BotMan AI 使用知识图实现遗产现代化
遗产现代化需要了解应用程序的代码、数据和商业规则在重建之前如何组合。
BotMan AI使用AI驱动的迁移管道，在知识图中绘制应用程序的依赖性，业务逻辑和数据模型。 据博特曼创始人兼首席技术官Alejandro D'Andrea(图)说
称说，“问题在于背景。 你必须向agent解释它需要理解的一切[如何]完成特定任务。 如果你不必解释，如果它已经知道你知道的一切呢？这就是现在的关键。
D'Andrea与CUBE研究的John Furrier为CUBE + NYSE Wired:AI Luminaries访谈系列CUBE,SiliconANGLE Media的直播工作室。 他们讨论了知识图如何支持遗留的现代化，并给AI agents提供可重复使用的商业环境。 (* 下文披露。 )
知识图如何支持遗产现代化
根据D ' Andrea的说法，BotMan AI使用知识图来表示应用程序的组件和商业规则如何相互联系。 它的方法重建了应用程序的架构，而不是简单地将旧代码翻译为更新的语言。
他解释说，“它基本上是从一个旧的代码基础——Delphi、Visual Basic、Oracle Forms——迁移过来的，并将同一系统改写成一个新的架构。” “它正在重新确定整个事情。 我们一时吞噬了整个东西，我们完全自主地迁移它。 这才是图表扮演重要角色的地方。
除了代码迁移之外，知识图还可以为AI agents提供可重复使用的上下文。 根据D ' Andrea的说法，在信息进入系统时绘制关系图可以减少后续查询所需的发现工作。
他举了个例子，“在这一点上，当你得到它时，你可以创建与其他文件的所有关系。 你可以说，“这与这个特定项目、这个特定的损失和这个特定的诉讼有关。” 你这样做一次。 然后，任何agent，当你指派任务时，都会从图表中自动知道所有的信息也可能相关。”
根据D'Andrea的说法，将非结构化的信息连接到可识别的实体给agents提供了另一种恢复商业环境的方法。 个人、项目或产品可以链接其他单独的信息和文件。
称先生说，“在处理所有这些无结构的信息方面，对我们起作用的一件事是通过实体将它们联系起来。 “对我们来说，一个实体将是个人、项目或产品。 它会看着你，它会看到所有你已经拥有的联系，你正在研究的项目，我们的相互作用和一切。 然后，它就会找出这些信息应该适合的地方。”
以下是完整的视频采访，SiliconANGLE的部分内容和CUBE对CUBE + NYSE Wired的报导：AI Luminaries访谈系列：
(*披露：CUBE是CUBE+NYSE Wired:AI Luminaries访谈系列的付费媒体合作伙伴。 Neo4j是CUBE活动报道的发起人，其他赞助者对CUBE或SiliconANGLE上的内容都没有编辑控制。 )
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BotMan AI uses knowledge graphs for legacy modernization
Legacy modernization requires understanding how an application’s code, data and business rules fit together before rebuilding it.
BotMan AI uses an AI-powered migration pipeline that maps an application’s dependencies, business logic and data models in a knowledge graph. That context helps agents interpret the system they are rebuilding, according to Alejandro D’Andrea (pictured), founder and chief technology officer of BotMan.
“Intelligence is not the problem anymore,” he said. “The problem is context. You have to explain to the agent everything it needs to understand [how] to do a particular task. What if you didn’t have to explain that? What if it already knew all that you know? That’s the key now.”
D’Andrea spoke with theCUBE Research’s John Furrier for theCUBE + NYSE Wired: AI Luminaries interview series on theCUBE, SiliconANGLE Media’s livestreaming studio. They discussed how knowledge graphs support legacy modernization and give AI agents reusable business context. (* Disclosure below.)
How knowledge graphs support legacy modernization
BotMan AI uses knowledge graphs to represent how an application’s components and business rules relate to one another, according to D’Andrea. Its approach rebuilds the application’s architecture rather than simply translating old code into a newer language.
“It’s basically migrating from an old code base — Delphi, Visual Basic, Oracle Forms — and rewriting that same system into a new architecture,” he explained. “It’s re-architecting the entire thing. We ingest the whole thing at once and we migrate it fully autonomously. That’s where graphs play a big part.”
Beyond code migration, knowledge graphs can provide reusable context for AI agents. Mapping relationships when information enters the system can reduce the discovery work needed for subsequent queries, according to D’Andrea.
“You get, let’s say, a legal document,” he gave as an example. “At that point, when you get it, you can create all the relationships to other documents. You can say, ‘This relates to this particular project and this particular loss and this particular lawsuit.’ And you do that once. Then any agent, when you assign a task, will know automatically from the graph that all that information also might be relevant.”
Connecting unstructured information to recognizable entities gives agents another way to recover business context, according to D’Andrea. A person, project or product can link otherwise separate messages and documents.
“One of the things that worked for us in terms of processing all this unstructured information … is link[ing] them through entities,” he said. “An entity for us will be a person, a project or a product. It will look you up, it will see all the connections that you already have, the projects that you’re working on, the interactions that we had and everything. And then it will figure out where that piece of information … should fit.”
Here’s the complete video interview, part of SiliconANGLE’s and theCUBE’s coverage of theCUBE + NYSE Wired: AI Luminaries interview series:
(* Disclosure: TheCUBE is a paid media partner for theCUBE + NYSE Wired: AI Luminaries interview series. Neither Neo4j, the sponsor of theCUBE’s event coverage, nor other sponsors have editorial control over content on theCUBE or SiliconANGLE.)
Photo: SiliconANGLE
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