The development of intelligent agents for oil companies in exploration and development may appear to be an issue of artificial intelligence technology application on the surface. In essence, however, it is a comprehensive reconstruction of business rules, data resources, knowledge systems, technology platforms, governance mechanisms, and organizational capabilities. The stronger the capabilities of an intelligent agent, the higher the requirements for its foundational conditions.
For oil companies, whether they are building Q&A agents, assistant-type agents, scenario-execution agents, role-based proxy agents, or business-collaboration agents, all such agents rely on six common foundations: business foundation, data foundation, knowledge foundation, technology foundation, governance foundation, and organizational foundation. Among them, the business foundation determines whether the agent understands what it should do; the data and knowledge foundations determine whether the agent understands what evidence and basis it should rely on; the technology foundation determines whether the agent can operate and execute tasks; the governance foundation determines whether the agent is controllable and auditable; and the organizational foundation determines whether the agent can be continuously iterated and truly implemented.
On this basis, intelligent agents at different levels also require their own specific construction conditions. L1 places greater emphasis on the availability of data and knowledge; L2 focuses more on content generation and contextual understanding; L3 emphasizes system connectivity and closed-loop processes; L4 requires role modeling and continuous operation; and L5 places greater emphasis on multi-agent collaboration and enterprise-level governance. Only by establishing both the common foundational conditions and the level-specific conditions can oil companies move intelligent agent development from demonstration-oriented applications toward a stable, replicable, and scalable business intelligence system.
Keywords: oil companies; exploration and development; intelligent agents; foundational conditions; role-based proxy agents; business collaboration.
At present, the development of intelligent agents for oil companies in exploration and development is gradually shifting from the question of whether they can be built to whether they can be built in a stable and sustainable manner. This shift means that the focus of intelligent agent development should not remain solely on model capabilities themselves, but must move toward more solid underlying conditions. An intelligent agent is not a tool that exists independently of the enterprise operating environment; rather, it is a digital capability unit embedded in business processes, connected to enterprise data, governed by organizational rules, and oriented toward role-based objectives. Without clear business definitions, data support, knowledge accumulation, technical infrastructure, and governance constraints, even the most advanced models will struggle to create sustained value within an enterprise.
This is particularly true in the exploration and development domain of oil companies. Long business chains, highly specialized divisions of labor, strong risk constraints, and complex process collaboration mean that intelligent agent development cannot be built on a single technological breakthrough alone. Instead, it must be supported by systematic foundational work. The higher the maturity level of an intelligent agent, the greater the requirements become: from Q&A functions to role-based proxy capabilities, and further toward business collaboration, intelligent agents demand increasingly strong support in business standardization, data connectivity, rule modeling, human–machine division of labor, and cross-departmental collaboration.
Against this background, this paper discusses the topic from two perspectives. First, it examines the six common foundational conditions required for oil companies to develop intelligent agents. Second, it analyzes the specific construction conditions that need to be strengthened across different maturity levels from L1 to L5. The purpose of this paper is to answer a core question: what foundational work must oil companies complete in advance in order to truly build, effectively use, and continuously operate intelligent agents?
The business foundation is the first prerequisite for building intelligent agents. In essence, an intelligent agent understands and executes organizational rules. If the business process itself is unclear, responsibilities are ambiguous, and rules are transmitted mainly through verbal communication, the agent will be unable to produce stable outputs, let alone move into an executable state. For oil companies, the first step in advancing intelligent agent development is to clearly define key business scenarios, sort out process boundaries, clarify role responsibilities, and establish the relationships between inputs and outputs. At the same time, relevant policies, specifications, and standard operating procedures should be systematically accumulated, while exceptional cases and escalation mechanisms should be clearly defined. Otherwise, intelligent agents will remain at the demonstration level and will struggle to be truly integrated into actual business operations.
This means that before building intelligent agents, what oil companies need to do first is often not to select a model, but to clarify scenarios. In areas such as production operations, equipment maintenance, safety management, procurement, and contracts, questions such as who should take what action under what conditions, what standards should be used for judgment, and under what circumstances issues should be escalated for manual handling must be clearly defined. Without such clarity, intelligent agents cannot form replicable and verifiable execution logic. If the business foundation is weak, even strong data, knowledge, and system capabilities will be difficult to implement effectively.
All well-structured business foundations should not remain merely at the document level. They need to be modeled and toolized, which requires the construction of a powerful business description model.
