AI Agents for Business: What They Are, How They Work, and When to Use Them

In 2024, Klarna deployed an AI assistant that handled the equivalent workload of 700 customer service agents — 2.3 million conversations in its first month — cutting resolution time from 11 minutes to under 2. By Q3 2025, the system was saving an estimated $60 million annually. Then, in May 2025, CEO Sebastian Siemiatkowski said the company had cut too deep and began rehiring human agents for complex, premium support cases.

The Klarna story is not a cautionary tale against AI agents. It is the clearest available illustration of what they actually do: they handle high-volume, structured requests at scale and at speed. They struggle — and sometimes fail — on novel, emotionally charged, or highly complex cases where human judgment is not optional.

An AI agent is a software system that receives a goal, reasons about what steps are needed, uses tools and external data, maintains enough context across steps, and acts with controlled autonomy to complete a multi-step job. The key word is acts: unlike a chatbot that answers questions, an agent reads data, makes decisions, executes tasks across connected systems, and hands off to a human when a situation exceeds its guardrails.

Whether your business needs one depends on the workflow, not the technology. This guide explains how to make that call.

What is an AI agent? A business definition

According to McKinsey's State of AI 2025 report, 88% of organizations now use AI in at least one business function — up from 78% the previous year. Only 7% have fully scaled it. The gap between those two numbers is where most businesses currently sit: experimenting, implementing in pockets, but not yet running AI end-to-end across core processes.

AI agents represent the next operational layer. Rather than generating a document or answering a question in isolation, an agent does work: it receives a goal, breaks it into tasks, selects the right tools, executes those tasks across connected systems, monitors results, and adjusts — until the job is complete or until a human needs to decide.

The convergence of definitions from IBM, AWS, McKinsey, Google Cloud, Anthropic, and OpenAI points to five distinguishing characteristics shared by every genuine agentic system:

• Autonomy — the agent selects its own next steps without being told each one explicitly.
• Tool use — it reads and writes data across external systems: CRMs, databases, APIs, documents.
• Memory — it maintains context across multiple steps, not just within a single prompt exchange.
• Multi-step planning — it decomposes a goal into subtasks and sequences them.
• Action — it does not just answer; it changes state in connected systems.

Gartner offers the most useful executive framing: an AI assistant "responds to prompts and depends on human direction at every step." An AI agent "can plan, use tools, reason outcomes, and act toward a goal with minimal oversight." The difference is not cosmetic — it determines what a system can own independently and what still requires a human to decide.

What an AI agent is not: chatbots, RPA, and agentwashing

The agent market has an accuracy problem. Gartner estimates that of the thousands of vendors claiming to offer "AI agents," only around 130 deliver genuinely agentic systems. The rest sell chatbots, assistants, or conventional automations relabeled to capture market attention. Gartner calls this agentwashing — and it matters because deploying the wrong system sets wrong expectations, produces real failures, and wastes real budget.

The useful distinctions:

The practical model for most operations combines both: RPA handles deterministic steps at the base; an agent layer manages exceptions, unstructured inputs, and cross-system coordination above it. They are complements, not competitors.

It is also worth separating agentic AI from an AI agent: agentic AI is the broader paradigm — AI systems that act in the real world and execute multi-step processes. An agent is the individual software component. A single deployment may use multiple agents cooperating within a broader agentic architecture.

How AI agents work: the agent loop and core components

The agent loop

Every AI agent runs the same fundamental cycle, regardless of the framework or platform:

• 01

  Perceive — the agent ingests an input: a user message, API response, document, database result, or trigger event.

• 02

  Reason — the LLM at the core processes the goal, reviews available information, and determines the best next step.

• 03

  Plan — it decomposes the goal into subtasks and selects the tools or actions needed.

• 04

  Act — it calls a tool, executes an API, writes to a system, or sends a communication.

• 05

  Observe — it reads the result of the action and evaluates whether the goal is met.

• 06

  Repeat — it cycles until the task is complete, a stop condition is reached, or a human escalation is needed.

The reasoning step is what makes agents qualitatively different from scripted automation. The most widely adopted approach is ReAct (Reasoning + Acting), developed at Princeton and Google Research in 2022. ReAct interleaves "thought → action → observation" in a continuous loop. In controlled experiments, it outperformed imitation and reinforcement-learning baselines by 34 percentage points on structured task environments and 10 percentage points on e-commerce task completion.

What an agent is made of

Seven components make up a production-grade agent, synthesized from IBM, Anthropic, Google Cloud, and OpenAI documentation:

• 1

  The reasoning model — an LLM (Claude, GPT, Gemini, IBM Granite) that processes inputs and decides what to do next.

• 2

  Perception — the ability to ingest structured and unstructured inputs from multiple sources.

• 3

  Reasoning and planning — task decomposition, self-correction, and recovery during multi-step execution.

• 4

  Memory — short-term (the active context window during a session) and long-term (vector databases such as Mem0 or MemGPT that persist knowledge and state across sessions).

• 5

  Tool use / function calling — APIs, web search, database queries, and code execution defined via JSON schema, which the model calls when it needs external data or needs to act on a system.

• 6

  Action and execution — the ability to write to and trigger downstream systems: CRMs, ERPs, email platforms, calendars, and workflow tools.

• 7

  Feedback loops — iterative refinement based on observed outcomes, including human feedback and automated quality checks.

OpenAI simplifies this to three base elements: model, tools, and instructions. Anthropic adds a fourth: the harness — the guardrails, scope constraints, and operational policies that define what the agent is permitted to do and where it must stop and ask.

The autonomy scale

Gartner observes that most 2026 production deployments sit at the middle of this scale (workflow-with-tool-calling to single agent), while vendor marketing material often implies the far right (fully autonomous multi-agent systems). Understanding where your system actually sits avoids both overpromising internally and underbuilding for real requirements.

