Agentic Systems in Oil & Gas

What Makes an AI Agent Different From a Chatbot?

A chatbot and an agent can run on the very same language model and still do different jobs. A chatbot answers one message and waits — the next step is always up to the human. Classic automation (RPA, macros, hard-coded integrations) goes further: it runs a fixed sequence of actions with no human in the loop, but on an unchanging script that breaks the moment reality deviates from the template.

An agent works differently. It decides for itself what data and tools a given task needs, chains the steps on its own, and carries the task through to a result — adjusting the plan along the way if it has to. That's the line SPE's own trade press draws between a "digital" oilfield and an "agentic" one.

«The agentic oil field goes well beyond a digital oil field. In simple terms, the digital oil field focuses on connecting equipment, collecting data, and giving greater visibility to professionals, with humans remaining at the center of command. In an agentic oil field, AI agents and generative AI systems understand data, reason through scenarios and options, and make decisions with minimal human intervention.» — Satyam Priyadarshy, SPE — JPT, Guest Editorial

In practice that looks like a loop, not a one-off reply. SLB's upstream agent platform, Tela, runs a repeating five-step cycle — observe, plan, generate, act, learn — on every step, not once at launch.

«Tela doesn’t just automate tasks—it can understand goals, make decisions and take action.» — Rakesh Jaggi, SLB — JPT

The gap is clearest exactly where classic asset-evaluation automation used to stand: a scheduled script pulling the same data and building the same report. An agent decides what data is missing, where to get it, and how to cross-check the result before a human ever sees it.

Why Isn't One Strong Model Enough?

A single prompt to a single large model is not an agentic system, however capable that model is. Field tasks are too different in kind: reading seismic data, parsing P&ID diagrams, routing tanker logistics and modelling pipeline corrosion each need different data, different tools and different checks. One model for everything either falls short of real depth in any one domain, or turns into an unwieldy monolith nobody can audit.

The working pattern runs the other way: many narrow agents, each with its own data and tools, under a coordinator that decides who gets the task and how the pieces come back together. One example from consulting practice: a corrosion-monitoring workflow where separate agents pull thickness-measurement data and read P&ID drawings, others reformat it for the asset-monitoring system, and a coordinating agent assembles the result into a single decision.

Marine logistics runs on the same logic: a vessel-operations manager states a request in plain language, and a vessel-reporting agent, an arrival-forecast agent, a port-congestion agent and a berth-booking agent each handle their own piece — inside one chat with a human.

«With the rise of agentic automation, enterprises are reimagining the art of the possible. In oil and gas, a highly data-driven sector, there is a drive to embrace this shift, with agents designed to work with humans, delivering improved value across the supply chain.» — Ken Brown, PwC Middle East — PwC India

How Is AVP AI's Agentic System Built?

AVP AI — an AI platform for oil and gas asset evaluation — is built on exactly this pattern: not one universal prompt to a model, but a coordinator and eight specialised agents — search, geology, reservoir simulation, surface facilities, economics, wells, business case and data standardisation. The coordinator takes the task, decides which agents handle which part and in what order, then assembles the results into one report.

Together, the agents run 34 calculation tools — from analog selection to the NPV/IRR model. Every step is logged: what data was used, from which source, and what formula produced the result — so the final number can be checked, not just trusted.

That's the practical answer to the risks above: an agent doesn't replace a geologist or an economist, it takes over the routine data assembly and first-pass calculation, leaving the human the part that can't be automated — checking the assumptions and making the call. What each agent actually calculates is covered in AI in oil and gas; the fastest place to see an agentic system's effect is license area screening, where an analyst used to spend weeks consolidating spreadsheets across a dozen assets — the agents now do it in a single pass.