Harness as a Language: A Minimalist Agent Framework With Maximal Expressivity

arXiv:2609.26891 · cs.AI · Submitted 2026-09-22 · Read on arXiv

cs.AI

Submitted: 2026-09-22

Updated: 2026-09-22

Code: https://github.com/jaz-lang/jaz

Terminology

• Benchmark — 8× in this paper · explained in 2 episodes
• Trace — 8× in this paper · explained in 2 episodes
• Core — 5× in this paper · explained in 2 episodes
• Multi-Agent System — 4× in this paper · explained in 6 episodes
• Abstraction — 3× in this paper · explained in 1 episode
• Agentic System — 3× in this paper · explained in 2 episodes
• Agent Harness — 2× in this paper · explained in 2 episodes
• Agentic Context Engineering — 2× in this paper
• Agentic Workflows — 2× in this paper · explained in 1 episode
• Continual Learning — 2× in this paper · explained in 2 episodes
• Failure Modes — 2× in this paper · explained in 3 episodes
• Generalization — 2× in this paper · explained in 13 episodes
• KV cache — 2× in this paper · explained in 3 episodes
• AI Agents — 1× in this paper · explained in 2 episodes
• Ablation — 1× in this paper · explained in 1 episode
• Adam — 1× in this paper · explained in 2 episodes
• Adaptation — 1× in this paper · explained in 1 episode
• Alignment — 1× in this paper · explained in 1 episode
• Benchmarking — 1× in this paper · explained in 3 episodes
• Decoupling — 1× in this paper · explained in 2 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• Multi-agent Collaborative Framework — 1× in this paper · explained in 2 episodes
• Reinforcement Learning — 1× in this paper · explained in 4 episodes
• Self-Evolving Agents — 1× in this paper · explained in 2 episodes
• Task Decomposition — 1× in this paper · explained in 2 episodes

Sources

• Recursive Language Models
• MemGPT: Towards LLMs as Operating Systems
• Agentic Context Engineering: Evolving Contexts for Self-Improving Language Models
• GEPA: Reflective Prompt Evolution Can Outperform Reinforcement Learning
• Meta-Harness: End-to-End Optimization of Model Harnesses
• Practical Online KV Cache Compaction for LLM Agents: An Empirical Study
• Mem0: Building Production-Ready AI Agents with Scalable Long-Term Memory

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