The Importance of Agent Harness in 2026

The importance of Agent Harness in 2026《2026 年智能体框架的重要性》

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We are at a turning point in AI. For years, we focused only on the model. We asked how smart/good the model was. We checked leaderboards and benchmarks to see if Model A beats Model B.
我们正处于人工智能的转折点。多年来，我们只关注模型本身。我们询问模型有多智能/好，检查排行榜和基准测试，看看模型 A 是否击败了模型 B。

The difference between top-tier models on static leaderboards is shrinking. But this could be an illusion. The gap between models becomes clear the longer and more complex a task gets. It comes down to durability: How well a model follows instructions while executing hundreds of tool calls over time. A 1% difference on a leaderboard cannot detect the reliability if a model drifts off-track after fifty steps.
顶尖模型在静态排行榜上的差距正在缩小。但这可能是一种错觉。当任务变得更长、更复杂时，模型之间的差距就会变得明显。这取决于持久性：模型在执行数百次工具调用时，如何长时间遵循指令。排行榜上的 1%差距无法检测到模型在 50 步后偏离轨道的可靠性。

We need a new way to show capabilities, performance and improvements. We need systems that proves models can execute multi-day workstreams reliably. One Answer to this are Agent Harnesses.
我们需要一种新的方式来展示能力、性能和改进。我们需要能够证明模型能够可靠地执行多日工作流的系统。其中一个解决方案是代理套件。

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What is an Agent Harness? 什么是代理套件？

An Agent Harness is the infrastructure that wraps around an AI model to manage long-running tasks. It is not the agent itself. It is the software system that governs how the agent operates, ensuring it remains reliable, efficient, and steerable.
代理套件是围绕 AI 模型管理长时间运行任务的基础设施。它不是代理本身。它是控制代理如何运行的软件系统，确保其保持可靠、高效和可控。

It operates at a higher level than agent frameworks. While a framework provides the building blocks for tools or implements the agentic loop. The harness provides prompt presets, opinionated handling for tool calls, lifecycle hooks or ready-to-use capabilities like planning, filesystem access or sub-agent management. It is more than a framework, it comes with batteries included.
它运行在比代理框架更高的层次上。框架提供工具的构建模块或实现代理循环，而 harness 提供提示预设、对工具调用的主观处理、生命周期钩子或即用型功能，如规划、文件系统访问或子代理管理。它不仅仅是一个框架，它自带电池。

Agent Harness Diagram

We can visualize this by comparing it to a computer:
我们可以通过将其与计算机进行比较来理解这一点：

• The Model is the CPU: It provides the raw processing power.
  模型是 CPU：它提供原始的处理能力。
• The Context Window is the RAM: It is the limited, volatile working memory.
  上下文窗口是 RAM：它是有限的、易失的工作内存。
• The Agent Harness is the Operating System: It curates the context, handles the "boot" sequence (prompts, hooks), and provides standard drivers (tool handling).
  代理框架是操作系统：它管理上下文，处理"启动"序列（提示、钩子），并提供标准驱动（工具处理）。
• The Agent is the Application: It is the specific user logic running on top of the OS.
  代理是应用程序：它是运行在操作系统之上的特定用户逻辑。

The Agent harness implements "Context Engineering" strategies like reducing context via compaction, offloading state to storage, or isolating tasks into sub-agents. For developers, this means you can skip building the operating system and focus solely on the application, defining your agent's unique logic.
代理框架实现了"上下文工程"策略，如通过压缩减少上下文、将状态卸载到存储或将任务隔离为子代理。对开发者而言，这意味着可以跳过操作系统构建，专注于应用程序，定义代理的独特逻辑。

Currently, general-purpose harnesses are rare. Claude Code is a prime example of this emerging category, attempting to standardize with the Claude Agent SDK or LangChain DeepAgents. However, one could argue that all coding CLIs are, in a way, specialized agent harnesses designed for specific verticals.
目前，通用框架很少见。Claude Code 是这一新兴类别的典型例子，试图通过 Claude Agent SDK 或 LangChain DeepAgents 进行标准化。然而，有人可能会认为所有编码 CLI 在某种程度上都是为特定垂直领域设计的专用代理框架。

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The Benchmark Problem and the need for Agent Harnesses 基准问题与代理套件的需求

In the past, benchmarks were mostly done on single-turn model outputs. Last year, we started to see a trend to evaluate systems instead of raw models, where the model is one component which could use tools or interacts with the environment, e.g. AIMO, SWE-Bench.
过去，基准测试主要针对单轮模型输出。去年，我们开始看到一种趋势，即评估系统而非原始模型，其中模型是一个可以使用工具或与环境交互的组件，例如 AIMO、SWE-Bench。

These newer benchmarks struggle to measure reliability. They rarely test how a model behaves after its 50th or 100th tool call/turn. This is where the real difficulty lies. A model might be smart enough to solve a hard puzzle in one or two tries, but fail to follow a initial instructions or correctly reasons over intermediate steps after running for an hour. Standard benchmarks struggle to capture the durabilitiy required for long workflows.
这些更新的基准测试难以衡量可靠性。它们很少测试模型在 50 次或 100 次工具调用/回合后的行为。这正是真正的难点所在。一个模型可能足够聪明，能在一次或两次尝试中解决一个难题，但在运行一小时后却无法遵循初始指令或正确推理中间步骤。标准基准测试难以捕捉长工作流程所需的持久性。