The upper limit of an intelligent agent’s capability is often determined by the upper limit of the enterprise’s data foundation. The development of intelligent agents for oil company exploration and development requires ensuring that key business data can be accessed, data definitions are relatively unified, data quality is basically reliable, historical data has been accumulated to a certain extent, and master data, coding systems, and tagging systems are as consistent as possible. For oil companies, particularly critical data includes production data, equipment status data, work orders and maintenance records, safety incidents and hazard records, procurement and inventory data, contracts and supplier data, financial and operating indicators, as well as documents, policies, reports, meeting minutes, and expert experience.
If the data foundation is insufficient, intelligent agents will face two fundamental problems: first, they cannot “see” the business site; second, their responses lack supporting evidence. Therefore, data construction should not stop at simple aggregation. Instead, data should be integrated and managed according to business logic, so that it can be interpreted through business semantics, invoked by rules, and directly used in scenarios. In other words, what intelligent agents need is not isolated data, but a data system that supports business understanding and business execution.
At present, oil companies are not necessarily facing a shortage of data, but rather the absence of a unified data management and service platform. To truly unlock the value of data, it is necessary to systematically address issues such as eliminating data silos, improving data quality, building data relationships, and transforming data into business-oriented assets. This requires a new design philosophy and an integrated management platform for coordinated advancement. Jurassic Software’s JuraData data lake platform provides a comprehensive solution to these challenges.
In oil and gas operations, many critical capabilities do not reside only in structured data. They are also deeply embedded in unstructured knowledge, such as operating procedures, equipment manuals, fault cases, expert experience, meeting resolutions, emergency plans, review comments, and post-event analysis reports. For intelligent agents, if such knowledge cannot be accumulated, classified, retrieved, and dynamically updated, problems will arise: agents may learn outdated rules, provide vague responses, or fail to connect rules with specific work nodes.
Therefore, while advancing intelligent agent development, oil companies must simultaneously build an enterprise-level knowledge system. This system should at least include a knowledge accumulation mechanism, classification and tagging system, version management mechanism, continuous update mechanism, and mechanisms for identifying outdated or invalid knowledge. Only on this basis can intelligent agents produce outputs that are traceable, evidence-based, and aligned with current policies and practical experience in real business scenarios.
In essence, knowledge system development is the process of transforming organizational experience scattered across individuals, meetings, documents, and cases into callable, maintainable, and reusable enterprise capabilities. It is necessary to parse, split, and organize unstructured document data according to business nodes, while also integrating unstructured documents with structured data to form a complete knowledge graph.
A real intelligent agent is not a single model, but a complete operating system. The technical foundation required for intelligent agent development in oil company exploration and development usually includes a large model foundation or model orchestration capability, retrieval-augmented generation capability, tool invocation and workflow orchestration capability, system interface integration capability, permission control and identity authentication capability, log monitoring and operational management capability, as well as the necessary computing power and deployment environment. At a minimum, an intelligent agent must be able to do three things: understand tasks, obtain information, and call tools to execute actions.
Without a platform-based foundation, each new scenario would require separate development, causing construction costs to rise rapidly and making standardized reuse difficult. Therefore, the focus of technical foundation development should not be limited to pursuing the performance of a particular model. Instead, it should focus on building foundational capabilities around unified access, unified invocation, unified orchestration, and unified monitoring. This is especially important because exploration and development operations involve numerous systems and tools. The less unified the technical foundation is, the harder it becomes for intelligent agents to achieve scalable application.
Oil companies operate in a highly secure and highly regulated industry, making governance a non-negotiable part of intelligent agent development. Intelligent agent construction must ensure controllable data access permissions, traceable outputs, auditable key actions, clearly defined human–machine responsibility boundaries, manual review for high-risk scenarios, and mechanisms for preventing model hallucinations, unauthorized access, and misoperations. For example, production parameter adjustments cannot be fully automated; safety-related recommendations cannot be made without sources or evidence; key actions involving contracts, procurement, and payments must retain approval chains; and confidential materials must be subject to graded access control.
If governance is inadequate, the stronger the capability, the greater the risk. For oil companies, intelligent agents are not simply better when they are more autonomous; they must be both efficient and controllable. The governance foundation determines whether intelligent agents can operate within secure boundaries over the long term, and whether the organization is willing to entrust higher-level tasks to them. In this sense, governance capability is not an accessory to technical construction, but a prerequisite for intelligent agents to enter core business scenarios.
