Understanding agentic AI use cases

Agentic AI uses large language models (LLMs) and tools to build on the power of generative AI (gen AI). It works by connecting to and communicating with external sources to perceive, decide on, and orchestrate a whole automated task to achieve a defined goal. 

To put agentic AI into practice, you create a system that gives an LLM access to external tools and to logic that supplies instructions for how the agentic system should use those tools. An agentic system is a layer that drives and operates other software tools. 

Agentic AI can be a physical structure, a software program, or a combination of the 2. For example, an AI agent in a robotic system might use cameras, sensors, and monitors to collect data about its environment, then run that information alongside software to determine its next step. This is a process known as sensor fusion. 

In a software setting, agentic AI would collect data from other sources—such as application programming interfaces (APIs), online searches, text prompts, and databases—which helps the agents create a sense of perception and context.

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Agentic AI vs. AI agents

An AI agent is a noun (“I’m building 3 agents.”). It refers to a software entity built to work and perform a role within an agentic system.

Agentic AI is descriptive and refers to the behavioral characteristics of a system (“We need to make our software more agentic.”). It describes a system that can plan, make decisions, and take action toward goals with limited human guidance. 

A brief history (of the brief history) of AI agents

Early iterations of AI agents involved basic API function calling. At that point, AI agents started creating instruction sets for your systems. 

Next, frameworks turned a single tool call into a loop: reason, act, observe, and repeat. 

Then, Model Context Protocol (MCP) came along and standardized communication between applications and services. This made it easier than ever for tools, resources, and prompts to be exposed to and discovered by an MCP client (the component that requests access to external data or resources). 

Today, we have autonomous coding agents and multi-agent systems where 1 agent plans tasks and delegates them to subagents it creates on the fly. The human moves from being in the loop to being notified by the loop.

So, if your agent can act on real systems, with real credentials, over long time horizons, what's stopping it from doing the wrong thing?

AgentOps. 

AgentOps (agent operations) is a framework of tools for monitoring the “brain” of an AI as it makes decisions in real time. 

The actions of agents are nondeterministic—that is, decided by a series of random probability distributions. This means we can’t precisely predict their actions. Agentic workflows can help creatively solve problems, but that creativity needs to be managed so systems don’t go rogue.

AgentOps addresses the production layer, making software more reliable. AgentOps helps by:

• Tracking hardware resources.
• Monitoring hallucination rates.
• Ensuring data stays encrypted.
• Providing an auditable log of actions made by the agent.
• Terminating a process if policy violations occur.

In an enterprise setting, AgentOps is critical for agentic workflows. It provides AI systems that are built on your infrastructure, trained on your data, aligned with your values, and following your rules. 

Learn more about AgentOps

Retrieval-augmented generation (RAG) is a method for getting better answers from a generative AI application by linking an LLM to an external resource. Agentic RAG takes traditional RAG a step further by enabling the LLM to actively investigate rather than simply retrieve.

While RAG can retrieve answers and provide some context from documentation and data sets it has access to, it relies on manual prompt engineering. Traditional RAG also has limited contextual awareness and relies exclusively on the initial query to retrieve relevant information.

Agentic RAG is comparatively more sophisticated and dynamic. It can come up with questions of its own, create context from its memory, and carry out additional tasks without being explicitly asked to do so. This step beyond traditional RAG grants agentic RAG the ability to make more informed decisions on your behalf, independently of your manual intervention.

When models stop answering and start acting

Agentic AI serves many purposes. Let’s take a look at how different industries may use it:

Agentic AI in finance

Financial teams oversee millions of transactions, making manual fraud detection and policy enforcement impossible to keep up with in real time. 

Autonomous financial agents can continuously monitor daily transactions and flag for fraud or policy violations before they escalate. Agents ingest live data, cross-reference it with internal rules, and flag anomalies. From here, human reviewers step in to analyze the flagged suspicious activity and make a final judgment call on whether to take action.

