Connecting Agents to Decisions连接智能体与决策
The Palantir OntologyPalantir本体
Palantir’s software powers real-time, human-agent decision-making in many of the most critical commercial and government contexts around the world. From disaster response to nuclear energy production, our customers depend on Palantir AIP to safely, securely, and effectively leverage AI in their enterprises — and drive operational transformation.Palantir的软件驱动着全球许多关键商业和政府领域中的实时人机协同决策。从灾害响应到核能生产，我们的客户依赖Palantir AIP来安全、可靠、有效地在企业中运用人工智能，并推动运营转型。
While many factors contribute to achieving and scaling operational impact, including our AIP AgentCamps — where customers are hands-on-keyboards and achieving outcomes with AI in a matter of hours — the key differentiator is a software architecture which revolves around the Palantir Ontology.虽然实现和扩大运营影响涉及许多因素，包括我们的AIP AgentCamp——客户在那里亲自操作键盘，在几小时内用AI取得成果——但关键的差异化因素是一种围绕Palantir本体构建的软件架构。
The Ontology is a system designed to represent the decisions in an enterprise, not simply the data. The prime directive of every organization in the world is to execute the best possible decisions, often in real-time, while contending with internal and external conditions that are constantly in flux. Traditional data architectures do not capture the reasoning that goes into decision-making or the action that results, and therefore limit learning and the incorporation of AI. Conventional analytics architectures do not contextualize computation within lived reality, and therefore remain disconnected from operations. To navigate and win in today’s world, the modern enterprise needs a decision-centric software architecture.本体是一个旨在表示企业中的决策的系统，而不仅仅是数据。每一个组织的首要指令是执行尽可能好的决策，通常是实时的，同时应对不断变化的内部和外部条件。传统的数据架构并不捕捉决策过程中的推理或由此产生的行动，因此限制了学习和AI的融入。传统的分析架构不能将计算置于现实背景中，因此仍然与运营脱节。要在当今世界中导航并获胜，现代企业需要一种以决策为中心的软件架构。
To understand the value of the Ontology, let’s start by considering the four components of any operational decision:要理解本体的价值，我们先考虑任何运营决策的四个组成部分：
- Data: the information leveraged to make the decision数据：做出决策所利用的信息
- Logic: the heuristics and computational processes that evaluate a decision逻辑：评估决策的启发式和计算过程
- Action: the orchestration and execution of the chosen decision行动：选定决策的编排与执行
- Security: the assurance that the decision complies with operational policies安全：确保决策符合运营策略
The Ontology integrates these four constituent elements of decision-making into a scalable, dynamic, collaborative foundation which reflects the ever-changing conditions and ambitions of the organization as they evolve in real time.本体将这四种决策要素整合到一个可扩展、动态、协作的基础中，该基础反映组织在不断变化的实时条件与目标。
Data数据
Today’s organizations are inundated with unprecedented amounts of data. The volume, variety, and velocity of data sources is not only increasing, but accelerating over time. While plenty of ink has been spilled on the virtues of cleaning and unifying data, in the age of AI the principal problem is relevance. Relevant data of course includes the full range of enterprise data sources — structured data, streaming and edge sources, unstructured repositories, imagery data, and more — but it also includes the data that is generated by end users and agents as decisions are being made. This “decision data” contains the context surrounding a given decision, the different options evaluated, and the downstream implications of the committed choice. Generative AI provides a breakthrough ability to synthesize learnings from the full scale of decision data, and continuously enrich both human- and agent-driven workflows. Naturally, integrating the full range of enterprise data with the fluid landscape of decision data requires a very different architecture than a classical database management solution that is optimized for reporting and analytics.当今组织被前所未有的大量数据淹没。数据源的数量、多样性和速度不仅增加，而且随时间加速。虽然关于清洗和统一数据的好处已有大量讨论，但在AI时代，主要问题是相关性。相关数据当然包括全部企业数据源——结构化数据、流数据和边缘数据源、非结构化存储库、图像数据等——但也包括终端用户和智能体在决策过程中生成的数据。这种“决策数据”包含给定决策的上下文、评估的不同选项以及已执行选择的下游影响。生成式AI提供了从整个决策数据规模中综合学习的能力，并不断丰富人类和智能体驱动的工作流。自然，将全部企业数据与动态的决策数据流集成，需要一种与针对报告和分析优化过的传统数据库管理解决方案非常不同的架构。