Intelligent agent development is not a task that can be completed by the IT department alone. It is a systematic effort requiring the joint participation of business, data, technology, and management teams. To truly implement intelligent agents, oil companies need business departments that are willing to co-build, clearly defined scenario owners, accountable data and system interface owners, and sound mechanisms for operation and maintenance, performance evaluation and iteration, as well as continuous expert involvement in knowledge correction and supplementation. Otherwise, intelligent agent projects are likely to remain at the pilot stage, with strong attention at launch but little sustained usage over time.
The key to the organizational foundation is not merely assigning enough personnel, but establishing a mechanism for continuous operation. Intelligent agents must be integrated into the existing work environment and form a collaborative work system together with people. They are not one-off software delivery projects, but organizational capability-building projects that require long-term operation, continuous optimization, and ongoing correction. The weaker the organizational foundation, the harder it is for intelligent agents to move from pilot projects to normalized operation.
The objective of L1 Q&A agents is to solve problems related to finding knowledge, policies, instructions, and data. Their core value lies not in execution, but in the usability of knowledge. Therefore, this level particularly requires a relatively complete knowledge base, document cleansing and chunking capabilities, tag management capabilities, retrieval accuracy optimization, clearly defined Q&A boundaries, basic citation and traceability capabilities, as well as foundational data construction and the ability to associate data, knowledge, and business contexts. Typical capabilities include enterprise knowledge Q&A, policy and specification inquiry, terminology explanation, document summarization, and basic information integration.
The key success condition for L1 is not how powerful the model is, but whether enterprise knowledge and data coverage are sufficient, whether data quality is reliable, whether answers can cite their sources, whether outdated knowledge may mislead users, and whether the agent truly reduces the need for repeated manual inquiries. In other words, the L1 level primarily tests an enterprise’s knowledge governance and knowledge supply capabilities, rather than advanced execution capabilities.
The objective of L2 assistant-type agents is to help with drafting materials, preparing summaries, and generating initial versions of documents. They still cannot independently complete an entire business task, but they can be deeply integrated into human workflows and improve the efficiency of deliverable production. Therefore, on the basis of L1, L2 must add content generation and contextual understanding capabilities. In particular, it requires various business templates and writing paradigms, samples of common work deliverables, contextual memory and task understanding capabilities, multi-document summarization capabilities, and output format control capabilities. Typical capabilities include generating documents according to templates, summarizing materials from multiple sources, extracting key issues, producing structured conclusions, and adjusting expression styles for different audiences.
The key success criterion for L2 is whether the generated result can be directly revised, rather than requiring users to rewrite it from scratch. Therefore, an enterprise template library, professional expression standards, business context understanding, and output quality evaluation mechanisms are all conditions that must be prioritized at this level. Only when editable drafts are truly usable can L2 move beyond a demonstrative writing tool and become an implementable business assistant.
L3 scenario-execution agents represent the watershed between “being able to speak” and “being able to act.” Their objective is to complete a closed-loop task within a clearly defined scenario. Therefore, this level must have interface capabilities with business systems and enterprise data systems, as well as workflow orchestration capabilities, rule engines, task status management, exception-handling mechanisms, and result feedback and confirmation mechanisms. Typical capabilities include the integration of data retrieval, analysis, and output; automatic process advancement within a single scenario; rule-based action triggering; automatic generation of to-do items, reminders, and records; and automatic formation of business deliverables.
At the L3 stage, the key success factors are no longer limited to the knowledge base or database itself. Instead, they depend on whether systems are truly connected, whether rules can be effectively executed, whether scenario boundaries are clear, and whether manual confirmation nodes are designed appropriately. In other words, the core difficulty of L3 does not lie in answering questions, but in effectively connecting an enterprise’s existing processes, rules, and system resources to form an executable closed-loop task chain. Whether a single scenario can achieve closed-loop operation is the most important criterion at this stage.
L4 role-based proxy agents begin to approach the concept of digital roles. Their objective is to undertake a large volume of repetitive, rule-based, and judgment-based work within a certain type of position. Therefore, compared with L3, what L4 adds is not merely a greater number of system integrations, but more importantly, role-level cognition and continuous operation capabilities. To achieve this, this level particularly requires role objective modeling, job responsibility and permission modeling, cross-scenario task collaboration capabilities, long-cycle task memory and tracking capabilities, multi-objective balancing capabilities, human–machine collaboration mechanisms, and performance and effectiveness feedback mechanisms. Typical capabilities include continuously working across multiple business nodes, handling relatively complex exceptions, prioritizing tasks according to role objectives, and automatically escalating issues to humans for approval at critical nodes.