Agentic AI in IT and software engineering

Developers spend hours reproducing bugs and writing fixes that could otherwise be spent on building and improving new features. AI agents can help triage by reproducing bugs in a sandbox, drafting potential fixes, and running tests to verify whether the patches work. Once a solution is verified, a human developer reviews code and approves deployment to production.

Agentic AI in supply chain management

External factors like extreme weather, labor strikes, and port congestion can disrupt supply chains, leading to delays and financial losses. Agents can track news updates, monitor weather, and alert teams to potential delays. They could even calculate the financial impact of rerouting. Supply chain managers then review the agent’s cost-benefit analyses and make a final call on which strategy to proceed with.

Agentic AI for autonomous vehicles

Self-driving vehicles have to navigate complex real-world environments, which means interpreting massive streams of conflicting sensory data. An agentic sensor fusion system merges disparate raw input (from cameras and radar) into a cohesive, real-time model of the vehicle's surroundings. The agentic system processes, organizes, and filters data from every vehicle sensor to make sense of visual ambiguities and predict collision risks in milliseconds. Human safety drivers or remote operators oversee edge-case exceptions, monitor the reliability of these systems, and take control manually when necessary.

Agentic AI in communications and public relations

The online world moves faster than any human communications team can be expected to keep up with. Stories break 24x7, sentiment can change in minutes, and a small news story can turn into a crisis quickly.

Agents can spend all day and night tracking global brand mentions across platforms and create a summary to gauge public mood. They can also scan news stories, recently published academic papers, and social trends to compile daily briefings. Then agents can summarize these insights to help teams stay ahead of industry shifts. From these briefs, human communications professionals can craft high-stakes messaging and strategize PR responses for escalated incidents.

Agentic AI in human resources

Onboarding new hires requires repetitive administrative tasks, scheduling, and a constant flow of questions that can drain resources. To assist with this, a dedicated onboarding agent can guide new hires through documentation, IT setup, and training. It can help new hires answer specific questions and schedule introductory meetings based on the employee’s role and department. Freeing up HR leaders’ and team managers’ time lets them focus on high-touch cultural integration, mentorship, and building personal connections with the new hire. 

Agentic AI in legal and compliance

Global trade laws and environmental regulations shift all the time, making it easy for international corporations to fall out of compliance. Agents can continuously track regulatory updates worldwide and analyze how changing legal frameworks impact operational workflows. This helps organizations stay ahead of legal risks without requiring manual policy reviews. Once an agent flags a specific area that may need internal adaptation, a human legal counsel can interpret the findings and draft updated corporate policies if needed. 

Agentic AI in marketing and sales

In a competitive marketplace, static pricing can lead to lost revenue. Agents monitor competitor pricing and market demand in real time. They suggest price adjustments to maintain an edge while protecting profit margins. Marketing and revenue leaders set pricing strategy boundaries, review automated recommendations, and approve promotional campaigns. 

Agentic AI in healthcare

Healthcare workers are overwhelmed with administrative data, which can lead to burnout for the provider and oversights in the patient’s care. A clinical assistant agent can help organize patient data, flag missing lab results, and alert the team to potential medication conflicts or dosage issues. Doctors and clinical staff treat the agent as a safety net, reviewing the flagged alerts and spending more time delivering direct patient care. 

Explore agentic use cases with Red Hat

Red Hat® AI operationalizes the full lifecycle of an agent through a dedicated AgentOps control plane. This helps keep deployments safeguarded, observable, and efficient across your hybrid cloud environment. 

The platform provides enterprise-grade governance through integrated safety guardrails. Its underlying infrastructure uses vLLM and llm‑d for high-performance distributed inference, so you can scale resource-intensive workflows, from on-premise to edge environments.

Red Hat AI offers fast, flexible, and efficient inference through its vLLM-powered server. It reliably connects models to your data to unify the customization and development of specialized agents on a single platform. Built on an open source foundation, our AI products give you full control of AI workflows from end to end at any scale.