The Ontology integrates all modalities of data into a full-scale, full-fidelity semantic representation of the enterprise. The wide range of operational data sources (ERPs, MES, WMS, et al.) can be synchronized and contextualized alongside data streams from IoT and edge systems, the relevant sections of unstructured data repositories, geospatial data stores, and more. The Ontology unites and activates these fragmented pools of data, and surfaces them in the language of the enterprise. Instead of dealing with golden tables that flatten the richness of operations into narrow schemas, the full expanse of the enterprise comes to life in the form of objects, properties, and links which evolve in real-time, and are designed to be embedded directly into decision-making workflows. Critically, the Ontology is designed to safely capture the decision data that is produced by operational users as they carry out daily work (e.g., within supply chains, hospital systems, customer service centers). This includes decisions made at the edge, captured through the lightweight Embedded Ontology. The end-to-end “decision lineage” of when a given decision was made, atop which version of enterprise data, and through which application, is automatically captured and securely accessible to both human developers and agents. This provides the comprehensive foundation that is required to power AI-driven learning at scale, and continuously refine all forms of agentic memory (working memory, episodic memory, semantic memory, procedural memory, et al.)本体将所有模态的数据集成到一个大规模、高保真的企业语义表示中。广泛的运营数据源（ERP、MES、WMS等）可以与来自物联网和边缘系统的数据流、非结构化数据存储库的相关部分、地理空间数据存储等同步并上下文化。本体将这些碎片化的数据池统一并激活，以企业的语言呈现它们。不再需要将运营的丰富性压平到狭窄模式中的黄金表，整个企业在对象、属性和链接的形式下栩栩如生——它们实时演化，并被设计为直接嵌入决策工作流。关键的是，本体被设计为安全地捕获运营用户在日常工作中产生的决策数据（例如，在供应链、医院系统、客户服务中心内）。这包括通过轻量级嵌入式本体在边缘捕获的决策。端到端的“决策谱系”——何时做出给定决策，基于哪个版本的企业数据，通过哪个应用程序——被自动捕获，并且对人类开发者和智能体都是安全可访问的。这提供了支持大规模AI驱动学习和不断精炼各种形式的智能体记忆（工作记忆、情景记忆、语义记忆、程序记忆等）所需的基础。
Logic逻辑
While data is foundational, it is only one dimension of the decision-making process; it must be complemented by the reasoning, or logic, that determines when and how to make a given decision. The logic that underpins a decision can be a simple piece of business logic within a core business system, a forecast model that is maintained using a cloud data science workbench, an optimization model that uses several data sources to produce an operational plan — among myriad possibilities. In real-world contexts, human reasoning is often what orchestrates which logical assets are utilized at different points in a given workflow, and how they are potentially chained together in more complex processes. With the advent of agentic orchestration, it is now critical that AI-driven reasoning can leverage all of these logical assets in the same way that humans have historically. Deterministic functions, algorithms, and conventional statistical processes must be surfaced as operational tools which complement the non-deterministic reasoning of LLMs and multi-modal models. Moreover, as workflows are conducted by humans and agents, the tribal knowledge accumulated can be incorporated into different pieces of logic, and can feed a continuous process of generating new functional encapsulations that are leveraged throughout workflows.虽然数据是基础，但它只是决策过程的一个维度；它必须由决定何时以及如何做出给定决策的推理或逻辑来补充。支撑决策的逻辑可以是核心业务系统中的简单业务逻辑、使用云数据科学工作台维护的预测模型、使用多个数据源生成运营计划的优化模型——可能性很多。在现实世界中，人类的推理通常协调哪些逻辑资产在给定工作流的不同点上被使用，以及如何在更复杂的过程中将它们链接在一起。随着智能体编排的出现，现在至关重要的是，AI驱动的推理能够以人类历史上同样的方式利用所有这些逻辑资产。确定性函数、算法和传统统计过程必须作为运营工具呈现，以补充LLM和多模态模型的非确定性推理。此外，随着人类和智能体执行工作流，积累的隐性知识可以被整合到不同的逻辑片中，并推动一个新的功能封装持续生成的过程，这些封装在整个工作流中被利用。
