Woodside deploys Startup Advisor agent for LNG plant startups

Woodside spent a decade ingesting sensor streams from compressors, valves, and heat exchangers before layering agentic workflows. The Startup Advisor encodes startup sequences as constrained decision trees that reference live telemetry and historical failure modes. This replaced ad-hoc checklists with policy-guided recommendations that operators can accept or override.

Data from the deployment shows reduced variance in startup duration across shifts and fewer manual interventions logged in the distributed control system. The company reports standardized data schemas and model governance as prerequisites; without them, agent outputs could not be audited against safety instrumented functions. Comparable patterns appear in Siemens Energy turbine control logs and a 2024 IEEE paper on reinforcement learning for compressor trains.

The shift moves AI from advisory dashboards into the actuation loop of critical infrastructure. Woodside's stated target is an autonomous enterprise where agents hold bounded agency over workflow state machines. This requires explicit separation between recommendation layers and safety shutdown logic rather than end-to-end learned policies.

Next milestones include closed-loop optimization of steady-state plant setpoints and cross-asset coordination between upstream and liquefaction trains, with Woodside targeting measurable reductions in flaring events by 2027.