Agentic AI Explained: Definition, Examples and How It Works

Traditional AI models focus on narrow tasks such as classification or prediction. Generative AI produces outputs like text or images based on input prompts.

Agentic AI goes further by:

• Planning sequences of actions

• Using reasoning to evaluate next steps

• Executing workflows across tools and systems

• Iterating toward a defined goal

Not exactly. AI agents are components that can perform specific actions, while agentic AI systems orchestrate multiple agents, tools and workflows into a coordinated process. Agentic AI is the broader system-level capability.

At a high level, agentic AI works through a continuous cycle of understanding a goal, breaking it into actionable steps, selecting the right tools, taking action and evaluating the results. Rather than generating a single response, the system uses planning, reasoning, tool integration and feedback loops to move through a workflow and adjust its approach as conditions change.

Goal-Oriented Planning and Reasoning
Agentic AI begins with a defined objective. It breaks the goal into smaller steps, evaluates options and determines the best sequence of actions to achieve the outcome.

Tool Use and Orchestration
These systems don’t operate in isolation. They interact with APIs, databases and enterprise tools to complete tasks. Orchestration ensures the right tool is used at the right time.

Multi-Agent and Multi-Step Workflows
Agentic AI can coordinate multiple agents — each responsible for a specific function — within a larger workflow. This enables more complex, cross-functional automation.

Role of Large Language Models (LLMs)
LLMs act as the reasoning layer for many agentic AI systems. They interpret context, generate plans and guide execution, but are integrated into a broader system that handles action and control.

Understanding the distinctions between agentic AI, generative AI and chatbots can help organizations choose the right approach for each use case. While generative AI creates outputs, chatbots facilitate interactions and agentic AI coordinates actions across tools and workflows to reach a defined outcome.

Generative AI vs. Agentic AI
Generative AI creates content based on prompts. Agentic AI uses generative capabilities as part of a larger system that can plan and act.

• Generative AI: output-focused
• Agentic AI: outcome-focused

Is ChatGPT an Agentic AI?
On its own, ChatGPT is not currently agentic AI. It becomes part of an agentic system when integrated with tools, memory and workflows that enable it to take action beyond generating responses.

When LLMs Become Agentic Systems
LLMs transition into agentic systems when they are connected to:

• Planning frameworks
• External tools
• Execution environments
• Feedback loops

Agentic AI is especially useful in environments where work spans multiple systems, decisions need to happen quickly and teams need automation that can adapt as conditions change. The examples below illustrate how agentic AI can move beyond simple assistance to coordinate actions, resolve issues and support more complex business workflows.

Customer Service and Support Automation
Agentic AI systems can handle end-to-end customer interactions — interpreting requests, retrieving data, resolving issues and escalating when necessary — all in real time.

IT Operations and Workflow Automation
In IT environments, agentic AI can monitor systems, detect issues, initiate remediation steps and coordinate across tools without manual intervention.

Data Analysis, Summarization and Decision Support
These systems can gather data from multiple sources, analyze trends, generate insights and deliver recommendations to support decision-making.

Predictive and Real-Time Use Cases
Agentic AI enables real-time automation, such as responding to security events or adjusting operations dynamically based on changing conditions.

Agentic AI delivers business value by enabling:

• Faster decision-making through continuous reasoning and analysis
• End-to-end task execution across systems and processes
• Reduced human intervention in repetitive or complex workflows
• Scalable automation tailored to domain-specific use cases

As agentic AI systems gain the ability to access tools, make decisions and execute workflows, the risk shifts from inaccurate outputs to actions that can affect data, operations and security. Organizations need clear oversight, access controls and validation steps to reduce the chance of unintended consequences as these systems become more autonomous.

Need for Human Oversight and Guardrails
Even advanced systems require human oversight. Guardrails ensure outputs align with expectations and business policies.

Reliability and Hallucination Risks
Because agentic AI often relies on LLMs, inaccuracies can occur. In multi-step workflows, errors can propagate if not controlled.

Governance, Safety and Control
Organizations must establish governance frameworks to manage agentic AI security risks, ensure safety and maintain control over autonomous actions.

Agentic AI is best suited for scenarios that involve:

• Complex, multi-step workflows
• Cross-system orchestration
• Real-time decision-making
• High-volume task automation

Readiness Considerations
Before adopting agentic AI, organizations should evaluate:

• Data quality and accessibility
• Tooling and integration capabilities
• Risk tolerance and governance maturity

Where Non-Agentic AI Is Still Sufficient

Not every use case requires full autonomy. Simpler tasks — like content generation or predictive analytics — may still be better served by traditional or generative AI.

For further guidance read our agentic AI readiness checklist.

What exactly is agentic AI?
Agentic AI is a goal-driven AI system that uses planning, reasoning and tools to complete multi-step tasks with limited supervision.

What is the difference between generative AI and agentic AI?
Generative AI produces outputs. Agentic AI uses those outputs within workflows to achieve broader objectives.

What is the 30% rule in AI?
The content brief references this question, but it does not define the rule. Interpretation may vary, so organizations should validate how it applies within their AI strategy.

Are agentic AI systems fully autonomous?
No. While they can operate with limited supervision, human oversight, governance and control mechanisms remain critical.

Agentic AI represents a shift from isolated AI tasks to intelligent systems that can plan, act and deliver outcomes across workflows. For organizations, the opportunity lies not just in automation — but in redefining how work gets done.

The key is balancing autonomy with control: combining advanced AI capabilities with the governance, oversight and strategy required to use them effectively. CDW can help:

• Identify high-impact use cases for agentic AI
• Assess your readiness across data, tools and risk
• Design and implement scalable AI-driven workflows