内容来源：https://blog.n8n.io/we-need-re-learn-what-ai-agent-development-tools-are-in-2026/

内容总结：

2025年AI智能体发展回顾与2026年趋势展望：技术普及化、企业级需求与评估框架革新

2025年，人工智能领域，特别是AI智能体（AI Agent）的发展，呈现出技术快速普及、市场格局演变和评估标准亟待更新的鲜明特征。行业分析师安德鲁·格林在一份行业观察中指出，过去一年的核心变化可归结为三点：关键能力成为“标配”、大厂入局改变竞争生态、以及面向企业的可靠部署成为新焦点。

技术“平民化”：去年需构建的功能，今年已成基础服务
一年前，开发企业级AI智能体还需重点关注检索增强生成（RAG）、记忆、工具调用和评估等基础模块。如今，这些能力已在很大程度上被标准化或直接集成到主流大语言模型服务中。例如，通过上传文档构建知识库、与评估工具集成等功能，已成为对供应商的基本预期。甚至像联网搜索这类过去需要专门编排的功能，现在也已内置于ChatGPT、Claude等通用服务中。这意味着，智能体构建工具的竞争基础已经抬升。

市场格局：巨头入场与开源项目的兴衰
随着谷歌、OpenAI、微软等大型云厂商纷纷推出可视化、低代码的智能体开发平台（如OpenAI Agent Builder、Google ADK），市场竞争加剧。这导致初创企业必须通过更快的创新速度和更深的功能来保持差异化。同时，模型上下文协议（MCP）等曾快速走红的技术方案因安全实践等问题热度消退，凸显出企业级市场对安全与可靠性的高度重视。

评估框架革新：从“集成能力”转向“企业就绪度”
基于上述变化，对AI智能体构建工具的评估标准也需要重大更新。去年的“可编码性”与“可集成性”二维评估模型将进行调整。

• “可集成性”维度将弱化，其部分核心能力（如无代码创建自定义集成）将并入“可编码性”维度。该维度将继续关注智能体的复杂编排能力，如路由、并行处理、多智能体协作等。
• 新的评估重心将转向“企业就绪度”。这是一个综合性概念，旨在评估智能体能否以负责任的方式在企业环境中部署和配置。关键指标将包括：可观测性、数据防泄露、透明性与可验证性、基于代理的过滤、身份认证与权限控制、运行隔离、回滚机制、供应链安全以及策略合规性检测等。这区分了面向个人消费者的工具与能够安全处理客户数据的企业级解决方案。

被低估的确定性逻辑与编码智能体的定位
报告指出，一个被市场低估但至关重要的方向是“确定性逻辑”。在许多企业关键场景（如安全审计）中，用户更需要智能体严格遵循预设流程（例如，必须检查病毒库），而非完全依赖其自由推理，以确保结果的一致性与可靠性。此外，尽管“氛围编程”引发关注，但报告认为，能够编写可靠、可维护应用程序的编码智能体，其目标用户仍然是专业开发者。对于非技术背景的知识工作者，工具本身应承担起运行业务自动化逻辑的基础设施职责。

展望2026：企业级能力定胜负
综上所述，2025年是AI智能体技术广泛普及和共识形成的一年。进入2026年，市场竞争将超越基础功能的堆砌，深入至企业级安全性、可靠性、合规性与复杂流程的稳健编排能力。那些能够在此方向上持续创新，并帮助组织负责任地部署AI智能体的工具平台，将在下一阶段竞争中占据优势。

中文翻译：

本文由技术撰稿人兼行业分析师安德鲁·格林撰写。我们向安德鲁支付稿酬，但他坚持只撰写个人观点。

巨头们涌入市场，OpenClaw 挪用了 MCP 的安全策略，人人都开始随性编程——前提是他们本来就会写代码。

2025 年堪称“智能体之年”，这主要因为行业就智能体的预期行为模式达成了共识，同时我们也发现可以通过创建子智能体来绕过上下文窗口的限制。

当初我们编写企业级 AI 智能体开发工具时，曾重点关注构建智能体的基础模块，例如 RAG、记忆、工具与评估。一年后的今天，这些功能似乎都在一定程度上变得标准化了。我们现在预期大多数供应商都能支持客户将文档用作上下文与事实依据，或是集成 Promptfoo（现已被 OpenAI 收购）进行评估功能。

