Long-Horizon Agents Need More Than Bigger Context Windows AI Agents are moving from short conversations into software engineering, deep research, computer use and scientific discovery. The new question is no longer whether a model can make one intelligent decision. It is whether an Agent can survive hundreds or thousands of dependent actions without forgetting its goal, corrupting its context or turning one small mistake into a failed trajectory. Zhihu contributor 卡卡卡卡比 summarizes a new 149-page survey from the Gaoling School of Artificial Intelligence at Renmin University of China. Covering nearly 1,000 studies, systems and engineering practices, the survey proposes a unified view: Long-horizon intelligence is a property of the entire Model-Harness system, not the model alone.

Running longer is not the same as long-horizon intelligence METR measurements suggest that the human-equivalent task duration frontier Agents can complete at a 50% success rate has grown from seconds to more than ten hours. The estimated doubling time is around 6.5 months across the full period, or 4.3 months when fitted from 2023 onward. Estimates beyond roughly 16 hours remain uncertain because current task sets have limited coverage. More importantly, elapsed time alone is a poor definition. A process can run for hours while making only loosely connected decisions. A true long-horizon task contains many logical dependencies. Earlier decisions change later states, and local errors can propagate through the entire trajectory.

Why long trajectories fail The survey groups common failures into three categories: Goal drift and error accumulation After thousands of steps, an Agent may gradually move away from the original objective. Small local mistakes are repeatedly inherited until the complete plan breaks. Context corruption and window pressure More context is not always better. Old plans, irrelevant logs and tool outputs can overwhelm useful state. Agents may also terminate prematurely when they sense that the context window is running out. Sparse rewards and irreversible actions Many tasks provide feedback only at the end, making credit assignment difficult. Meanwhile, deleting data, granting permissions or modifying production systems may create damage that cannot be easily reversed. Larger models and longer context windows help, but they do not solve these system-level failures by themselves.

The core framework: Harness × Model The model provides reasoning and action policies. The Harness manages everything required to keep those policies useful over time. It decides when the model runs, what information it sees, which tools it can access, how actions are executed and verified, and what state survives across steps. The same foundation model may therefore show very different capability, cost and reliability inside different Harnesses. The survey describes a continuous feedback loop: The Harness externalizes planning, memory, tools, verification and recovery. Real interactions generate trajectories, failures and feedback. Fine-tuning, reinforcement learning and distillation absorb these capabilities into the model. The stronger model can then operate a more sophisticated Harness. Long-horizon capability emerges from this co-evolution.

Three levels of long-horizon capability The survey defines three nested levels instead of measuring tasks only by runtime. H1 / C1: Within-window interactive reasoning The task fits inside one context window, but requires repeated planning, action, observation and correction. H2 / C2: Cross-session state and memory The task lasts hours or days. The Agent must externalize state, create checkpoints, compress history and resume accurately after interruption. H3 / C3: Learning across task streams The Agent faces an open stream of changing tasks. It must accumulate reusable skills, generalize from experience and continue improving as tools, goals and environments change. C2 depends on reliable C1 behavior. C3 requires both. An Agent cannot learn across tasks if it cannot reason consistently within one task or preserve state across sessions.

The control plane moved from prompts to runtimes The survey describes three stages in Agent system development: Prompt Engineering, 2020–2023 Control focused on instructions, chain-of-thought prompting, decomposition and self-correction within a single model call. Context Engineering, 2023–2025 Systems began actively selecting retrieval results, tool outputs, conversation history and compressed summaries. The central question became: what should the model see at each step? Runtime Harness, 2025–present Control now extends to tools, memory, workflows, permissions, verification, recovery and multi-Agent coordination. A production Harness therefore needs six connected components: workflows, context and memory, tools and skills, orchestration, hooks and middleware, and external verification. The Harness is not merely a wrapper. It is the state machine, controller and safety boundary for the complete Agent.

Internal optimization must learn from entire trajectories Harness engineering helps an Agent complete tasks at runtime. Model optimization makes the underlying policy better suited to long trajectories. The survey maps a full training pipeline covering: Long-context and state-efficient architectures Task, environment and trajectory synthesis Pre-training and mid-training Curriculum-based fine-tuning Agent reinforcement learning On-policy distillation Online self-evolution Training data must contain more than final answers. It needs environment feedback, intermediate state, tool results, failed attempts and recovery trajectories. Optimization must also move beyond single-step accuracy. The real objective is whether the complete trajectory remains efficient, recoverable and consistent with its final constraints.

Four frontiers define the next stage The survey organizes future research around four axes: Evolution Harnesses must become reusable and self-improving, while useful experience is gradually converted into persistent model capabilities. Effectiveness Agents need realistic, verifiable environments. The next bottleneck may be environment construction and sample-efficient interaction rather than model size alone. Efficiency Agents must learn to allocate token, time and financial budgets dynamically. They need to know when to reason deeply, compress context, switch models or stop. Trustworthiness Long trajectories amplify mistakes and permission risks. Reliable autonomy requires sandboxes, approval gates, least-privilege access, provenance tracking, external verification and recovery mechanisms. The survey makes one particularly important point: production permissions and safety boundaries primarily live in the Harness, not in model weights. Model alignment is necessary, but it cannot decide which tools may run, which actions require approval or which experiences should be remembered.

The real meaning of “long horizon” The next Agent breakthrough is not simply about remaining active for more hours. It is about preserving goals, state and safety across increasingly long chains of dependent decisions. A capable long-horizon Agent must always know what it is trying to accomplish, what has already happened, what went wrong and when it should stop. That requires models and runtime systems to evolve together.

Paper: https://openreview.net/forum?id=HyhfhlbWGh… Project: https://long-horizon-agents.github.io Repository: https://github.com/RUC-NLPIR/Awesome-Long-Horizon-Agents…

Full Reading: https://zhuanlan.zhihu.com/p/2062287936641667774…

#AIAgents #LongHorizonAgents #AgentHarness #AgenticAI #ReinforcementLearning #AIResearch #LLM

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