Kuwait Oil Company AI Center debuts agentic drilling with Ghaia.ai's G Agent, boosting rig productivity and planning

Kuwait Oil Company brings agentic AI to drilling operations

Kuwait Oil Company (KOC) has delivered the first milestone from its new Artificial Intelligence Innovation Center: an AI-driven rig drilling project built on Ghaia.ai's G Agent platform, in collaboration with Microsoft and Halliburton. The initiative boosts productivity, streamlines planning, and tightens real-time operational analysis across drilling rigs.

KOC Chief Executive Officer Ahmad Al-Eidan said the project has produced "tangible results in raising productivity and improving planning and operations." He noted that AI agents improve system integration, analyze operational data instantly, and support faster, proactive decision-making that aligns with New Kuwait 2035.

What G Agent brings to the field

According to Ghaia.ai, G Agent deploys digital agents that "think, act, and evolve," creating a mesh for collaboration between agents and human teams. This approach goes beyond static automation, enabling cross-department workflows in energy, retail, and government.

For IT and development teams, the shift is clear: move from dashboards that report to agents that act-with controls, approvals, and audit trails baked in. This is a path to scale AI across rigs, wells, and support functions while keeping humans in the loop.

Ghaia.ai | Microsoft Azure for Energy

Why it matters for engineering and IT

• Real-time decisions: Agents consume telemetry and operational data to suggest or trigger actions faster than manual processes.
• Integrated workflows: A single fabric across drilling, maintenance, logistics, and HSE reduces handoff delays and data silos.
• Human-in-the-loop: Approvals and policy checks keep critical decisions governed and traceable.
• Scalable patterns: Agent orchestration can extend to supply chain, cost control, and asset integrity.

Common building blocks for agentic drilling (practical view)

• Data ingestion: Stream rig data (e.g., WITSML/OPC UA), well logs, and planning inputs into a secure, low-latency pipeline.
• Context layer: Use a unified data model and metadata for wells, rigs, procedures, and constraints. Add retrieval over structured and unstructured sources.
• Agent orchestration: Coordinate task-specific agents (planning, monitoring, logistics) with clear roles, policies, and escalation paths.
• Controls: Human approvals for high-impact actions, change windows, and fail-safes for unsafe recommendations.
• Observability: Full telemetry for agent decisions, prompts, actions, and outcomes to support audit and continuous improvement.
• MLOps and SecOps: Versioning, rollbacks, secrets handling, and model evaluation tied to operational KPIs.

Training and talent: building the bench

The AI Innovation Center, launched with support from the Kuwait Direct Investment Promotion Authority, is training Kuwaiti professionals on modern AI systems and enterprise integration. Al-Eidan called it a cornerstone for applying AI at KOC and a step toward a stronger digital future in the region.

Microsoft's Middle East and Africa President, Naeem Yazbek, highlighted a dual focus: accelerating digital transformation and developing local talent through practical upskilling of engineers, analysts, and technical experts. The center serves as a joint innovation platform and a base for smarter energy solutions.

Governance to keep operations safe and compliant

• Policy-first design: Define actions agents can take, when they require reviews, and which roles approve them.
• Risk tiers: Separate advisory, semi-autonomous, and autonomous actions with clear thresholds and rollbacks.
• Data protections: Apply least-privilege access, lineage tracking, and redaction for sensitive operational data.
• KPIs that matter: Tie agent performance to NPT reduction, cost per foot, HSE incidents, and SLA adherence.

What to watch next

• Broader deployment across rigs and fields with standardized agent policies.
• Deeper integrations with ERP/CMMS for maintenance and inventory automation.
• Continuous training programs to expand internal capability and reduce time-to-value.

This move signals a practical path for AI agents in heavy industry: measurable productivity, faster planning cycles, and safer, more consistent operations-without losing human oversight.

Want to upskill your team on automation and agent workflows? Explore the AI Automation Certification for engineers and builders.