If you’re an enterprise leader trying to make sense of AI, you’ve likely noticed a shift in the conversation. ChatGPT and copilots were impressive—but now there’s talk of agentic AI: systems that don’t just answer questions, but take action to achieve goals. What does this mean for your organization?
The numbers suggest this isn’t hype. According to Gartner, 40% of enterprise applications will be integrated with task-specific AI agents by the end of 2026, up from less than 5% in 2025. That’s an 8x increase in a single year. McKinsey’s 2025 State of AI report found that 62% of organizations are already experimenting with AI agents, and 79% say they’ve adopted agents to some extent.
This guide cuts through the hype to explain what makes AI “agentic,” how it differs from the chatbots and copilots you’re already using, and what enterprise leaders need to know as autonomous agents become a reality.
The Evolution of Enterprise AI
To understand agentic AI, it helps to see where we’ve been.
Traditional AI (2020-2022) consisted of machine learning models that predict outcomes based on patterns. Think fraud detection scoring, demand forecasting, or customer churn prediction. These systems were powerful but passive—they required humans to interpret results and take action on the insights they provided.
Chat AI (2023) brought large language models that respond to prompts with natural language. ChatGPT made AI accessible to everyone, enabling research assistance, content drafting, and customer service chatbots. But these systems had no ability to take action—they could only provide information and leave the execution to humans.
Copilots (2024) represented AI assistants that augment human work with suggestions and completions. GitHub Copilot, Microsoft 365 Copilot, and Salesforce Einstein GPT define this generation. They’re context-aware and integrated into workflows, but humans remain in control of every decision. The AI suggests; the human decides and executes.
Agentic AI (2025-2026) introduces autonomous systems that take action to achieve goals with minimal human intervention. These agents don’t wait for prompts—they plan multi-step workflows, use tools and APIs, and execute end-to-end processes. For a deeper exploration of how this evolution is unfolding, see our analysis of enterprise AI’s evolution from prediction to action.
Six Core Characteristics of Agentic AI
What makes an AI system truly “agentic”? According to Gartner, autonomous agents are combined systems that achieve defined goals without repeated human intervention, using a variety of AI techniques to make decisions and generate outputs. They have the potential to learn from their environment and improve over time. Look for these six characteristics.
Autonomy means the system takes action without constant human input. It operates independently within defined boundaries and escalates only when necessary. Think of it like a trusted personal assistant who knows to book your recurring monthly flight without asking each time, but will check with you if prices exceed your usual budget. Gartner predicts that by 2029, agentic AI will autonomously resolve 80% of common customer service issues without human intervention.
Planning enables the system to break down complex tasks into actionable steps. It creates execution plans and adjusts based on outcomes and changing conditions. Like a seasoned chef preparing Thanksgiving dinner—they know to start the turkey first, prep sides while it cooks, and adjust timing if guests arrive late. The planning capability is what transforms a responsive system into a proactive one.
Tool Use allows the system to integrate with other systems via APIs, databases, and applications. It orchestrates multiple tools to complete end-to-end workflows. Think of a general contractor who doesn’t just plan your kitchen remodel—they actually pick up the phone to coordinate electricians, plumbers, and inspectors to get the job done. Agentic AI doesn’t just recommend calling the API; it calls it.
Memory maintains context across interactions and sessions. The system remembers past decisions, user preferences, and workflow state. Like your family doctor who remembers your medication allergies from three years ago, your preferred pharmacy, and that you respond better to evening appointments. Memory transforms one-off interactions into ongoing relationships.
Reasoning enables decisions based on goals, constraints, and context. The system evaluates trade-offs and selects optimal actions given the information available. Like a financial advisor who weighs your retirement goals against current cash needs and recommends whether to max out your 401(k) or pay down your mortgage. The reasoning is transparent and auditable.
Learning allows the system to adapt from feedback, successes, and failures. It improves performance over time through experience and reinforcement. Like a barista who remembers you liked your latte extra hot last time, tries it that way again today, and asks for feedback to get your order perfect every visit. Learning agents get better the more they’re used.
For a comprehensive exploration of these characteristics with interactive examples, the Future of Agentic guide to agent characteristics provides detailed analysis.
Chat AI vs. Copilots vs. Agents: Key Differences
Understanding the spectrum helps you set appropriate expectations.
| Dimension | Chat AI | Copilots | Agentic AI |
|---|---|---|---|
| Autonomy Level | None—responds only when prompted | Limited—suggests but doesn’t execute | High—executes multi-step workflows |
| Human Oversight | 100% (every interaction) | 80-90% (review before action) | 10-30% (key decision points only) |
| Task Complexity | Single-turn Q&A | Assisted completion | Multi-step workflows |
| Response Time | Seconds | Milliseconds to seconds | Minutes to hours |
| Cost per Interaction | $0.001-0.01 | $0.01-0.10 | $0.10-1.00+ |
| Risk Level | Low (information only) | Medium (human reviews) | High (requires governance) |
While generative AI focuses on creating content such as text, images, or code, agentic AI focuses on action. Adding task specialization capabilities evolves AI assistants into AI agents with the capacity to operate and perform complex, end-to-end tasks.