The Ontology enables the full set of logic assets — the calculations and processes that dictate how decisions are made — to be connected and contextualized for both human and agents. This includes business logic pertaining to customer interactions often found in CRMs and ERPs; the modeling logic that drives conventional machine learning, which is spread across data science environments; and the planning, optimization, and simulation algorithms that are typically intertwined with domain-specific tools. The Ontology’s flexible “logic binding” paradigm provides a consistent interface for constructing workflows that seamlessly incorporate and combine heterogeneous logic assets — which may all live in very different environments (e.g., on-premises data centers, enterprise cloud environments, SaaS environments, the Palantir platform). Ultimately, this means that agent-driven reasoning can be smoothly introduced into decision-making contexts which leverage diverse sets of logic, and which have been traditionally steered exclusively by human users.本体使得全套逻辑资产——决定如何做出决策的计算和过程——能够被连接并为人类和智能体提供上下文。这包括通常在CRM和ERP中发现的客户交互相关业务逻辑；驱动传统机器学习的建模逻辑，分散在数据科学环境中；以及与特定领域工具交织的规划、优化和仿真算法。本体灵活的“逻辑绑定”范式提供了一个一致的接口，用于构建无缝整合和组合异构逻辑资产的工作流——这些资产可能位于非常不同的环境中（例如，本地数据中心、企业云环境、SaaS环境、Palantir平台）。最终，这意味着智能体驱动的推理可以顺利引入到利用多样化逻辑集的决策场景中，而这些场景传统上仅由人类用户操控。
Action行动
With both information (the data) and reasoning (the logic) incorporated into a shared representation, the next piece to model is the execution and orchestration of the decision itself (the action). Closing the action loop as decisions are made in real-time is what distinguishes an operational system from an analytical system. Since Palantir’s inception, the execution of decisions has been as critical a consideration as the synthesis of data, or the incorporation of analytics. This has required the design and implementation of a broad set of functionality which includes how to safely capture decisions which might be happening simultaneously and are potentially in conflict; a collaborative model that segments those who can explore possible decisions, those who can stage decisions for review, and those who can commit those decisions; and an extensive framework for synchronizing decisions to existing databases, edge platforms, and rugged assets.有了信息（数据）和推理（逻辑）都纳入一个共享表示之后，下一个要建模的是决策本身的执行和编排（行动）。在实时决策过程中关闭行动循环，是区分运营系统和分析系统的关键。自Palantir成立以来，决策的执行一直是与数据综合或分析集成同等重要的考量。这需要设计和实现一套广泛的功能，包括如何安全地捕获可能同时发生且可能冲突的决策；一个协作模型，将探索可能决策的人、暂缓决策供审查的人以及提交决策的人区分开；以及一个用于将决策同步到现有数据库、边缘平台和坚固资产的广泛框架。
The Ontology natively models actions within a cohesive, decision-centric model of the enterprise. If the data elements in the Ontology are “the nouns” of the enterprise (the semantic, real-world objects and links), then the actions can be considered “the verbs” (the kinetic, real-world execution). With every Ontology-driven workflow, the nouns and the verbs are brought together into complete sentences through human- and/or AI-driven reasoning, which incorporates various pieces of logic. While uniting data within a semantic model is itself valuable, and while it is imperative to stitch together the logic required to holistically evaluate possible decisions — it is all ultimately of limited value unless the executed decisions are synchronized with operational systems, with the full decision lineage captured within a compounding substrate that can better inform the next decision. The Ontology enables human and agent actions to be safely staged as scenarios, governed with the same granular access controls as data and logic primitives, and securely written back to every enterprise substrate — transactional systems, edge devices, custom applications, et al.