What is agentic AI? A guide to how it works and how to use it

Key takeaways

• Gartner predicts 40% of enterprise applications will feature task-specific AI agents by the end of 2026, up from under 5% in 2025.¹

• Agentic AI is software that pursues a goal on its own, planning the steps and calling the tools it needs with limited human supervision.

• Airwallex AgentOS provides developers the tools to connect agents to an Airwallex Business Account. This setup lets an agent query balances or provision a card while the business controls authentication and permissions.

Enterprise software is shifting from static automation to autonomous execution. Agentic AI works against a high-level goal and reaches for the tools it needs instead of waiting for a prompt at every step. For finance leaders, that means automating repetitive digital tasks, as long as the data underneath is clean and the guardrails hold.

What is agentic AI?

Agentic AI is software that plans, acts, and adjusts on its own to finish a goal, calling whatever tools it needs along the way. Generative AI reacts to a prompt and produces content. In contrast, agentic AI reasons through complex tasks, extracts data, calls APIs, and resolves errors independently.

Consider billing disputes. An agentic system logs into the billing platform, verifies the tax rate, processes the correction, and confirms the fix with the customer. Nobody clicks through each step.

How IBM, Google Cloud, AWS, and Salesforce each define agentic AI

The four major vendors agree on autonomy but vary in focus. Salesforce and IBM emphasize independence. Google Cloud emphasizes the underlying language model. AWS emphasizes control. Salesforce defines agentic AI as autonomous systems that execute multi-step reasoning to achieve business goals with minimal supervision.² IBM describes AI agents as systems replicating human decision-making to complete goals with limited oversight.³

Google Cloud calls it a subset of generative AI that uses large language models to orchestrate actions in underlying systems rather than just generate content.⁴ AWS defines it as autonomous systems that follow strict policies, call approved APIs, and keep an audit trail that companies can review afterward.⁵

How agentic AI differs from chatbots and traditional automation

Traditional automation, like Robotic Process Automation (RPA), runs on rigid, pre-programmed rules. It efficiently manages repetitive tasks with structured inputs, but it has no adaptability. A minor interface change breaks the script until developers issue a fix.

Standard chatbots understand language better, but they're still reactive. They wait for a prompt, process it, and generate a response. They can't start a task on their own, pick a tool, or troubleshoot an error.

Agentic AI combines language comprehension with execution. It reads its environment and decides the best path to the goal. If an API call fails mid-task, the agent works out another plan and keeps going.

Generative AI vs agentic AI vs rule-based automation

The following table compares the three automation models across key enterprise criteria:

The final row highlights a key advantage for finance teams. Agentic AI allows teams to establish upfront permissions and boundaries instead of reviewing individual operations.

The four core characteristics of agentic AI

Four operational traits enable an agentic system to handle open-ended situations that would break a script.

1. Goal-oriented, not step-by-step

Traditional code requires explicit step-by-step programming. In contrast, agentic AI pursues high-level goals. When tasked with a weekly competitor pricing audit, the agent determines the necessary subtasks independently. It identifies target sites, extracts relevant data, and formats the final report.

2. Autonomous decision-making

Once assigned a goal, an agentic system executes tasks continuously without requiring step-by-step approvals. It evaluates options against operational risks and selects efficient paths forward.

3. Tool use (APIs, web, software)

Agentic AI orchestrates the tools already in a company's stack, connecting to databases, searching the web, and calling APIs. It enters data, moves through software interfaces, and reads files, much like an employee would.

4. Adaptability and reflection

Agentic systems evaluate their own performance and refine operations dynamically. When an action fails, the agent investigates the cause and attempts an alternative strategy.

How agentic AI works

An agentic workflow operates as a continuous loop. The system analyzes feedback, updates its state, and coordinates external tools to achieve the assigned goal.

1. Perceive

The agent starts by reading the assigned goal and scanning its environment for context. It might review an email thread, an internal database, an uploaded document, or a server status as required.