当然，仍存在一些细分功能具有差异化优势，例如基于语义相似度对 RAG 文档进行重排序。但如今许多智能体工作甚至不再需要 RAG。就连以往需要明确编排的网页搜索等功能，现在也已成为 ChatGPT、Claude 等大多数标准 LLM 服务的原生能力。

MCP 曾如流星般崛起又迅速沉寂。我欣赏 Anthropic 为 MCP 添加身份验证等安全功能的尝试，但 OpenClaw 将这些努力彻底抛弃。考虑到其随意删除数据、暴露全部漏洞的倾向，任何理性的组织都不会将 OpenClaw 纳入考虑范围。

有鉴于此，我们需要大幅更新 AI 智能体构建平台的评估框架。为此，我梳理了一系列问题，希望通过自主探究来勾勒 2026 版报告的模样：

• 哪些功能已标准化或成为基础模型/LLM 服务的原生能力？
• 去年哪些评估维度依然成立？
• 去年哪些维度仍然重要但未受足够重视？
• 当前评估体系应作何调整？
• 供应商过去一年有何动向？
• 编程类智能体处于什么状态？

哪些功能已标准化或成为基础模型/LLM 服务的原生能力？
如今，即便是基础的 LLM 即服务产品也已接近智能体形态。除前述的网页搜索外，还包括：

• Claude 与 ChatGPT 的“项目”功能，支持用户上传文档、代码及文件以创建可多次引用的主题集合。
• Claude 连接器与 ChatGPT 应用，可对接各类应用、文件与服务（多由第三方开发）。
• 原生 Skills.md（本质是增强型提示模板），但仍能替代去年智能体构建平台所需的部分额外工作。
• 值得关注的 Claude Code 与 Codex（虽不属核心范畴但需予以承认）

这意味着上述能力已成为准入门槛，我们预期所有智能体构建平台都应具备。

去年哪些评估维度依然成立？
“可编程性”维度依然重要——它评估产品帮助组织利用大语言模型实现流程自动化的能力。将继续保留的评估要点包括：

• 路由与分支：根据输入内容、意图或需求，将查询导向最合适的专用智能体或流程。
• 并行处理：在任务相互独立时同步运行多个 AI 智能体或流程。
• 协调者-工作者模式：由中心 LLM 动态分解任务、分配给工作者 LLM 并整合结果。
• 顺序智能体：按特定顺序工作的 AI 智能体链，各智能体执行专项任务并将结果传递给下一环节。
• 多智能体协作：能在对话线程中交互，同时保持对彼此响应及整体对话状态的感知。

去年哪些维度仍然重要但未受足够重视？
“确定性逻辑”组件。在希望借助智能体实现流程自动化的人群中（包括我所深耕的企业网络等难以自动化且具有专有性的领域），许多人宁愿反复调整智能体 20 次以获取理想结果，也不愿预先投入精力定义确定性逻辑。

我还观察到，确定性逻辑的重点并非执行功能（如将数据规范化为通用模式），而是确保智能体在执行任务时遵循预设流程。例如，在安全运营中，你会要求 AI 智能体始终在 VirusTotal 中核查 URL 或文件哈希值，而不希望它通过推理决定是否检查——万一它选择不检查呢？

下图展示了 AI 智能体运行 50 次安全审计的案例，统计了所有漏洞被检测到的情况：

（截图说明：使用故意编写的有漏洞应用进行测试，通过 Claude Code 的 /security-review 命令运行 50 次迭代后人工评估。所有运行中的应用字节完全一致，但有时能识别全部漏洞，有时则会遗漏。）

当前评估体系应作何调整？
去年我们采用“可编程性 vs 可集成性”二维评估体系。

我们可能会取消整个“可集成性”维度。虽然预配置的 API 集成组合非常实用，但在 AI 智能体场景中似乎未得到充分利用。我们将精简该维度并融入“可编程性”维度。部分能力仍会保留，例如使用无代码编写自定义集成，或通过通用 HTTP GET/POST/PUT 请求推送/拉取数据。我可能会评估供应商能否借助第三方工具 API 参考文档，使用 LLM 临时编写集成方案。