Agentic RAG vs. traditional RAG

Most enterprise agents need access to your organization's own knowledge — policies, product data, prior tickets, contracts. Retrieval-augmented generation (RAG) is the technique that connects a language model to that knowledge so it answers from your facts rather than its training data.

Traditional RAG is a single, linear pass: take the query, retrieve matching documents, generate an answer. It works well for direct factual lookups. It struggles when a question requires multiple sources, reasoning across steps, or verifying its own output. Agentic RAG wraps retrieval inside the agent loop — the agent decides what to retrieve, evaluates whether the results are sufficient, retrieves again if not, and synthesizes across sources before answering.

The practical rule: if your use case is a straightforward lookup against a single knowledge base, traditional RAG is cheaper and faster. If answers require pulling from several systems, cross-checking, or multi-step reasoning, agentic RAG is worth the added complexity and cost.

When to use an AI agent

An agent is the right tool when a workflow has high variance, unstructured inputs, and multiple steps across systems — and when the cost of an occasional error is bounded and recoverable. The decision tree below captures the test most teams should run before committing budget.

The strongest agent candidates share a profile: repetitive but not identical, judgment-light but context-heavy, spanning two or more systems, and tolerant of a human checkpoint. Customer support triage, invoice and document processing, IT service requests, research synthesis, and lead qualification all fit this shape.

When not to use one

The honest part of this guide. Agents are the wrong choice more often than the marketing implies. Gartner predicts that over 40% of agentic AI projects will be cancelled before the end of 2027, citing escalating costs, unclear business value, and inadequate risk controls. Avoid being in that statistic by recognizing the anti-patterns early.

• ×

  The task is deterministic. If a rule covers every case, an agent adds cost, latency, and unpredictability for no benefit. Use RPA.

• ×

  Errors are catastrophic and irreversible. Where a single wrong action causes legal, financial, or safety harm that cannot be undone, autonomy is a liability. Keep a human in control.

• ×

  The volume is low. If a task runs a handful of times a month, the build and maintenance cost rarely pays back. Do it manually.

• ×

  The data foundation is not ready. Agents amplify the quality of the systems they connect to. If your data is fragmented, inaccurate, or ungoverned, fix that first — an agent on bad data fails faster and louder.

The Klarna correction belongs here. The system worked at scale, but the company concluded that fully removing humans from complex, premium support degraded the experience for exactly the customers it could least afford to lose. The lesson is calibration, not retreat: deploy agents on the high-volume tier, route the hard cases to people.

Use cases by industry

The clearest returns today cluster in a few high-volume, multi-step domains. Customer service is the most mature, with public, audited results.

Beyond customer service, four other domains show consistent, repeatable returns. The pattern holds: high volume, multi-step, bounded risk, human checkpoint available.

Maturity here is a measure of how reliably the domain delivers at scale today — not a ceiling. Emerging domains are improving quickly; the gap is operational evidence and tooling, not feasibility.

Risks, guardrails & governance

Autonomy is the source of an agent's value and its risk. The same capability that lets it act across systems without supervision is the capability that, unguarded, lets it act wrongly across systems without supervision. Three controls are non-negotiable for any production deployment.

• Human-in-the-loop checkpoints

  Define which actions require human approval before execution — anything with financial, legal, or customer-facing consequence above a set threshold. The agent prepares; a person commits.

• Scoped permissions & the harness

  Grant least-privilege access. The agent can read and write only the systems and records its job requires, with explicit boundaries on what it must never touch. Enforce it at the integration layer, not in the prompt.

• Full observability & audit trails

  Log every decision, tool call, and action with its reasoning. You cannot govern what you cannot see — and regulators increasingly require this record.

The regulatory clock: the EU AI Act

For any organization operating in or serving the EU, the AI Act sets binding, phased obligations. The timeline is already running.

Governance is not a post-deployment add-on. Building observability, scoped permissions, and human checkpoints in from day one is far cheaper than retrofitting them — and it is increasingly a legal requirement, not a best practice.

How to deploy your first AI agent

The most successful first deployments are narrow: one workflow, a defined tool set, a measurable success metric. This is the seven-step sequence Liorant uses with operations teams.

1. Identify a workflow with the right properties

   High-volume, multi-step, two or more connected systems, measurable outcomes, and human attention needed mainly for exceptions — not judgment in every case. Best candidates: service triage, first-pass contract review, invoice matching.

2. Establish your baseline before building anything

   Record current handling time, error rate, cost per transaction, monthly volume, and escalation percentage. These are your ROI anchor — without them you cannot prove value or diagnose failures.

3. Choose the right level of sophistication

   Map the workflow against the autonomy scale. If a well-tuned RAG system and a single tool call will do, don't build a multi-agent architecture. Match the solution to the actual complexity.

4. Build on tools you already license

   On Microsoft 365, Copilot Studio is the fastest path; on Salesforce, Agentforce connects natively to your CRM. Building from the API layer (LangGraph, CrewAI, OpenAI Agents SDK) offers more control at the cost of more engineering.

5. Run a narrow pilot with defined success criteria

   A 2–4 week pilot, one workflow, one tool set, one KPI. Define in advance what "working" looks like, what "failing" looks like, and who reviews output daily during the pilot.

6. Build human checkpoints for risky actions

   Classify every action: safe to fully automate (reads, low-value writes), requires human confirmation (payments, mass comms, contract changes), or out of scope entirely (irreversible actions with no recovery path).

7. Measure, iterate, and decide on scale

   If the pilot delivers on its KPI, scale to 100% of the target workflow within roughly 90 days. If not, diagnose the root cause — data, integration, model behavior, or workflow design — and fix it before expanding.

Frequently asked questions