From AI Answers to Agentic AI Execution
AI agents in the enterprise are software systems that can understand business context, coordinate across applications, and execute multi-step tasks on behalf of human teams under explicit controls. This shift moves AI from passive question answering toward agentic AI execution embedded in day-to-day operations. For years, most AI tools in enterprises have focused on information retrieval: summarising documents, answering queries, or generating text. That is useful but leaves a gap between AI insight and real work. New infrastructure automation platforms aim to close that gap by giving AI agents secure access to workflows, telemetry, and approvals. In this emerging model, AI agents run continuously, monitor systems, propose changes, and trigger workflows, while humans approve actions and define guardrails. Cisco and Procore show how different sectors are building AI agents enterprise foundations that connect data, tools, and teams into a shared operational environment.
Cisco Cloud Control: A Command Center for AI Agents and Ops Teams
Cisco Cloud Control is positioned as a single command center where human IT teams and AI agents share one operational view of networks, security, compute, observability, and collaboration tools. The platform gives both people and agents access to the same data layer, with decision-making authority still reserved for humans. Jeetu Patel describes AI agents as operating “continuously at software speed,” changing how organizations manage and defend critical infrastructure. Cloud Control ties into Cisco’s AgenticOps vision by combining cross-domain telemetry, purpose-built AI models, and autonomous agents that can identify issues, recommend fixes, test changes, and verify outcomes before deployment. With Cisco AI Canvas, operators and agents can investigate and resolve incidents together, while Cloud Control Studio lets teams create custom agents that connect to more than 50 third-party platforms, turning the environment into an AI agent management and infrastructure automation platform.
Security, Quantum Risk and Zero Trust for AI Agents
As AI agents begin to execute actions directly in production environments, the security model around them needs to change. Cisco is framing this as an expansion of Zero Trust to agents themselves, embedding Agentic Security into hybrid environments. Cloud Control is paired with capabilities such as AI Defense, Hybrid Mesh Firewall, and the expansion of Live Protect, which shields supported products from newly discovered vulnerabilities without reboots or downtime. At the same time, the platform prepares for quantum-era threats. Cisco is introducing quantum-safe secure boot on new infrastructure products and aims to enable quantum-safe communications across most of its core portfolio by December 2026. Quantum Ready Assessments through Cisco IQ help organizations identify systems at risk of “harvest now, decrypt later” attacks, aligning AI agent management with longer-term cyber resilience planning and architectural control.
Procore CDE and Datagrid: AI Agents Embedded in Construction Workflows
Procore’s new connected Common Data Environment (CDE) shows what agentic AI execution looks like in a project-based industry. By embedding Datagrid’s agentic AI directly into Procore, the platform connects project data, workflows, BIM models, asset information, RFIs, submittals, and site activity into one environment. This gives AI agents the structured, spatial and operational context they need to run construction workflows, not only retrieve information. Procore describes these agents as “AI coworkers” that remove administrative friction while keeping professional judgment, accountability, and final approval with project teams. According to Procore, the system can surface answers already contained in project records before new RFIs are raised and identify discrepancies between approved designs and field execution. Alain Waha of Buro Happold says, “We’re on track to reduce construction administration work with respect to RFI creation, response, and submittal review by 50%.”
Architectures for Controlled Autonomy Across Enterprise Platforms
Cisco Cloud Control and Procore CDE point to a common architectural pattern for AI agents enterprise adoption: unify data and workflows, embed agents in the same operational layer as humans, and enforce strict controls on execution. In both cases, AI agents operate from a shared context but within guardrails where humans retain authority. That requires new thinking about identity, authorization, auditing, and lifecycle management for agents as first-class users of systems. Platforms must record who (or what) did what, when, and under which policy, so that automated changes are traceable and compliance needs are met. Procore highlights defensible audit trails across the project lifecycle, while Cisco links agentic operations with quantum-safe infrastructure and continuous protection against fast-moving vulnerabilities. Together, these developments show how infrastructure automation platforms are becoming the execution layer that turns agentic AI from isolated experiments into dependable operational coworkers.