本体在统一、以决策为中心的企业模型中原生地建模行动。如果本体中的数据要素是企业的“名词”（语义上的真实世界对象和链接），那么行动可以被视为“动词”（动态的真实世界执行）。在每个本体驱动的工作流中，名词和动词通过人类和/或AI驱动的推理（结合各种逻辑）组合成完整的句子。虽然将数据统一在一个语义模型中本身有价值，并且整合全面评估可能决策所需的逻辑是必要的，但如果执行后的决策不与运营系统同步，且完整的决策谱系没有被捕获到一个能更好指导下一个决策的累积基质中，那么所有这一切最终价值有限。本体使得人类和智能体的行动可以安全地作为场景暂存，受到与数据和逻辑原语相同的细粒度访问控制，并安全地写回每个企业基质——事务系统、边缘设备、自定义应用等。
Security安全
In any operational setting, human-agent interaction requires rigorous security and governance capabilities that stretch far beyond conventional role-driven policies on buckets of data. Palantir AIP provides a security architecture that can blend marking-, purpose-, and role-based policies; dynamic lineage that flows across data, logic, action, and application artifacts; and a full suite of integrated change and release management tools that apply across both human-driven and agentic workflows. Granular policies can be affixed across the Ontology to constrain both agentic and human access to sensitive or context-dependent information. These policies are dynamically computed at runtime for every interaction, and can combine row- and column-level restrictions that have been applied to underlying datasets, attributes of particular user groups (including those that flow via SSO), security markings that propagate across underlying data pipelines, and more.在任何运营环境中，人机交互需要严格的安全和治理能力，远超传统基于角色对数据桶的策略。Palantir AIP提供了一个安全架构，可以融合基于标记、基于目的和基于角色的策略；跨数据、逻辑、行动和应用制品流动的动态谱系；以及一套完整的变更和发布管理工具，适用于人类驱动和智能体工作流。细粒度策略可以附加到本体上，以限制智能体和人类对敏感或上下文相关信息的访问。这些策略在运行时针对每次交互动态计算，并且可以结合应用于底层数据集的行级和列级限制、特定用户组的属性（包括通过SSO流动的）、跨底层数据管道传播的安全标记等。
Tool usage is dynamically enforced through the same security architecture that governs data access and all forms of memory. This ensures, at minimum, that any tool invocations are dependent on access to the underlying objects, properties, and links in the Ontology. Moreover, tools can contain runtime validations that are dependent on granular submission criteria. Every agentic or human action depends on precise authorization grants that explicitly dictate the set of allowable operations, safeguarding against unexpected invocations (e.g., querying data that exists across organizational boundaries, or tools that connect to unspecified external systems) and other forms of privilege escalation. As detailed telemetry is generated by agents, the security and transmission of the logs is a critical last-mile concern. AIP enables administrators to control how logging is accessible across specific projects, workflows, and agents. Data markings and other active security primitives govern log access, in the same manner that they govern access to the underlying data, logic, and action primitives.工具的使用通过相同的安全架构动态执行，该架构治理数据访问和所有形式的记忆。这至少确保任何工具调用都依赖于对本体中底层对象、属性和链接的访问。此外，工具可以包含依赖于细粒度提交标准的运行时验证。每个智能体或人类行动都依赖于精确的授权许可，明确指定允许的操作集，防止意外调用（例如，查询跨组织边界存在的数据，或连接到未指定外部系统的工具）和其他形式的权限提升。当智能体生成详细遥测数据时，日志的安全性和传输是一个关键的最后环节。AIP允许管理员控制日志在特定项目、工作流和智能体之间的可访问性。数据标记和其他主动安全原语以与治理对底层数据、逻辑和行动原语的访问相同的方式治理日志访问。