As Benchmarks are going to become more complex we need to bridge the gap between benchmark claims and user experience. A Agent Harness can be essential for three critical reasons:
随着基准测试变得越来越复杂，我们需要弥合基准测试声明与用户体验之间的差距。代理套件对于三个关键原因至关重要：

• Validating Real-World Progress: Benchmarks are misaligned with user needs. As new models are released frequently, a harness allows users to easily test and compare how the latest models perform against their use cases and constraints.
  验证实际进展： 基准测试与用户需求不匹配。由于新模型频繁发布，使用测试套件可以让用户轻松测试和比较最新模型在其用例和约束条件下的表现。

• Empowering User Experience: Without a harness, the user's experience might be behind the model's potential. Releasing a harness allows developers to build agents using proven tools and best practices. This ensures that users are interacting with the same system structure.
  赋能用户体验： 没有测试套件，用户的体验可能落后于模型的潜力。发布测试套件允许开发者使用经过验证的工具和最佳实践构建代理。这确保用户与相同的系统结构进行交互。

• Hill Climbing via Real-World Feedback: A shared, stable environment (Harness) creates a feedback loop where researchers can iterate and improve ("hill climb") benchmarks based on actual user adoption.
  通过实际反馈进行爬山式改进： 共享的稳定环境（测试套件）创建了一个反馈循环，研究人员可以根据实际用户采用情况迭代和改进（“爬山式”）基准测试。

The ability to improve a system is proportional to how easily you can verify its output. A Harness turns vague, multi-step agent workflows into structured data that we can log and grade, allowing us to hill-climb effectively.
提升系统的能力与你验证其输出的难易程度成正比。 一个 Harness 将模糊的多步骤代理工作流程转换为结构化数据，使我们能够记录和评分，从而有效地进行爬山（优化）。

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The "Bitter Lesson" of building Agents 构建代理的"苦涩教训"

Rich Sutton wrote an essay called the Bitter Lesson. He argued that general methods that use computation beat hand-coded human knowledge every time. We see this lesson playing out in agent development right now.
理查德·萨顿写了一篇题为《苦涩教训》的文章。他认为，使用计算的一般方法每次都能战胜手工编码的人类知识。我们目前看到这个教训正在代理开发中上演。

• Manus refactored their harness five times in six months to remove rigid assumptions.

• Manus 在六个月内对其代理框架进行了五次重构，以消除僵化的假设。

• LangChain re-architected their "Open Deep Research" agent three times in a single year.

• LangChain 在一年内对其"开放深度研究"代理进行了三次重新架构。

• Vercel removed 80% their agents tool leading to fewer steps, fewer tokens, faster responses

• Vercel 移除了他们 80%的代理工具，导致步骤更少、令牌更少、响应更快

To survive the Bitter Lesson, our infrastructure (Harness) must be lightweight. Every new model release, has a different, optimal way to structure agents. Capabilities that required complex, hand-coded pipelines in 2024 are now handled by a single context-window prompt in 2026.
为了应对“严酷教训”，我们的基础设施（Harness）必须轻量级。每个新模型的发布，都有不同的、最佳的方式来构建代理。在 2024 年需要复杂、手写管道的功能，在 2026 年现在由单个上下文窗口提示处理。

Developers must build harnesses that allow them to rip out the "smart" logic they wrote yesterday. If you over-engineer the control flow, the next model update will break your system.
开发者必须构建 Harness，让他们能够移除他们昨天写的“智能”逻辑。如果你过度设计控制流，下一个模型更新将破坏你的系统。

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What Comes Next? 接下来会发生什么？

We are heading toward a convergence of training and inference environments. We see a new bottleneck being context durability. The Harness will become the primary tool for solving "model drift". Labs will use the harness to detect exactly when a model stops following instructions or reasoning correctly after the 100th step. This data will be fed directly back into training to create models that don't get "tired" during long tasks.
我们正朝着训练和推理环境的融合方向发展。我们看到了一个新的瓶颈——上下文持久性。Harness 将成为解决"模型漂移"的主要工具。实验室将使用 Harness 来检测模型在执行第 100 步后何时停止正确执行指令或推理。这些数据将直接反馈到训练中，以创建在长时间任务中不会"疲劳"的模型。

As builders and developers the focus should shift:
作为构建者和开发者，我们的重点应该转移：

• Start Simple: Do not build massive control flows. Provide robust atomic tools. Let the model make the plan. Implement guardrails, retries and verifications.
  从简单开始： 不要构建庞大的控制流程。提供强大的原子工具。让模型制定计划。实现护栏、重试和验证。

• Build to Delete: Make your architecture modular. New models will replace your logic. You must be ready to rip out code.
  构建以删除为目标： 使你的架构模块化。新的模型将取代你的逻辑。你必须准备好删除代码。

• The Harness is the Dataset: Competitive advantage is no longer the prompt. It is the trajectories your Harness captures. Every time your agent fails to follow an instruction late in a workflow can be ued for training the next iteration.
  Harness 就是数据集： 竞争优势不再是提示。它是你的 Harness 所捕捉的轨迹。你的智能体在流程后期未能遵循指令的每一次失败都可以用于训练下一次迭代。