我们将保留并完善“触发器”评估项。以 OpenClaw 为例，其大部分自主性与智能都源于“心跳”机制——这本质是定时触发器的全新表述，让智能体“记得”每隔几小时检查邮件。

腾出 Y 轴后，当前草案计划评估“企业适用性”，即 LLM 能否以负责任的方式部署与配置。这将区分消费者或个体创业者使用的粗糙个人智能体，与适合处理客户数据等场景的组织级负责任部署。

评估要点将包括：可观测性、数据丢失防护、透明度与可验证性、基于代理的过滤与防火墙、身份验证与授权、智能体身份标识、数据溯源、基于角色的访问控制、紧急停止开关、回滚机制、智能体代码沙箱、代码执行、运行时可靠性与加固、LLM 托管、软件供应链完整性、策略定义、违规活动检测、错误检测与处理。

“可编程性”维度的调整将评估智能体在预定义工作流外的自主行为，例如自发创建新的子智能体执行任务（同时隐含防止上下文偏移问题）。对于前文所述用例存在诸多细节考量：例如主智能体的 skills.md 文件需要被新创建的智能体继承或修改，以确保其具备适当的工具与权限。

供应商过去一年有何动向？
尽管领域发展迅速，但令人欣慰的是大多数供应商仍在市场中深耕，并构建更完善的企业级功能。简要列举部分亮点：

• n8n 完成 B 轮与 C 轮融资，估值达 10 亿美元，GitHub 星标超 18 万。
• Dify 与 Langflow 的 GitHub 星标均突破 10 万，竞争日趋激烈。
• Flowise 被 Workday 收购，期待其如何整合至产品组合。
• Stack AI 获得 SOC2、ISO 27001 等企业认证。
• Workato 推出新标语“用 Workato Enterprise MCP 点亮你的 AI”，预计很快将改为“Workato Enterprise Skills.md”。

多数大型 LLM 提供商也已进入可视化无代码智能体开发领域，包括 Google Opal、OpenAI Agent Builder、Google ADK 和 Microsoft Studio Copilot。

基于过去十年的观察，大型提供商进入由初创企业定义的市场将呈现以下趋势：

• 原生用户群在用例允许时会自然流向这些产品。例如，拥有 OpenAI 订阅且希望使用低代码工具构建 AI 智能体的用户会优先选择 OpenAI Agent Builder。
• 当原生功能无法满足需求时，用户会评估市场上其他产品。
• 初创企业与小型厂商必须通过创新超越大型提供商。相较于功能发布周期较长的大型企业，敏捷的初创团队具有天然优势。即便是发布速度相对较快的头部 AI 基础模型提供商，相比初创企业仍显迟缓。
• 大型厂商入场时，其功能集可能已落后于现有产品。本报告旨在验证该假设，但相信多数读者会认同：在定义智能体逻辑的能力上，OpenAI Agent Builder 不如专为此设计的工具全面。

编程类智能体处于什么状态？
编程类智能体服务于程序员。或许有人认为任何人都能随性编写应用程序，但现实是：任何负责任的非开发人员知识工作者，都不会在组织中编写自定义应用并期望其具备可维护性与可靠性。运行这些自动化逻辑及相关应用的大部分软件基础设施将由工具自身处理。