In short, the Ontology brings together data, logic, action, and security into a decision-centric model of the enterprise, which can be jointly leveraged by both humans and agents. Everything from data integration, to application building, to end user workflows is driven through a battle-tested, modular architecture — enabling human users and agents to query, reason, and act across a shared operational foundation.简而言之，本体将数据、逻辑、行动和安全汇集到一个以决策为中心的企业模型中，该模型可由人类和智能体共同利用。从数据集成到应用构建，再到最终用户工作流，一切都通过经过实战检验的模块化架构驱动——使人类用户和智能体能够在共享的运营基础上查询、推理和行动。
Let’s step through a notional example to unpack how the Ontology is enabling organizations across 50+ sectors to activate human-agent workflows in days.让我们通过一个示例场景来理解本体如何在50多个行业中使组织在几天内激活人机协同工作流。
An Operational Example一个运营示例
Onyx Incorporated, a fictional manufacturer of medical equipment, produces a range of finished goods, from syringes to surgical masks, each of which requires moving a precise set of materials through an associated manufacturing process. A diverse set of teams is managing everything from supplier relations, to warehouse operations, to production of the finished goods, to distribution to end customers; decisions are interdependent, and constantly adapting to changing circumstances. In short, every day brings unique challenges when operating the business.Onyx公司是一家虚构的医疗设备制造商，生产一系列成品，从注射器到外科口罩，每种产品都需要通过一个关联的制造流程移动一套精确的材料。多样化的团队管理着从供应商关系到仓库运营、成品生产再到最终客户分销的一切；决策相互依赖，并不断适应变化的情况。简而言之，运营业务每天都会带来独特的挑战。
In this example, Onyx is faced with an unexpected disruption with one of their major suppliers, who provides the key raw materials needed to produce surgical masks. Given the tight production schedules across Onyx’s manufacturing plants and the escalating demand from customers for surgical masks, this disruption is poised to create serious issues with fulfilling outstanding customer orders. Fortunately, Onyx’s operational teams have leveraged AI FDE to connect a wide array of data sources, logic assets, and systems of action into their enterprise ontology — and have the ability to swiftly respond.在这个例子中，Onyx面临主要供应商之一的意外中断，该供应商提供生产外科口罩所需的关键原材料。鉴于Onyx各制造厂紧张的生产排程和客户对口罩不断增长的需求，这次中断将给履行现有客户订单带来严重问题。幸运的是，Onyx的运营团队利用AI FDE将广泛的数据源、逻辑资产和行动系统连接到了他们的企业本体中——并且能够迅速做出响应。
Onyx will start by assessing the immediate impact of the supplier shortage, and will then employ AI to assess possible reallocation strategies across production lines, before finally translating their decisions into a set of connected actions that will simultaneously update warehouse processes, production schedules, and fulfillment routes.Onyx将首先评估供应商短缺的即时影响，然后利用AI评估跨生产线的可能重新分配策略，最后将决策转化为一组关联行动，同时更新仓库流程、生产排程和履行路线。
Onyx’s ontology provides real-time, end-to-end visibility into the operations happening across each interdependent part of the business — enabling both leadership and on-the-ground teams to quickly understand the supplier disruption. The vital data systems pertaining to supplier management, warehouse operations, production activity within plants, distribution center processing, and customer fulfillment are all synthesized into semantic objects and links, which reflect the language of the business. In a few clicks, an operations leader is able to pinpoint the surgical mask production that is at risk due to the raw material shortage, and through the connections in their ontology, navigate to every outstanding customer order that is now also at risk. The Ontology’s granular security model ensures that more sensitive data elements (e.g., financial metrics) are automatically hidden by default, as the response widens to include more teams across the enterprise.Onyx的本体提供对业务中每个相互依赖部分运营的实时端到端可见性——使领导层和一线团队都能快速理解供应商中断。与供应商管理、仓库运营、工厂生产活动、分销中心处理和客户履行的关键数据系统都被综合成语义对象和链接，反映业务语言。只需几次点击，运营领导者就能查明因原材料短缺而面临风险的外科口罩生产，并通过本体中的链接导航到每个现在也面临风险的未结客户订单。本体细粒度的安全模型确保当响应扩大到包括企业内更多团队时，更敏感的数据元素（例如财务指标）默认自动隐藏。
