展示HN：Agent Composer – 用于火箭科学（及其他复杂事务）的AI代理
嗨，HN，我是来自Contextual AI的Jay（<a href="https://contextual.ai" rel="nofollow">https://contextual.ai</a>）。
我们正在构建一个专注于技术行业（如半导体、航空航天、制造等）的AI代理平台。Agent Composer是我们强大的新视觉构建器和运行时，用于创建能够对技术文档、日志和规格进行推理的代理。
我们解决的问题是：通用AI在复杂技术任务上表现不佳。这并不是因为模型没有能力，而是因为它们无法获取正确的上下文（数据表、测试日志、流程规格、机构知识）。
Agent Composer的功能包括：
- 三种创建代理的方式：预构建模板、自然语言描述或空白画布
- 视觉拖放构建器，提供无代码体验，同时为开发者提供YAML配置
- 混合工作流：将确定性步骤（合规检查、验证）与动态推理（根本原因分析、研究）结合
- 基于您的数据，提供完整的归属
我们在构建过程中学到的经验：
- 解析比人们想象的更为重要。包含表格、图形和交叉引用的技术PDF会破坏大多数现成的解析器。我们自己构建了一个。
- 检索精度至关重要。基本的向量搜索可以解决70%的问题；最后30%需要混合检索、重新排序和查询重构。那最后的30%是“精彩演示”和“真正有用”之间的区别。
- 企业需要控制。纯自主代理让合规团队感到害怕。在一个工作流中混合确定性和动态步骤的能力是对客户反馈的直接回应。
以下是一些供您探索的链接：
- 产品快速入门指南：<a href="https://docs.contextual.ai/quickstarts/agent-composer" rel="nofollow">https://docs.contextual.ai/quickstarts/agent-composer</a>
- 我们构建的有趣火箭科学演示：<a href="https://demo.contextual.ai/" rel="nofollow">https://demo.contextual.ai/</a>
- 博客：<a href="https://contextual.ai/blog/introducing-agent-composer" rel="nofollow">https://contextual.ai/blog/introducing-agent-composer</a>
- 免费账户注册链接：<a href="https://app.contextual.ai/?signup=1" rel="nofollow">https://app.contextual.ai/?signup=1</a>
欢迎深入讨论架构、检索策略或经验教训。您有什么问题或反馈吗？
查看原文
Hi HN, Jay from Contextual AI (<a href="https://contextual.ai/" rel="nofollow">https://contextual.ai/</a>) here.<p>We've been building a platform for AI agents focused on technical industries—semiconductors, aerospace, manufacturing, etc. Agent Composer is our powerful new visual builder and runtime for creating agents that reason over technical documentation, logs, and specs.<p>The problem we solved: General-purpose AI fails on complex technical tasks. Not because the models aren't capable, but because they don't have access to the right context (datasheets, test logs, process specs, institutional knowledge).<p>What Agent Composer does:<p>- Three ways to create agents: pre-built templates, natural language description, or blank canvas<p>- Visual drag-and-drop builder for a no-code experience and YAML configs available for developers<p>- Hybrid workflows: combine deterministic steps (compliance checks, validation) with dynamic reasoning (root cause analysis, research)<p>- Grounded in your data with full attribution<p>What we learned building this:<p>Parsing matters more than people think. Technical PDFs with tables, figures, and cross-references break most off-the-shelf parsers. We built our own.<p>Retrieval precision is everything. Basic vector search gets you 70% of the way; the last 30% requires hybrid retrieval, reranking, and query reformulation. That last 30% is the difference between "neat demo" and "actually useful."<p>Enterprises need control. Pure autonomous agents scare compliance teams. The ability to mix deterministic and dynamic steps in one workflow was a direct response to customer feedback.<p>Here are some links for you to explore:<p>- Product quick-start guide: <a href="https://docs.contextual.ai/quickstarts/agent-composer" rel="nofollow">https://docs.contextual.ai/quickstarts/agent-composer</a><p>- Fun rocket science demo we built: <a href="https://demo.contextual.ai/" rel="nofollow">https://demo.contextual.ai/</a><p>- Blog: <a href="https://contextual.ai/blog/introducing-agent-composer" rel="nofollow">https://contextual.ai/blog/introducing-agent-composer</a><p>- Free account signup link: <a href="https://app.contextual.ai/?signup=1" rel="nofollow">https://app.contextual.ai/?signup=1</a><p>Happy to go deep on architecture, retrieval strategies, or lessons learned. What questions or feedback do you have?