因此我们将探索在更广泛工作流中使用 LLM 生成代码自动化（例如编写数据处理 Python 脚本）的角度，但不会明确评估使用 LLM 编写应用程序的能力。

参与邀请
欢迎各供应商与用户在报告发布前提出意见与批评。与去年一样，本报告将基于现有技术文档进行书面分析。

诚挚欢迎修正意见与一手经验分享，无论您是否支持 n8n！请通过 LinkedIn 发送消息与我联系。

英文来源：

This article was written by Andrew Green, technical writer and industry analyst. We pay Andrew, but he refuses to write anything else but his own opinion.
The big boys entered the market, OpenClaw appropriated the MCP security strategy, and everyone started vibe coding but only if they already knew how to code.
It really feels like 2025 was the year of agents, mainly because the industry came to a consensus about how we expect an agent to behave. That and because we found we can bypass context window sizes by spawning sub-agents.
When we first wrote the Enterprise AI agent development tools, we focused a lot on the building blocks of writing agents, such as RAG, memory, tools, and evaluations. One year later, all these capabilities appear to have been commoditized to some degree. We now expect most vendors to allow customers to use a document as context and grounding, or to integrate with Promptfoo (now acquired by OpenAI) for evaluations.
Granted, there are some niche things, like reranking RAG documents based on semantic similarity, which are still differentiators. However, a lot of agent work today doesn’t even need RAG. Even things like web search, which you had to orchestrate explicitly, are now natively available with most vanilla LLM services like ChatGPT and Claude.
MCP had a meteoric rise and then fizzled out. I appreciated Anthropic’s attempts at adding security features such as auth around MCP, but then OpenClaw threw all of that out the window. OpenClaw is not in the cards for any sensible organization considering its tendency to delete data and expose ALL the vulnerabilities.
With this in mind, we need a rather drastic update on our framework for evaluating AI agent builders. So, I have a set of questions that I want to answer myself to understand how a 2026 version of the report will look.