While it is seamless for operational users to navigate the Ontology through intuitive Workshop- and SDK-driven applications, the inclusion of agentic capabilities is a force multiplier for Onyx Incorporated. Agents, which leverage both open-source and proprietary LLMs, are able to fluidly navigate across supplier information, stock levels, real-time production metrics, shipping manifests, and customer feedback all contained within the organization’s ontology. Critically, all agentic activity is controlled with the same security policies that govern human usage — ensuring that Onyx engineers always have precise control over what the LLMs can query, recommend, and act upon. Each constructed and deployed agent can be considered a new team member, who is gradually granted a wider purview as Onyx team members gain confidence in its performance.虽然运营用户通过直观的Workshop和SDK驱动应用在本体中导航很顺畅，但智能体能力的加入对Onyx公司来说是一个力量倍增器。利用开源和专有LLM的智能体能够流畅地浏览组织的本体中包含的供应商信息、库存水平、实时生产指标、货运清单和客户反馈。关键的是，所有智能体活动都受到与人类使用相同的安全策略控制——确保Onyx工程师始终精确控制LLM可以查询、推荐和行动的内容。每个构建和部署的智能体可以被视为一个新团队成员，随着Onyx团队成员对其性能建立信心，逐步获得更广泛的权限。
Situational awareness is only the tip of the ontological iceberg; Onyx Incorporated needs to rapidly identify solutions to deal with the supplier disruption, and explore the tradeoffs inherent with each possible decision. Fortunately, the diverse set of forecast models, allocation models, production optimizers, and other logic assets have been connected into Onyx’s ontology, alongside the aforementioned data sources. This enables supply chain analysts to quickly run a battery of simulations that detail the consequences of the different possible material substitutions. The connected, real-time nature of the Ontology is key at this stage, since substituting raw materials will potentially have downstream implications for the other products (e.g., syringes, gloves) being produced from the same materials. As the simulations are run, the simulated outputs are staged as ontology scenarios, which safely package the proposed changes into a sandboxed subset of the Ontology — enabling teams to safely explore and analyze the implications of the decision before committing to it.态势感知只是本体的冰山一角；Onyx公司需要快速找到应对供应商中断的解决方案，并探索每个可能决策固有的权衡。幸运的是，多样化的预测模型、分配模型、生产优化器和其他逻辑资产已连接到Onyx的本体中，与前述数据源一起。这使得供应链分析师能够快速运行一系列模拟，详细说明不同可能材料替代方案的后果。本体的连接性和实时性在此阶段至关重要，因为替代原材料可能会对由相同材料生产的其他产品（例如，注射器、手套）产生下游影响。运行模拟时，模拟输出作为本体场景暂存，将提议的更改安全地打包到本体的沙盒子集中——使团队在提交决策前安全地探索和分析决策的影响。
The true game-changer for the Onyx team is that fleets of agents can securely leverage the full range of logic assets, and the same scenarios framework. The Ontology enables agents to go beyond the data-centric limitations of retrieval-augmented generation, and instead interface with the interconnected data, logic, and action primitives in the Ontology through an extensible tools paradigm. This means that as Onyx’s analytics and data science teams are creating new machine learning models in their cloud workbenches, tuning optimization algorithms within enterprise systems, and fine-tuning LLMs using Palantir’s open model building framework, the Ontology securely surfaces all of these logic assets as AI-ready tools. In this case, Onyx has created a tuned agent, “Disruption Bot,” that is able to use a set of Ontology-driven tools to scan across the full range of enterprise data sources, the after-action reports on prior courses of action taken in similar situations, and the potentially applicable material reallocation models. Because of the rich, dense context provided through the Ontology, Disruption Bot is able to surface a novel reallocation plan, which uses a newer model that the supply chain analysts had not yet considered. With the consequences of the plan safely staged in a scenario, the agent’s proposed decision is handed off to a human analyst for final review.