The next frontier of offshore performance: autonomous systems and agentic AI

Like many sectors, oil and gas companies are investing hundreds of millions of dollars in artificial intelligence (AI). In offshore operations, agentic AI opens a new frontier by enabling autonomous systems that continuously sense, decide, and learn within clearly defined boundaries. Fully capturing this value, however, requires sustained investment in end-to-end workflows, change management, and robust governance and controls, along with clear accountability for how value will be realised.

There are three areas where agentic AI can reshape offshore operations. 

1. Integrated planning and scheduling

Each day offshore requires coordinating hundreds of interdependent activities, including vessel movements, crew rotations, maintenance work, and well interventions, all brought together in an integrated logistics plan. These activities are constrained by factors such as weather, permits, personnel, inventory, infrastructure availability, and other dependencies. Historically, planning has relied on spreadsheets that must be manually rebuilt whenever conditions or priorities change.

Agentic AI replaces manual, reactive planning with a mixed-integer optimisation core supported by self-learning agents. Each agentic system represents a vessel, base, or crew and respects hard constraints, including crew-rest rules, SIMOPS restrictions, and weather windows. By combining live AIS data, forecasts, and ERP inputs, the system translates operational needs into vessel, capacity, and schedule requirements, then automatically updates plans as conditions evolve—minimising downtime and presenting prioritised options for human selection.

2. Production optimisation

While traditional simulators remain central to flow assurance, they struggle to respond to real-time uncertainty. Hybrid AI–physics models provide fast surrogates, leveraging historical sensor and production data to deliver sub-minute predictions of pressure, flow, and temperature.

Agentic controllers embed these models in a closed-loop system where agents detect anomalies, test corrective actions, and automatically adjust operating parameters such as chokes, gas lift, compressors, and injection rates. Outcomes are logged to continuously retrain and improve the model.

Deployments in the North Sea and Gulf of America have shown 3–10% production uplift and quicker recovery from disruptions. This mirrors Boston Consulting Group insights that, although adoption remains nascent, hybrid data-driven systems can unlock meaningful value at scale. Continuous adaptation enables wells and facilities to self-optimise within operational constraints, while engineers retain control through explainable dashboards—reinforcing trust through transparency.

3. Autonomous drilling

Drilling represents the most advanced application of automation. Existing closed-loop systems already control parameters such as weight on bit, rotary speed, and mud flow through layered inner and outer

control loops. The next evolution is agentic drilling, in which AI agents analyse downhole telemetry, evaluate scenarios using hybrid physics–AI models, and autonomously fine-tune steering parameters.

Today’s systems already adapt in real time, with the next wave expected to coordinate across wells and apply reinforcement learning to optimise entire campaigns. As a result, drillers shift from manual control to supervising learning agents. While still nascent, this approach offers substantial potential for the future AI-driven oil field.

The future of agentic AI

Agentic AI has the potential to generate significant value, but scaling it responsibly demands discipline and rigor.

Organisations that succeed in adoption typically excel in four areas:

• Anchor agents to business outcomes: Each agent is explicitly tied to a measurable performance metric—such as vessel days, barrels produced, or hours per well—and directly linked to executive-level KPIs.
• Establish shared infrastructure: Core capabilities such as data historians, control gateways, and cybersecurity layers are standardised across pilots to enable deployment at scale across multiple basins and facilities.
• Govern for confidence and control: Clear boundaries are set between decision support and autonomous action, autonomy is phased with defined human-in-the-loop roles, and comprehensive audit trails are maintained.
• Drive field-level adoption: Agents are embedded into everyday workflows, field teams build familiarity and confidence in AI-driven performance, tolerance for error is developed, and agentic tools are positioned as collaborators rather than competitors.

Offshore operations remain the ultimate testing ground for AI—where algorithms must perform in concert with steel, complex physical systems, harsh environments, and human crews. The operators that move beyond pilots and embed agentic AI into daily operations will set the next frontier of performance.