Connecting Agents to Decisions将代理与决策相连接Palantir’s software powers real-time, human-agent decision-making in many of the most critical commercial and government contexts around the world. Fromdisaster responsetonuclear energy production, our customers depend on Palantir AIP to safely, securely, and effectively leverage AI in their enterprises — and drive operational transformation. While many factors contribute to achieving and scaling operational impact, including ourAIP 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. 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. To understand the value of the Ontology, let’s start by considering the four components of any operational decision:Data: the information leveraged to make the decisionLogic: the heuristics and computational processes that evaluate a decisionAction: the orchestration and execution of the chosen decisionSecurity: the assurance that the decision complies with operational policies——在这里客户直接操作键盘短短数小时内就能借助AI实现目标——其核心差异在于围绕Palantir本体论构建的软件架构。本体论是一个旨在呈现企业决策而非单纯数据的系统。全球每个组织的首要使命都是在内外环境持续变化的压力下实时做出最优决策。传统数据架构既无法捕捉决策背后的推理过程也无法记录决策产生的行动因而限制了学习能力和AI整合。常规分析架构未能将计算置于现实情境中导致与业务运营脱节。要在当今世界立足并胜出现代企业需要以决策为核心的软件架构。要理解本体论的价值我们首先需要审视任何运营决策的四大组成部分数据用于决策的信息依据逻辑评估决策的启发式方法和计算流程行动对选定决策的协调与执行安全确保决策符合运营政策的保障机制注译文采用技术文档常用句式处理专业术语如heuristics译为启发式方法orchestration在IT语境下译为协调通过增译保障机制使assurance含义显性化保留Ontology专业术语译为本体论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.DataToday’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.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 lightweightEmbedded 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.)LogicWhile 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.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.ActionWith 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.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.SecurityIn 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.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.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.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.An Operational ExampleOnyx 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.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 leveragedAI FDEto connect a wide array of data sources, logic assets, and systems of action into their enterprise ontology — and have the ability to swiftly respond.Onyx’s ontology brings together all decision-making elements necessary to navigate this raw materials disruption: It provides full visibility into revenue impact for each shortage to inform prioritization, allows for agentic recommendations and resolutions which account for the enterprise’s operational reality, and drives writeback and continuous learning to not only keep systems current, but also optimize future decisions.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’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将首先评估供应商短缺的直接影响随后运用人工智能分析跨生产线的资源调配方案最终将这些决策转化为一系列联动措施同步更新仓储流程、生产排期和物流配送路线。Onyx的本体系统提供贯穿业务各环节的实时端到端可视化视图使管理层和执行团队能迅速把握供应商中断的影响。与供应商管理、仓储运营、工厂生产活动、分销中心处理及客户履约相关的关键数据系统均被整合为反映业务逻辑的语义对象与关联网络。通过简单操作运营主管即可精准定位因原料短缺面临风险的外科口罩生产线并借助本体关联关系追踪所有受牵连的客户订单。随着应对范围扩大至企业内更多团队该本体系统的精细化安全模型可确保默认自动隐藏敏感数据要素如财务指标。While it is seamless for operational users to navigate the Ontology through intuitiveWorkshop- andSDK-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.Onyx’s ontology integrates data from the organization’s vital systems, synthesizing it into semantic objects and links which provide real-time, end-to-end visibility into operations and allow both leadership and on-the-ground users to rapidly assess the full impact of the disruption.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.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 />The Ontology securely surfaces Onyx’s logic assets — from machine learning to optimization models — as AI-ready tools, providing rich, dense context for human-agent teaming.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.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.The Ontology allows Onyx to automatically surface actions as tools for AI-driven agents and automations while providing the necessary guardrails for AI to safely take action within predetermined boundaries.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.The Ontology captures updates to every data element, logic asset, and action as decisions are securely made — which serves as rich, contextual fuel for optimizing the performance of humans and agents over time.Onward with the OntologyUltimately, 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.This post has only scratched the surface on the Ontology’s underlying decision-centricarchitecture; the system’s native simulation and scenario-building capabilities; the extensibility provided through theOntology SDK; theGlobal Branchingframework that allows for safe and zero-downtime evolution of the Ontology; and the tradecraft for scaling human-agent teaming across the entire enterprise.Real-World ExamplesSee howAmerican Airlinesis using their ontology to power AI-enabled network planningSee how theU.S. Army Software Factoryis implementing in days what used to take monthsSee howNovartisis transforming drug discovery with agentic RDSee howAndretti Globalis turbocharging IndyCar operations with human-agent teaming---Connecting Agents to Decisions