对于Onyx团队来说，真正的游戏规则改变者是智能体群可以安全利用全套逻辑资产和相同的场景框架。本体使得智能体能够超越检索增强生成的数据中心限制，而是通过可扩展的工具范式与本体的互连数据、逻辑和行动原语交互。这意味着当Onyx的分析和数据科学团队在其云工作台上创建新的机器学习模型、在企业系统中调整优化算法、并使用Palantir的开放模型构建框架微调LLM时，本体将这些逻辑资产安全地呈现为AI就绪的工具。在这种情况下，Onyx创建了一个经过调整的智能体“中断机器人”，它能够使用一组本体驱动的工具扫描全部企业数据源、过去类似情况下采取的行动后报告以及可能适用的材料重新分配模型。由于本体提供了丰富密集的上下文，中断机器人能够提出一个新颖的重新分配计划，该计划使用了一个供应链分析师尚未考虑的较新模型。该计划的结果安全地暂存在场景中，智能体提出的决策被交给人类分析师进行最终审查。
With a viable plan to address the material shortage identified, Onyx Incorporated needs to rapidly and safely push the decision to the operational systems that run the constituent processes. Given that the enterprise has grown through acquisition, and contains a diverse and delicate mix of critical operational systems, the Onyx IT team is vigilant about which processes can write back to these systems, and under which conditions. Fortunately, the Ontology applies the same rigorous control and validation to actions as it does to data and logic; enabling fine-grain control over who can invoke a given action, test-driven frameworks for publishing changes, the ability to stage and review changes in batch, and detailed logging for every event. In this case, the execution of the material reallocation plan automatically orchestrates a set of writeback routines, each tuned for the receiving system: the warehouse management system receives an API-driven update; the three ERP systems each receive updates via native Ontology-driven connectors, which abide by the safeguards in each system; and the production planning system receives a consolidated flat file, which it ingests asynchronously. As actions are executed, the Onyx IT team can monitor system responses, and always has the ability to audit past activity.确定了应对材料短缺的可行计划后，Onyx公司需要快速且安全地将决策推送到运行各流程的运营系统。鉴于企业通过收购增长，包含多样化且敏感的关键运营系统，Onyx IT团队对哪些流程可以向这些系统写回以及写回条件保持警惕。幸运的是，本体对行动应用了与数据和逻辑相同的严格控制和验证；实现了对谁可以调用给定行动的细粒度控制、用于发布更改的测试驱动框架、批量暂存和审查更改的能力，以及对每个事件的详细日志记录。在这种情况下，材料重新分配计划的执行自动编排一组写回例程，每个例程针对接收系统进行了调整：仓库管理系统接收API驱动的更新；三个ERP系统各自通过原生本体驱动的连接器接收更新，遵守各系统的安全保护；生产计划系统接收一个合并的平面文件，异步摄取。随着行动的执行，Onyx IT团队可以监控系统响应，并始终能够审计过去的活动。
The Ontology provides the guardrails needed for AI to safely take action within permitted boundaries. Alongside data and logic, actions can be automatically surfaced as tools for all types of agents. The scope of an action can be limited to simply reflecting a given change (e.g., an edit to an object, or the creation of a new object) in the Ontology itself; or can write back to single, or multiple systems. In Onyx’s context, they have granted Disruption Bot and the handful of other production AI agents access to a handful of actions. In the default case, these actions (e.g., changing the status of a work order, or pushing a reallocation plan) can only be staged by the AI, and are then handed off to a human for final review. However, with the granular logging and operational instrumentation provided by the Ontology (and the wider Palantir platform), Onyx is able to surgically choose which trusted, well-worn AI processes can automatically close the action loop without human review. As conditions evolve, the latitude given to AI can be expanded or contracted — and instantly reflected across all Ontology-driven workflows.本体提供了AI在允许的边界内安全采取行动所需的安全护栏。与数据和逻辑一起，行动可以作为所有类型智能体的工具自动呈现。行动的范围可以仅限于在本体中反映给定更改（例如，对对象的编辑或创建新对象）；或者可以写回单个或多个系统。在Onyx的上下文中，他们授予中断机器人和其他几个生产AI智能体访问少量行动。默认情况下，这些行动（例如更改工单状态或推送重新分配计划）只能由AI暂存，然后交给人类进行最终审查。然而，通过本体（以及更广泛的Palantir平台）提供的细粒度日志记录和运营仪器，Onyx能够精确选择哪些受信任、经过充分验证的AI流程可以自动关闭行动循环，无需人工审查。随着条件变化，给予AI的权限可以扩大或缩小——并立即反映在所有本体驱动的工作流中。
