What Are AI Agents? A Plain-English Guide for Business Leaders

AI agents are software systems that complete tasks autonomously. Unlike chatbots that wait for your next prompt, AI agents pursue goals. You give them an objective, and they figure out how to achieve it, taking multiple steps, making decisions along the way, and adapting when circumstances change.

The difference matters for business. Chatbots answer questions. AI agents do work.

Deloitte predicts 25% of companies using generative AI will launch agentic AI pilots in 2025, growing to 50% by 2027¹. The World Economic Forum projects that 42% of business tasks will be automated by 2027, with 75% of companies adopting AI technologies². AI agents are the mechanism through which this automation happens.

This guide explains what AI agents are, how they differ from the AI tools you already use, and where businesses are deploying them today.

Table of Contents

• What Makes AI Agents Different
• AI Agents vs Chatbots: The Key Distinction
• Five Capabilities of Agentic AI
• Where Businesses Deploy AI Agents Today
  • Accounting and Finance
  • Software Development
  • Customer Service
  • Legal
  • Research and Analysis
• The Maturity Spectrum
• Getting Started With AI Agents
• Common Questions About AI Agents

What Makes AI Agents Different

Traditional software does exactly what you tell it. You click a button, it performs an action. You enter data, it stores the data. The software never acts without explicit instruction.

Chatbots like ChatGPT represent a step forward. They understand natural language and generate responses. But they still operate in a prompt-response loop. You ask, they answer. You prompt, they generate. Without your next input, they wait.

AI agents break this pattern. They operate autonomously, pursuing goals across multiple steps without constant human direction. When Box CEO Aaron Levie describes his company’s AI strategy, he talks about agents that “can do everything from start to finish, so you don’t have to do it manually.”³

The practical distinction: you tell a chatbot “summarize this document.” You tell an AI agent “prepare this client’s tax documents for filing.” The chatbot produces a summary and stops. The agent identifies what documents exist, extracts relevant information from each, categorizes the data, flags missing items, and continues working until the objective is complete or it encounters something requiring human judgment.

AI Agents vs Chatbots: The Key Distinction

Microsoft defines agentic AI as “an autonomous AI system that plans, reasons and acts to complete tasks with minimal human oversight.”⁴ Current AI systems like ChatGPT require human prompting to complete assignments. Agentic AI operates more independently.

The distinction shows up in workflow:

Chatbot Workflow:

1. Human prompts: “What deductions can I claim?”
2. Bot responds with list
3. Human prompts: “How do I calculate mileage?”
4. Bot explains calculation
5. Human does the work

AI Agent Workflow:

1. Human assigns: “Process this month’s expenses”
2. Agent reviews receipts
3. Agent categorizes each expense
4. Agent flags items needing clarification
5. Agent outputs organized expense report
6. Human reviews and approves

The agent handles the execution. The human handles the judgment calls and final approval.

This matters because most business work involves sequences of tasks, not isolated questions. Processing invoices means extracting data, matching to purchase orders, coding to accounts, routing for approval, and updating records. Each step follows from the previous. AI agents can execute these sequences, where chatbots can only assist with individual steps.

Five Capabilities of Agentic AI

According to research from the University of Cincinnati, five characteristics define agentic AI systems⁵:

1. Autonomy

AI agents perform tasks beyond exactly what you assign. They require significantly less human oversight than traditional automation. When processing a bank statement, an agent does not stop at extraction. It categorizes transactions, identifies patterns, flags anomalies, and prepares the data for the next step in your workflow.

2. Reasoning

Through contextual clues and sophisticated decision making, AI agents select potential solutions on their own. When a transaction description is ambiguous, the agent considers historical patterns, client context, and accounting rules to determine the appropriate category. It reasons through the problem rather than following rigid rules.

3. Adaptive Planning

When conditions shift, agentic AI alters its plans accordingly. If a document is missing, the agent does not fail. It identifies what is available, notes what is needed, and proceeds with what it can accomplish while flagging the gap for human follow-up.

4. Context Understanding

AI agents comprehend context with ease. They understand that “Amazon” might mean office supplies for one client and inventory purchases for another. They learn the specific patterns of each situation rather than applying universal rules.

5. Action Capability

AI agents deliver tangible results by acting whenever capable. They do not just recommend. They execute. An agent tasked with processing receivables does not suggest which invoices to send. It generates and sends the invoices.

Where Businesses Deploy AI Agents Today

AI agent adoption is accelerating across specific domains where autonomous task completion delivers measurable value.

Accounting and Finance

The Big Four accounting firms have made massive investments in agentic AI. KPMG is spending $2 billion on cloud and AI, building agents into their Clara audit platform for substantive procedures like expense vouching and liability searches⁶. EY is deploying 150 different agents to 80,000 tax professionals globally⁷. PwC has committed $1 billion to AI capabilities⁸.

For mid-size and small firms, AI agents for accounting handle document processing, transaction categorization, and audit preparation. The most common starting point is bank statement automation, where agents extract transactions from PDFs and categorize them against charts of accounts.

