Agentic AI in the enterprise: An evolution, not a revolution

Agentic AI is expected to revolutionize a vast array of workflows through autonomous, AI-driven automation. This may work easiest for a startup without legacy systems, processes, or people to account for. But established enterprises tend to have complex ecosystems built over decades: established processes that verify compliance, legacy systems that handle mission-critical operations, and experienced teams whose institutional knowledge drives business success. For enterprises, the real value lies not in disruption, but in strategic augmentation of existing operations. Think evolution, not revolution.

In this article, we’ll provide specific recommendations on how enterprises can benefit through strategic integration of AI tools and processes rather than rebuilding their business from the ground up.

The adoption of agentic AI presents a new set of strategic considerations. Unlike traditional AI—which is built for a single task—agentic AI can make its own decisions to achieve a specific goal. This shift from simple automation to a system of self-governing agents requires a thoughtful, phased approach. Here are three ways enterprises can benefit from agentic AI.

1. Experimenting in low-risk areas

Enterprises can more safely explore agentic AI's potential by starting with contained, low-impact environments where failure won't compromise mission-critical operations. This experimental approach will help your teams build confidence and expertise while minimizing risk. For example, these could be well-defined repetitive tasks, which are easier for an agent to learn and execute.

Example use cases:

• Customer service: Conversational agents can handle basic inquiries or support requests, with seamless handoffs to humans for complex issues. These agents can guide a customer through a password reset process, update shipping addresses, or process simple returns by interfacing with existing customer portals and backend systems.
• Administrative assistant: Agents can summarize meetings, follow up on action items, and summarize and prioritize emails to improve worker productivity.

Applications like these allow workers to focus on higher-value, complex, or sensitive customer issues that truly require human nuance. In addition, this approach demonstrates the value of agents in a controlled environment and helps organizations develop the skills and frameworks needed for larger implementations.

2. Improving performance of backend operations

Agentic AI can work behind the scenes to help make current operations more efficient and intelligent. This approach uses your existing infrastructure while adding a layer of autonomous decision-making that improves performance without requiring users to change their workflows. In effect, agentic AI acts as a smart abstraction layer that can observe data flowing through your core, often complex, backend operations (ERP, CRM, or supply chain systems) and identify bottlenecks, proactively trigger actions, and even correct minor errors.

Example use cases:

• Financial watchdog: Agents that monitor expense patterns, flag anomalies for human review, and automatically categorize transactions.
• Quality inspector: Agents that analyze production data in real-time, identifying potential quality issues and recommending preventive actions.

This approach can deliver immediate value without disrupting established workflows. Your teams continue using familiar systems while benefiting from enhanced intelligence and automation working invisibly in the background.

3. Trainee managers with human-in-the-loop

For enterprises, the real promise of agentic AI may not be full autonomy but collaborative autonomy. Think of agents as trainee managers or co-pilots that have tiered decision authority. The agents have autonomy for low-risk, routine decisions while escalating complex or high-impact decisions to human managers. The agent performs tasks like data gathering, analysis, and initial recommendation, but a human manager provides the final sign-off, especially for decisions that have significant financial, reputational, or legal implications.

Example use cases:

• Business loan officer: The agent autonomously gathers data from financial reports, credit bureaus, and market trends. It then processes this information, analyzes risk factors, and even generates a preliminary recommendation (e.g., "Approve with conditions," "Deny," or "Requires further review"). A human loan officer reviews, validates, and makes the ultimate decision.
• Sales manager: A sales manager agent coordinates multiple sub-agents working on a sales proposal. One agent pulls competitive insights, another agent drafts the proposal, and a third agent checks for pricing accuracy. A human sales manager finalizes and approves the proposal before submission.

Think of this approach as hiring a trainee manager who shadows experienced colleagues, gradually taking on more responsibility as they prove their competence. The agent handles routine work that frees up human managers for higher level strategic work while maintaining oversight and control over these AI assistants.

How Red Hat can help enterprises adopt agentic AI

Red Hat, with its deep roots in open source, is uniquely positioned to help enterprises navigate this evolutionary path to agentic AI. Our approach emphasizes control, flexibility, and enterprise-grade support, which are critical for integrating AI into existing, complex environments.

• Open source foundations for flexibility and control: Red Hat's portfolio, including Red Hat AI, provides a robust, open source foundation for building, deploying, and managing AI models and agentic systems.
• Hybrid cloud consistency: Enterprises often operate across on-premises data centers, multiple public clouds, and edge environments. Red Hat's platforms are designed for the hybrid cloud, so your agentic AI solutions can be developed, deployed, and managed consistently, no matter where your data resides or your applications run. This consistency simplifies operations and enables for seamless scaling.
• Operationalizing AI with MLOps and LLMOps: Red Hat OpenShift AI provides a comprehensive platform for managing the entire AI lifecycle, from data preparation and model training to deployment and monitoring. This includes capabilities for MLOps (Machine Learning Operations) and LLMOps (Large Language Model Operations), which are essential for taking agentic AI from experimentation to reliable production. It helps teams collaborate, automate workflows, and verify that models are performing as expected.

Red Hat provides the enterprise-grade, open source platform that allows businesses to safely experiment, build, and scale agentic AI capabilities within their existing IT landscape.

Agentic AI for the enterprise is not about a disruptive "big bang" that sweeps away your current investments. Instead, it offers a pragmatic, evolutionary path to enhanced efficiency and innovation. By focusing on improving existing backend operations, experimenting in low-risk frontend areas, and thoughtfully integrating agents as "trainee managers" with human oversight, businesses can incrementally unlock significant value over time.

The goal is to augment your organization's capabilities, empower your people, and make your enterprise more intelligent, agile, and resilient for the future.

Article by Ishu Verma, Emerging Technology Evangelist, Red Hat