What comes after the crisis? With data, logic, action, and security all connected into Onyx’s ontology, the organization has the ability to conduct powerful decision-centric learning. The human-agent teaming that produced a specific solution to the material shortage also revealed generalizable workflows, which the organization will want to memorialize and surface in the future. Every data element, logic asset, and action assessed is captured in end-to-end decision lineage — which serves as rich, contextual fuel for optimizing the performance of AI. The aggregate decisions made by thousands of users and agents throughout Ontology can be securely leveraged as training data when fine-tuning models, and can be distilled into targeted principles that are called upon during agent prompting. The tribal knowledge that has been traditionally trapped in the seams of workflows can be illuminated by AI, in order to improve the application of AI.危机之后呢？由于数据、逻辑、行动和安全都连接到Onyx的本体中，组织有能力进行强大的以决策为中心的学习。解决材料短缺问题的人机协同产生了可泛化的工作流，组织希望将其记录下来并在未来使用。每个被评估的数据元素、逻辑资产和行动都被捕获在端到端的决策谱系中——为优化AI性能提供了丰富、上下文的燃料。通过本体中成千上万的用户和智能体做出的聚合决策，可以安全地用作微调模型时的训练数据，并可以提炼为在智能体提示时调用的目标原则。传统上被困在工作流缝隙中的隐性知识可以被AI照亮，以改进AI的应用。
Onward with the Ontology本体向前推进
Ultimately, the Ontology allows each organization to implement and scale human-agent operations, and precisely control how and when agent-driven recommendations, augmentations, and automations can be utilized in frontline contexts. This is uniquely possible because the Ontology is decision-centric, not simply data-centric; it brings together the constituent elements of decision-making — data, logic, action, and security — within a single software system. New data can be rapidly integrated into a full-fidelity semantic representation; new algorithms and business logic can be seamlessly surfaced for both human and AI users; and robust action integration is achieved through real-time connections with the full range of operational systems. Each organization’s ontology is a real-time pulse on the changing conditions, ambitions, and decisions being made across teams — ensuring that AI is always anchored in the reality of the enterprise.最终，本体允许每个组织实施和扩展人机协同运营，并精确控制如何以及在何时可以在一线场景中使用智能体驱动的推荐、增强和自动化。这之所以独特地可能，是因为本体是以决策为中心的，而不仅仅是数据中心；它在单一软件系统中汇集了决策的组成要素——数据、逻辑、行动和安全。新数据可以快速集成到高保真的语义表示中；新算法和业务逻辑可以无缝呈现给人类和AI用户；通过与全套运营系统的实时连接实现强大的行动集成。每个组织的本体都是对跨团队不断变化的条件、目标和决策的实时脉搏——确保AI始终扎根于企业的现实。
This post has only scratched the surface on the Ontology’s underlying decision-centric architecture; the system’s native simulation and scenario-building capabilities; the extensibility provided through the Ontology SDK; the Global Branching framework that allows for safe and zero-downtime evolution of the Ontology; and the tradecraft for scaling human-agent teaming across the entire enterprise.这篇文章只是触及了本体底层以决策为中心的架构的皮毛；系统的原生仿真和场景构建能力；通过本体SDK提供的可扩展性；允许安全零停机演化的全局分支框架；以及在整个企业范围内扩展人机协同的实践。
Real-World Examples真实世界示例
- See how American Airlines is using their ontology to power AI-enabled network planning了解American Airlines如何利用其本体推动AI赋能的网络规划
- See how the U.S. Army Software Factory is implementing in days what used to take months了解美国陆军软件工厂如何在几天内实现以往需要数月才能完成的工作
- See how Novartis is transforming drug discovery with agentic R&D了解Novartis如何通过智能体研发改变药物发现
- See how Andretti Global is turbocharging IndyCar operations with human-agent teaming了解Andretti Global如何通过人机协同提升IndyCar运营