Real-World Examples
What does agentic AI look like in practice?
Agentic Example: Invoice Processing. When an invoice exceeds $50K or has mismatched PO numbers, an agentic system automatically flags it, updates the status to “Review Required,” adds a comment explaining the anomaly, and sends a Slack message to the appropriate approver based on department and amount thresholds. No human initiated these steps—the agent made decisions and executed actions autonomously based on policy and context.
Agentic Example: Travel Booking. An employee submits a trip request: “Book me a flight to San Francisco next Monday, staying until Thursday.” The agent searches flights, books the cheapest option under $500 per company policy, reserves a hotel near the office, creates an expense report pre-filled with trip details, updates the employee’s calendar, and sends a confirmation email with the complete itinerary—all without human intervention.
Not Agentic: Code Completion. A developer uses an AI-powered code editor that predicts what they’ll type next. The AI suggests function completions, but the developer must explicitly accept each suggestion. This is a copilot pattern—sophisticated assistance, but no autonomous execution. The human remains in the loop for every action.
Why This Matters for Enterprise Leaders
The shift to agentic AI has significant implications that go beyond technology decisions.
Higher stakes. When agents take action autonomously, mistakes have real consequences. A chatbot that gives wrong information is annoying; an agent that executes wrong actions can cost money, damage relationships, or create compliance issues. Deloitte’s 2025 study found that while 30% of organizations are exploring agentic options and 38% are piloting solutions, only 14% have solutions ready to deploy and just 11% are actively using agents in production. The gap reflects how seriously enterprises are taking the governance requirements.
New governance requirements. You need visibility into what agents are doing, controls to prevent unauthorized actions, and the ability to audit decisions after the fact. Traditional IT governance wasn’t designed for autonomous systems. Gartner predicts that guardian agents—specialized agents focused on governance and oversight—will capture 10-15% of the agentic AI market by 2030. For a comprehensive framework, see our AI governance checklist for CISOs.
Different ROI model. Agents cost more per interaction but can deliver dramatically higher value by completing end-to-end workflows. The economics shift from “cost per query” to “value per outcome.” In a best-case scenario, Gartner projects agentic AI could generate nearly 30% of enterprise application software revenue by 2035—surpassing $450 billion. For a framework on measuring this value, see our AI ROI measurement guide.
Workforce implications. Agents won’t replace humans wholesale, but they will change what humans do. Many roles will shift from execution to oversight and exception handling. By 2028, Gartner predicts 33% of enterprise software applications will include agentic AI, enabling 15% of day-to-day work decisions to be made autonomously. Organizations need to prepare their workforce for this shift.
The Multi-Agent Future
Just as monolithic applications gave way to distributed service architectures, single all-purpose agents are being replaced by orchestrated teams of specialized agents. Gartner reported a staggering 1,445% surge in multi-agent system inquiries from Q1 2024 to Q2 2025. By 2028, Gartner predicts 70% of AI applications will use multi-agent systems.
This evolution means enterprise AI will increasingly involve ecosystems of specialized agents working together—finance agents, HR agents, security agents, customer service agents—coordinating to complete complex workflows that span organizational boundaries.
Getting Started with Agentic AI
If you’re considering agentic AI for your enterprise, start with low-risk, high-volume use cases. Lead qualification, invoice processing, and IT ticket routing are common starting points where autonomous action delivers clear value with manageable risk. 50% of enterprises using generative AI are expected to deploy autonomous AI agents by 2027, doubling from 25% in 2025.
Build governance from day one. Don’t wait until you have a dozen agents to think about visibility, controls, and measurement. Establishing governance foundations early prevents painful retrofitting later. Our AI risk heatmap framework helps you match governance intensity to risk level.
Measure what matters. Track not just agent activity but business outcomes: time saved, error rates, cost per transaction, and ROI. Without measurement, you can’t prove value or identify problems before they become crises.
Plan for scale. Pilot projects often succeed; scaling is where most enterprises struggle. Consider how your infrastructure, governance, and change management will handle 10x the agents before you need to find out.
The Bottom Line
Agentic AI represents a fundamental shift from AI that informs to AI that acts. For enterprise leaders, this means new opportunities for automation and efficiency—but also new requirements for governance, measurement, and oversight.
The enterprises that thrive will be those who embrace agentic AI while building the guardrails to use it responsibly. That means investing not just in the agents themselves, but in the infrastructure to measure their impact, govern their behavior, and ensure they’re delivering real business value.
Ready to implement agentic AI with confidence? Schedule a demo to see how Olakai helps enterprises measure ROI, govern risk, and scale AI agents responsibly.