2. Reason

The agent then uses a large language model as its primary reasoning engine. It identifies missing information and breaks the goal into steps, checking its own permissions along the way to keep the plan inside policy.

3. Act

The agent executes required operations by calling external tools, running database queries, or writing and running code. For complex work, a supervising agent might spin up specialized sub-agents to run in parallel.

4. Reflect

After each action, the system checks the result against the goal, flags errors, and gathers feedback to sharpen the next move. If the goal is met, the workflow ends. If not, it loops back and adjusts.

Agentic AI examples

Companies are moving agentic AI out of pilots and into production across a handful of core functions.

Customer service

Basic chatbots fail when requests stray from a template. In contrast, an agentic customer service system checks shipment status, calculates refund eligibility against policy, issues the refund, and confirms it with the customer. No human touches the case.

IT ticket resolution

When a user reports a bug, an agentic helpdesk assistant reproduces issues in a sandbox and analyzes system logs. The assistant then writes a patch, runs tests, and opens a merge request for a developer to review.

Financial data analysis

Controllers and FP&A teams use agentic AI in finance to automate repetitive parts of analysis and close the books faster. That means logging into accounting platforms, parsing bank statements, reconciling transactions against invoices, and building rolling, driver-based forecasts.

Travel booking

When tasked with booking employee travel, an AI travel agent evaluates flight options against corporate policies and calendar availability. It completes bookings, reserves suitable hotel accommodations, and syncs itinerary details directly to employee calendars.

Procurement

In agentic commerce, procurement systems watch inventory levels on their own. When stock drops below a threshold, the agent analyzes supplier rates, drafts a purchase order, negotiates pricing within approved limits, and queues a payout through the business account.

Benefits of agentic AI for business

For enterprises with high volumes of repetitive back-office work, benefits extend beyond simple cost-cutting. Agents operate continuously, allowing jobs that used to take days, like multi-entity bank reconciliations or supply chain risk assessments, to be completed in minutes.

Agentic AI also expands automation capabilities across business units. When workflows are configured in plain language, finance and operations managers can deploy and adjust processes without booking developer time for every change.

Building reusable agents and components, instead of a one-off agent per task, eliminates 30% to 50% of the nonessential work companies would otherwise repeat across workflows, according to a McKinsey report.⁶ Efficiency gains depend heavily on component reuse across successive implementations.

Risks of agentic AI for business

Autonomous systems create security and compliance exposure that traditional IT frameworks weren't built for. When software can start a transaction or edit a record on its own, single logic errors create immediate operational risks.

Prompt injection and unsanctioned actions

Because agentic systems process natural language, prompt injection poses a significant risk. Malicious actors can embed instructions within invoices or customer chats. Without proper data sanitization, agents may bypass safety protocols, expose sensitive records, or execute unauthorized transactions.

The need for human-in-the-loop checkpoints on high-stakes tasks

Unrestricted autonomy risks costly operational errors. Organizations must maintain strict human-in-the-loop checkpoints for high-stakes tasks, including fund transfers, contract modifications, or code deployments. In these scenarios, agents draft actions and wait for explicit human approval.

How to use agentic AI in your business

Maximizing value from agentic AI requires workflow re-engineering rather than software acquisition alone. Teams treating agents as simple plug-and-play upgrades often fail to achieve target outcomes.

Start with a narrow, well-defined workflow

Organizations often fail by applying agents to vague, overly broad problems. Implementations should target narrow, high-volume workflows with defined boundaries, such as invoice matching or client onboarding. Teams must redesign operational processes around agent capabilities rather than imposing automation onto flawed manual workflows.

Set guardrails before granting write access to systems

Set the security boundaries before an agent can write to a database or execute a transaction. Identity-based access controls should limit it to the specific APIs and data fields the task needs, keeping any model failure contained.

Measure task success rate, not just output quality

Traditional software gets measured on uptime. Agentic AI requires performance evaluation based on end-to-end task completion rates without human intervention, supplemented by F1 scores and calibration metrics. Agents generating output without completing transactions fail to deliver business value.

