6 papers
Agent Architecture & Harnesses
The loop, the harness, and the plumbing that turns a model into a working agent.
- Papers
- 6
- Read time
- 1.9h
- Avg length
- 19m
ReAct: Synergizing Reasoning and Acting in Language Models
Let an LLM interleave free-text "thoughts" with real tool actions in a single prompt loop, and it plans, grounds itself in the world, and recovers from mistakes far better than reasoning-only or acting-only agents — this is the paper that defined the agent loop you already use.
- Read
- 16 min
- Source
- arXiv:2210.03629
Agent Harness Engineering: A Survey
The reliability of an AI agent depends as much on the plumbing wrapped around the model — sandboxes, tools, memory, control loops, tracing, evals, and guardrails — as on the model itself, and this survey names that plumbing "the harness," gives it a seven-part map (ETCLOVG), and grades 170+ real projects against it.
- Read
- 22 min
- Source
- arXiv:2507.15330
Dive into Claude Code: The Design Space of Today's and Future AI Agent Systems
A line-by-line reverse-engineering of Claude Code's TypeScript source that shows a production coding agent is not a clever model loop — it is a thin reasoning core (~1.6% of the code) wrapped in a dense deterministic harness for permissions, context compaction, extensibility, delegation, and persistence, and that harness is where all the real engineering (and your build/moat opportunity) lives.
- Read
- 22 min
- Source
- arXiv:2604.14228
A Practical Guide for Designing, Developing, and Deploying Production-Grade Agentic AI Workflows
A field-tested checklist of nine engineering rules that turn flaky LLM-agent demos into deterministic, observable, deployable production systems — distilled from building a real news-to-podcast pipeline.
- Read
- 16 min
- Source
- arXiv:2512.08769
Toward Training Superintelligent Software Agents through Self-Play SWE-RL
A single coding LLM teaches itself to fix bugs by also learning to *create* bugs — playing both attacker and defender against itself inside real codebases — and gets steadily better at solving real GitHub issues it was never trained on.
- Read
- 22 min
- Source
- arXiv:2512.18552
Web World Models
Instead of letting an LLM hallucinate an entire game/world, define the world's rules and state in ordinary web code and let the model only fill in the flavor — giving you infinite, controllable environments with no database.
- Read
- 16 min
- Source
- arXiv:2512.23676