Financial teams spend 30% of operations time re-keying statement data⁹. AI agents eliminate this manual work entirely.

Software Development

By October 2025, software development had emerged as a primary use case for AI agents¹⁰. Agents write code, review pull requests, debug issues, and automate testing. GitHub Copilot and similar tools have evolved from code completion to autonomous development assistance.

Development teams report 30-50% productivity improvements when using AI coding agents¹¹. The agents handle boilerplate code, documentation, and routine debugging while developers focus on architecture and complex logic.

Customer Service

Customer support was among the earliest AI agent deployments. Modern support agents go beyond scripted responses. They understand customer history, access relevant knowledge bases, take actions like processing refunds or updating accounts, and escalate appropriately when human judgment is needed.

80% of companies plan to adopt AI agents for customer service by 2027². The technology handles volume while freeing human agents for complex cases requiring empathy and judgment.

Legal

AI agents in legal handle document review, contract analysis, and research. Levie describes Box’s internal deployment: “We’ve seen legal teams dramatically reduce the amount of time that they have to spend on contracts.”³

Agents identify relevant clauses, flag unusual terms, compare against standard language, and prepare summaries for attorney review. What took paralegals days to review now takes hours.

Research and Analysis

Agents excel at gathering, synthesizing, and organizing information from multiple sources. Research that required a junior analyst spending days reading and summarizing now happens in minutes.

OpenAI’s Deep Research and similar tools represent this category. You define a research question. The agent identifies sources, extracts relevant information, synthesizes findings, and produces a structured report.

The Maturity Spectrum

Not all AI agents are equally capable. KPMG’s framework describes a maturity spectrum from basic to fully autonomous¹²:

Level 1: Assisted

AI handles specific subtasks while humans direct overall workflow. An agent extracts data from documents. A human reviews and categorizes.

Level 2: Semi-Autonomous

AI executes multi-step workflows with human checkpoints. An agent processes a batch of invoices end-to-end, then presents results for human approval.

Level 3: Autonomous

AI completes complex objectives independently, involving humans only for exceptions or high-stakes decisions. An agent manages routine accounts payable, flagging only unusual transactions for review.

Level 4: Fully Autonomous

AI operates independently across extended timeframes, self-correcting and adapting as needed. Few production deployments reach this level today.

Most business applications currently operate at Levels 1-2, with advancement toward Level 3 for well-defined, lower-risk processes.

Getting Started With AI Agents

Successful AI agent adoption follows a pattern:

Start with high-volume, repetitive tasks. Document processing, data entry, and categorization work deliver the fastest ROI. These tasks have clear success criteria and low risk from errors.

Choose processes with defined inputs and outputs. AI agents work best when the goal is clear. “Process these bank statements” works better than “improve our accounting.”

Maintain human oversight initially. Run agents in review mode first, where humans approve outputs before they become final. This builds trust and identifies edge cases.

Measure before and after. Document current time investment, error rates, and bottlenecks. Measure the same metrics after agent deployment. Concrete results justify expansion.

Expand incrementally. Once one process works, apply the same pattern to adjacent processes. Document processing leads to categorization leads to reconciliation.

The firms seeing success treat AI agents as capability builders, not cost cutters. The goal is expanding what your team can accomplish, not shrinking the team.

Common Questions About AI Agents

How are AI agents different from RPA (robotic process automation)?

RPA follows explicit rules: if X, then Y. Agents make decisions based on context and learning. RPA breaks when processes change. Agents adapt. RPA handles structured, predictable workflows. Agents handle variation and judgment.

Do AI agents replace employees?

The evidence suggests agents change roles rather than eliminate them. EY’s deployment of 150 agents aims to “free up employees for more complex work.”⁷ The World Economic Forum projects job creation will exceed job loss through 2027, with AI creating 69 million new positions while eliminating 83 million². Net job loss exists, but the primary effect is task shift, not role elimination.

How accurate are AI agents?

Accuracy depends on the task and training. For document processing with clear formats, modern agents achieve 95-99% accuracy¹³. For judgment-intensive tasks like categorization, accuracy ranges from 85-95% with human review catching exceptions. The practical question is whether agent accuracy exceeds human accuracy, which it frequently does for high-volume repetitive work.

What data do AI agents need?

Agents learn from historical examples. For transaction categorization, they need past transactions with correct categories. For document processing, they need sample documents with expected outputs. More training data generally means better performance, but modern agents can start with hundreds of examples rather than millions.

How long does implementation take?

Simple use cases like document extraction can be running in days. Complex workflows with custom integration may take weeks to months. The pattern is fast initial deployment followed by gradual expansion as the agent learns your specific patterns.

What happens when agents make mistakes?

Good agent systems flag low-confidence decisions for human review. When errors reach production, they become training data. The agent learns from corrections, improving over time. Organizations should expect some errors during initial deployment, with accuracy improving as the agent learns from feedback.

Conto automates bank statement processing, transaction categorization, and document extraction. See how it works with your actual client files.