The key success condition for L4 lies in whether job knowledge modeling has been completed, whether a stable human–machine division of labor has been designed, whether continuous monitoring and correction capabilities are available, and whether the agent can operate over the long term in a cross-system environment. In other words, L4 is not a simple accumulation of multiple L3 scenarios; rather, it is a continuous, stable, and manageable task proxy capability built around job responsibilities.
L5 business-collaboration agents represent the highest level of intelligent agent development. Their objective is to enable multiple professional agents to collaborate around business objectives and form an enterprise-level intelligent operation system. At this level, the capability of any single agent is no longer the core issue; system-level capability becomes the key. Therefore, L5 particularly requires a multi-agent collaboration architecture, a unified identity and permission system, a unified knowledge and data foundation, unified task orchestration and messaging mechanisms, a unified monitoring, auditing, and evaluation system, cross-departmental process integration capabilities, and mechanisms linking business objectives with operational indicators. Typical capabilities include division of labor and collaboration among multiple agents, event-driven coordinated responses, escalation from single-point tasks to chain-based collaboration, and continuous optimization of resources and actions around business objectives.
The key success conditions for L5 are whether enterprise-level master data and process governance are mature, whether departments are willing to collaborate under unified rules, whether a unified scheduling and governance platform has been established, and whether local optimization can be transformed into global optimization. This indicates that L5 is no longer merely a technical issue in essence, but a comprehensive reflection of enterprise governance capability, organizational collaboration capability, and business management capability.
From an overall perspective, the condition system for intelligent agent development in oil companies demonstrates a clear pattern of maturity progression. L1 focuses on knowledge coverage, retrieval, and traceability; L2 focuses on templates, expression, and contextual understanding; L3 focuses on system integration and closed-loop execution within a single scenario; L4 focuses on role-based knowledge modeling and continuous operation; and L5 focuses on enterprise-level collaboration and unified governance. As the maturity level increases, the requirements gradually evolve from making knowledge searchable and queryable, to enabling automatic generation of deliverables, then to achieving closed-loop scenario execution and continuous role-based proxy capabilities, and ultimately to coordinated business operations among multiple intelligent agents.
This also means that oil companies should not pursue intelligent agent development in a one-step manner. Without foundational conditions such as clear processes, usable data, manageable knowledge, platform support, controllable security, and sustainable organizational mechanisms, it will be difficult to move stably into higher-level applications. The higher the development level, the greater the requirements for system integration, rule modeling, human–machine collaboration, permission governance, and organizational coordination.
In actual implementation, oil companies often fall into several typical misconceptions when developing intelligent agents. These include mistaking L1 Q&A capabilities for L4 role-based proxy capabilities, which results in users being able only to conduct conversations rather than execute specific tasks; directly pursuing autonomous execution capabilities without sufficient data and process foundations, making higher-level capabilities difficult to implement in a stable manner; placing excessive emphasis on model capabilities while neglecting critical mechanisms such as governance, permissions, auditing, and responsibility traceability, which may ultimately cause accumulated risks to outweigh efficiency gains; and overlooking the phased evolution path by attempting to reach L5 in a single step, whereas the development process should follow a gradual path: achieving quick results at L1/L2, conducting closed-loop pilots at L3, advancing toward role-based proxy capabilities at L4, and ultimately moving toward an L5 collaborative system.
These misconceptions indicate that the real difficulty in intelligent agent development often does not lie in whether the model can answer well, but in whether it can connect to systems, control permissions, trace responsibilities, and sustain long-term operations. Therefore, the focus of development must shift from demonstrating capabilities to strengthening foundational conditions.
The essence of intelligent agent development for oil companies in exploration and development does not lie in the simple deployment of models. Rather, it lies in building a systematic capability framework driven by business rules, supported by data and knowledge, enabled by technology platforms, constrained by governance mechanisms, and safeguarded by organizational collaboration. Regardless of the stage of development, oil companies must establish six foundational capabilities—business, data, knowledge, technology, governance, and organization—in order to support the effective implementation and continuous evolution of intelligent agents.
On this basis, intelligent agents at different maturity levels correspond to different construction priorities: L1 focuses on knowledge usability, L2 on usable content generation, L3 on closed-loop scenario execution, L4 on role-based proxy capabilities, and L5 on business collaboration. As the maturity level increases, enterprises face higher requirements in system integration, rule modeling, human–machine collaboration, permission governance, and cross-departmental coordination. Only by solidly building these foundational capabilities can oil companies truly move intelligent agent development from proof of concept to business application, from localized efficiency improvement to role restructuring, and ultimately toward enterprise-level intelligent collaboration.
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