a Verified World Model Still Loses: Play-Adequacy vs Prediction-Accuracy in LLM-Synthesized Code World Models

A new study reveals that LLM-synthesized Code World Models can achieve near-perfect prediction accuracy yet fail systematically in actual play, exposing a critical 'verified-vs-correct' gap in planning-oriented AI.