Defining Agentic AI in the Enterprise Context
Agentic AI in enterprise software is a class of AI agent software that not only retrieves and summarises information but also plans, coordinates, and carries out multi-step tasks across digital systems, enabling autonomous work execution while humans retain oversight of critical decisions. This represents a shift from traditional search and analytics tools, which stop at recommendations, to AI systems that can trigger workflows, update records, and close process loops. In an agentic AI enterprise, the goal is to connect structured data, operational context, and permissions so that AI can act on behalf of users within clear boundaries. That foundation is now emerging across industries, as platforms in construction, industrial operations, and asset management embed agentic logic directly into their core products instead of treating AI as a separate add-on.
Procore’s Datagrid: From Common Data to Autonomous Work Execution
Procore’s connected Common Data Environment shows how agentic AI enterprise platforms are being redesigned for autonomous work execution, not only information retrieval. By bringing project data, workflows, BIM models, and asset information into one environment, Procore gives Datagrid’s agentic AI the structured context it needs to understand relationships between drawings, RFIs, submittals, and site activity. The company describes Datagrid-powered “agentic AI coworkers” that operate inside existing construction workflows, helping automate tasks from approved design to handover while teams keep control and final sign-off. According to Buro Happold’s CTO Alain Waha, “We’re on track to reduce construction administration work with respect to RFI creation, response, and submittal review by 50%.” This kind of AI agent software does more than answer questions; it can surface existing answers from records, flag discrepancies between design and field execution, and trigger follow-up actions across connected workflows.
Building the Foundations for Agentic AI Workflows
The shift toward agentic AI depends on connecting islands of enterprise data into coherent systems that AI agents can reason over. Procore’s CDE ties together BIM, specifications, RFIs, submittals, and site logs so that AI can see both spatial and operational context and then act within that shared record. This same pattern appears in other platforms that combine IoT data, digital twins, and workflow engines to support autonomous work execution. In construction, the connected record also supports compliance by providing a defensible audit trail from design through handover, which matters as regulatory expectations rise. For vendors, the strategy is clear: embed AI into the process fabric, not the user interface alone. When workflows, approvals, and data are all exposed in a consistent way, agentic AI coworkers can coordinate complex processes without breaking traceability or accountability.
Shell and C3 AI: Predictive Maintenance AI at Scale
Shell’s work with C3 AI shows what agentic AI looks like once predictive maintenance AI reaches enterprise scale. Since 2018, Shell has used C3 AI Reliability on Microsoft Azure to monitor more than 13,000 pieces of equipment across its asset base, spotting anomalies before they become failures. Under a new agreement, Shell is extending this programme beyond detection to AI agent-based root cause analysis and remediation. That means AI agents will not only flag abnormal behaviour but also help identify why it happened and suggest or coordinate next steps across reliability workflows. Deloitte estimates that unplanned downtime costs industrial manufacturers about US$50 billion (approx. RM230 billion) each year, so this evolution from dashboards to autonomous AI agent software is more than a technical upgrade. It is a way to tie continuous monitoring, diagnosis, and response into a single, closed-loop reliability system.
From Search Boxes to AI Coworkers
Across sectors, the story is similar: agentic AI is moving enterprise tools beyond static search boxes to systems that can execute work. In construction, embedded coworkers inside Procore reduce repetitive administration so humans focus on complex trade-offs and field decisions. In industrial operations, predictive maintenance AI agents in Shell’s environment connect sensor data, statistical models, and workflow automation to keep equipment available and productive. MarketsandMarkets expects the predictive maintenance market to grow from US$13.89 billion (approx. RM64 billion) in 2026 to US$23.79 billion (approx. RM109 billion) by 2031, reflecting rising investment in these capabilities. The next challenge is governance: organisations must define which actions AI agents can perform autonomously, which require approval, and how to keep audit trails intact. Done well, agentic AI enterprise deployments will look less like chatbots and more like specialised digital coworkers embedded in everyday systems.