How Airwallex supports agentic, machine-initiated finance

Autonomous workflows significantly impact treasury and spend management because legacy financial systems rely on traditional business hours and batch processing. Airwallex embeds intelligence directly into payments, treasury, and spend infrastructure. This design allows agents to operate on the same core architecture as human users.

For a fuller picture of how Airwallex approaches agentic finance, see the Airwallex agentic finance guide.

Programmatic APIs for agent-triggered payments and payouts

Through developer tools like Airwallex AgentOS and Model Context Protocol (MCP) servers, Airwallex provides development teams with secure connectors for financial infrastructure.⁷ Agents check live balances, construct beneficiary profiles, and initiate agentic payments across global networks. By default, outgoing transactions require human confirmation.

Multi-currency accounts built for automated, cross-border transactions

An Airwallex Business Account holds and receives funds in 20+ currencies, allowing agents to collect, hold, and pay out in the same currency without a forced conversion. That matters in autonomous procurement and commerce workflows, where FX costs stack up fast and skipping the markup protects margin on every transaction.

Policy controls at the point of spend

Airwallex integrates policy controls into the payment infrastructure itself. The Airwallex Expense Policy Agent enforces expense policy across entities, currencies, and languages, evaluating expenses at the point of spend rather than at month-end close.

Frequently asked questions about agentic AI

What is the difference between generative AI and agentic AI?

Generative AI operates reactively, whereas agentic AI operates proactively. Generative AI creates text, code, or images following explicit user prompts. Agentic AI pursues high-level goals autonomously by orchestrating tools, making operational decisions, and executing multi-step tasks.

Is ChatGPT or Claude “agentic?”

ChatGPT and Claude aren't agentic by default. A standard web session with either is mostly generative, acting as a reactive assistant. However, connecting these models to external APIs, databases, and execution scripts transforms them into reasoning engines for fully agentic workflows.

What's the difference between an “AI agent” and “agentic AI?”

The difference between an AI agent and agentic AI is scope. An AI agent is a specific, task-oriented piece of software built to operate inside a system. Agentic AI describes the broader category: the traits like autonomy, planning, and adaptability that define how these systems behave.

How is agentic AI different from RPA?

The difference between agentic AI and RPA is adaptability. RPA follows deterministic rules, executing fixed sequences and failing when interface elements or data formats change. Agentic AI uses reasoning engines to process unstructured data, navigate obstacles, and adapt execution plans dynamically.

Do you need engineering resources to adopt agentic AI, or can business teams use it as-is?

Business teams can use basic conversational agents as-is through out-of-the-box SaaS tools, but production-grade workflows still need engineering resources: developers to connect agents to internal systems securely, build custom APIs, set up data storage, and monitor decision-making with AgentOps tooling. The Airwallex guide to building agentic finance and commerce walks through what that setup involves.

Does agentic AI replace jobs?

Agentic AI doesn't replace jobs entirely. It works more like a digital colleague that takes on repetitive cognitive tasks, which frees people up for strategy and problem-solving. Human oversight still matters for accuracy, exceptions, judgment, and training the models.

Sources

1. https://www.gartner.com/en/newsroom/press-releases/2025-08-26-gartner-predicts-40-percent-of-enterprise-apps-will-feature-task-specific-ai-agents-by-2026-up-from-less-than-5-percent-in-2025 

2. https://www.salesforce.com/agentforce/what-is-agentic-ai/ 

3. https://www.ibm.com/think/topics/agentic-ai 

4. https://cloud.google.com/discover/what-is-agentic-ai 

5. https://aws.amazon.com/what-is/agentic-ai/ 

6. https://www.mckinsey.com/capabilities/quantumblack/our-insights/one-year-of-agentic-ai-six-lessons-from-the-people-doing-the-work 

7. https://www.airwallex.com/en-us/blog/devx-ai-guide