100-Page Survey on Long-Horizon Agents
青稞AI · wechat · 2026-07-18
Taking three months to compile, the 100-page survey "Towards Long-Horizon Agents: A Survey" systematically outlines the development trajectory and key technologies of long-horizon agents.
The paper frames "long-horizon capabilities" as the co-evolution of Harness Engineering and Model Optimization: explicit harness capabilities will gradually be internalized into model policies, and stronger policies will, in turn, drive more powerful harnesses.
The survey explores six main aspects:
- Foundations: Definition and difficulty grading of long-horizon capabilities
- Evolution: From prompt engineering to context engineering, and then to harness engineering
- Harnesses: Loops and workflows, context and memory, tools/MCP and skills, orchestration, hooks, verification
- Optimization: Architecture, data/environment synthesis, pre-training/mid-training, fine-tuning, reinforcement learning, policy distillation, self-evolution
- Applications: Software engineering, information retrieval, Computer Use, multimodality, general agents
- Frontiers: Evolution, effectiveness, efficiency, and trustworthiness
Repository and homepage links are included for further in-depth reading and tracking.
More from coding & agent
- Treating agents like 50 First Dates: a 3-layer context system so every conversation doesn't start from zero — evielync · 2026-09-11
- SmolVM open-sources persistent computer infrastructure for agents that outlive chat sessions — aniketmaurya · 2026-09-11
- ARRM targets silent economic regressions in AI agents that functional tests miss — Beautiful_Belt_601 · 2026-09-11
- Dev builds browser 3D pizza delivery game with Claude: physics, GPS pathfinding, traffic AI — vinishkapoor · 2026-09-11
- Build X Carousel Posts from One Wide Image: A Splitter Tool Plus YouMind Skill Workflow — sujingshen · 2026-09-11
- "Anyone still coding the old way?" The joke capturing post-AI programming culture — lxfater · 2026-09-11