• What got commoditized or natively implemented in vanilla models or LLM services?
• What stands from last year?
• What is still relevant from last year but underappreciated?
• What should change in our evaluation today?
• What did the vendors do over the past year?
• What about coding agents?
  What got commoditized or natively implemented in vanilla models or LLM services?
  Today, even basic LLM-as-a-service products come close to being agents. I mentioned web search above, but some of the others include:
• Claude’s and ChatGPT’s Projects, which allow users to upload docs, code, and files to create themed collections that can be referenced multiple times.
• Claude Connectors and ChatGPT apps, which connect to apps, files, and services. These connectors are built by third parties.
• Native Skills.md, which are glorified prompt templates, but they still replace some additional work that would have been required in agent builders last year.
• Honorable mentions to Claude Code and Codex which are not really part of the scope but need to be acknowledged
  This means all these capabilities are now table stakes, and we expect every agent builder to have them.
  What stands from last year?
  The codability axis, which evaluates the capabilities available in a product that allow organizations to automate processes using large language models. Some evaluations points that will appear again will include the likes of:
• Routing and branching, which queries to the most appropriate specialized agent or process based on the content, intent, or requirements of the input.
• Parallelization, run multiple AI agents or processes simultaneously when their tasks are independent of each other
• Orchestrator-workers, in which a central LLM dynamically breaks down tasks, delegates them to worker LLMs, and synthesizes their results.
• Sequential Agents, where AI agents are designed to work in a specific order, where each agent performs its specialized task and passes the results to the next agent in the sequence.
• Multi-Agents, which can interact in a conversation thread while maintaining awareness of each other's responses and the overall conversation state.
  What is still relevant from last year but underappreciated?
  The deterministic component. It looks like those who want to automate processes using agents (including in difficult-to-automate and proprietary fields like enterprise networks where I do a lot of work) prefer nudging an agent 20 times to get a response they want instead of putting some work upfront in defining some deterministic logic.
  I’ve also seen that the deterministic logic part is not that much focused on performing functions (e.g. normalizing data to a common schema) but rather in ensuring that agents go through a set of pre-defined processes when completing a task. For example, you want an AI agent in security operations to always check a URL or file hash in VirusTotal. You don’t want it to reason its way through checking them on the off chance that it might not.
  A good example below is of an AI agent running a security audit 50 times, mapping whether all vulnerabilities were detected.
  In the screenshot below, you see my test with a purposely written vulnerable app that is run across 50 iterations of Claude Code’s /security-review command and then manually assessed. The app is byte-for-byte exactly the same across all runs. Sometimes all the bugs are identified and other times overlooked.
  What should change in our evaluation today?
  Last year, we evaluated codability vs integrability.
  We’ll likely drop the whole integrability axis. Having a portfolio of pre-configured API integrations is great and tremendously useful, but seems to be underutilized in the context of AI agents. We will likely trim it down and roll it over into the codability axis. Some capabilities will surely be kept, such as writing custom integrations using no-code, or pushing/pulling data - via generic HTTP GET/POST/PUT requests. I’ll likely evaluate whether vendors can write integrations ad-hoc using LLMs using third party tools API reference docs.
  We’ll also keep and refine triggers. If you look at OpenClaw, most of its autonomy and intelligence comes from the idea of a heartbeat. It’s a brand new term for a scheduled trigger, so it seems like the agent “remembers” to check your emails every few hours.
  With the freed-up Y axis, the current draft plan is to evaluate enterprisiness, or enterprise-readiness. This is a catch-all term that defines how an LLM can be deployed and configured in a responsible way. This will make the difference between a crude personal agent that consumers or solopreneurs are using, and responsible deployments that are suitable for organizations that actually deal with customer data and such.
  These will include observability, data loss prevention, transparency and verifiability, proxy-based filtering and firewalling, authentication and authorization, agent identity, lineage, role-based access controls, killswitches, rollback, agent code sandboxing, code execution, runtime reliability and hardening, LLM hosting, software supply chain integrity, policy definition, detection of out-of-policy activities error detection and handling.
  Some changes to the codability axis will evaluate how agents can behave autonomously outside the pre-defined workflow, such as spinning up new sub-agents spontaneously to carry out tasks (and implicitly prevent any context drift issues). There is a lot of nuance for use cases like the one above. Consider a skills.md file for a main agent, which would have to be inherited and/or modified for newly spun up agents such that they have the right tools and permissions.
  What did the vendors do over the past year?
  As quickly as the space evolved, it is rather reassuring to see that most vendors are still in the market and are building more enterprise-grade functions. Without going into too much detail, some highlights for vendors previously included include:
• n8n raising series B and C, a total evaluation of $1bn and >180k github stars.
• Dify and Langflow both surpassing 100k Github stars, meaning the competition is fierce
• Flowise getting acquired by Workday. I’m curious to see how they integrated it in the portfolio.
• Stack AI getting some enterprise certs like SOC2 and ISO 27001
• Workato’s new “Light up your AI with Workato Enterprise MCP” tagline. I expect it will soon be replaced by “Workato Enterprise Skills.md”
  Most big LLM providers have also entered the visual no-code agent development space. This includes Google Opal, OpenAI Agent Builder, Google ADK, and Microsoft Studio Copilot.
  Speaking from observations over the past decade, large providers entering a market defined by start-ups will manifest in the following way:
• Native user bases will naturally gravitate to these products where use cases permit. I.e. someone with an OpenAI subscription that wants to build an AI agent using a low-code tool will first use OpenAI Agent Builder.
• In instances where the native features cannot meet the customers requirements, the users will evaluate the rest of the market.
• Start-ups and smaller players will have to out-innovate large providers. This is only the natural progression when you compare a lean organization that ships fast versus a big provider where a new feature has a much longer release time. Even the cool AI foundational model providers that ship relatively fast for their size are rather slow compared to startups.
• The big players entering the market will have already been out-performed in terms of featureset. The intent of the report is to validate this assumption, but I hope most readers will share the intuition that OpenAI Agent Builder is not as comprehensive in its ability to define agentic logic as tools that are purpose-built for this.
  What about coding agents?
  Coding agents are for coders. You may think that anyone can vibe code applications or such, but the reality is that no responsible non-developer knowledge worker working in an organization will write custom applications and have the expectations for them to be maintainable and reliable. Most of the software infrastructure for running these automation logic and associated applications will be handled by the tool itself.
  We will therefore explore the angle of using LLM-generated code-based automation within a wider workflow, such as writing a data processing python script, but will not explicitly evaluate the ability to write applications using LLMs.
  Call to Participate
  I invite any vendors or users to chip in and critique the report prior to publishing. This report, as the one from last year, will be a paper-based analysis based on available technical documentation.
  I therefore welcome corrections and first-hand experiences, regardless if they’re pro n8n or not! Please send me a message on LinkedIn.