Signals from the field arrive fragmented and delayed. When analysis moves slower than the field, operators pay for it. Agentic AI shortens the path from signal to action, with the governance, traceability, and control that production workflows demand.
Predictive AI can detect a pattern or generate a forecast. But humans still have to interpret the result, validate it against current conditions, coordinate across systems, and act before the opportunity or the risk changes underneath them.
Predictive models are good at this part. They forecast production, flag equipment risk, and recommend artificial lift parameters. The signal is real. The clock starts the moment it appears.
Predictive AI can detect a pattern or generate a forecast. But humans still have to interpret the result, validate it against current conditions, and act before the opportunity changes underneath them.
The agent interprets new context, checks the action against approved operating limits, interacts with the relevant production system, notifies the lease operator with its rationale, and logs every step for review. Humans stay in control; the waiting disappears.
Pick the profile that sounds most like your operation. We'll show you the strongest first agent and the outcome it's accountable for.
A pilot that works in a cloud sandbox doesn't prove the agent can support drilling, production, or field operations under real constraints. The highest-value decisions happen close to the asset, where connectivity may be limited and latency matters.
A useful demo isn't enough for workflows tied to production, safety, and capital-intensive assets. Each stage creates the evidence that justifies the next. The control stack makes every stage safe.
Agents monitor changing conditions, summarize what changed, surface risks, and recommend next steps. Humans own every action. Trust gets built here.
Agents carry work across systems within a defined scope: open a ticket, check inventory, route an exception, prepare a recommendation for approval.
Where behavior is proven and limits are clear, agents initiate approved steps, like a lift cycle-time change inside narrow operating boundaries, with full traceability.
The highest bar. Agents support decisions at the rig or pad when connectivity is limited, tested for degraded conditions, with telemetry preserved for review.
Least-privilege access tied to the agent's role: what it can see, what it can change, which tools it can call, and when human approval is required.
Reconstruct how any recommendation or action happened: model version, prompt, tools, data sources, constraints checked, and who approved the next step.
An agent can be "up" and still behaving badly. Track recommendation quality, exception rates, tool use, drift, and business outcomes, not just system health.
Explicit boundaries that reflect operating limits, safety requirements, and approval thresholds, and clarity about when the agent continues, pauses, routes to a human, or stops.
Every meaningful step captured from the start: inputs, retrieved context, tool calls, recommendations, approvals, actions, exceptions, and outcomes.
Maturity matters more than size. Pick the path that matches where your organization stands today.
wells. One North American E&P monitors oil, water, and gas data in real time across more than 13,000 wells to support safer, more efficient field performance.
users. One global supermajor scaled its governed AI program from 10 users to more than 1,300 in four years, a foundation for enterprise AI operations and lifecycle governance.