AI agent

An AI agent is a software program that perceives its environment, reasons over goals, and takes actions on its own. Unlike a chatbot that only replies, an agent plans, calls tools, and closes the loop — often without a human in the middle.

Agents run on large language models paired with memory, a planner, and a toolbelt of APIs. The LLM decides what to do next. Tools do it. Memory records what happened, so the next step reasons over context, not a blank slate.

For business process outsourcing operations, agents matter because they compress work an offshore team does manually. Ticket triage, invoice matching, KYC checks fold into a background loop. The human moves upstream to exceptions. Volume rises, handle time falls.

Key takeaways

• An AI agent perceives, reasons, and acts on its own to complete a defined goal.
• It combines an LLM brain, a planner, memory, and a set of tools.
• Common uses include ticket triage, sales research, invoice matching, and code review.
• Governance matters — NIST’s AI RMF is the standard reference for risk controls.

How it works

An AI agent works in a perceive-plan-act loop. It reads the state of a task, picks the next best action from its toolbelt, executes, then reads the new state and loops again until the goal is met or a stop condition fires.

Tool use is the leap. Without tools, an agent is a fancy chatbot. With tools like CRM writes, database reads, browser clicks, and code execution, it can actually finish work. The quality of the toolbelt sets the ceiling on what any agent can do.

Memory is the second lever. A stateless agent forgets between turns and re-does the same research. Agents with vector-store memory carry context across a session or weeks, closing the gap between a fresh temp every morning and a colleague who remembers.

Observation closes the loop. After every action, the agent reads back what changed: a new email in the inbox, a 200 response from an API, a code test that failed. That observation feeds the next planning cycle. Without it, the agent is flying blind.

In 2025, Deloitte’s Tech Trends report flagged that only 11% of organizations run agents in production, while 38% pilot them, a gap Gartner projects will still see 40% of agentic AI projects cancelled by end of 2027.

Examples

AI agents already run inside workflows the outsourcing industry has priced by the hour for decades: support triage, research, and back-office reconciliation. Named vendors ship them today. The results show up in throughput, not headlines.

Klarna’s OpenAI agent (2024). In its first month live, Klarna’s support agent handled 2.3 million chats. Klarna estimated the workload matched 700 full-time reps, cutting average resolution time from 11 minutes to under two.

Salesforce Agentforce (October 2024). Salesforce launched a full agent platform inside its CRM, with pre-built agents for service, sales, and marketing, priced at $2 per conversation. Early adopters include Wiley, ADP, and OpenTable.

Intercom Fin (2023 onward). Fin is a deflection agent that answers ticket queries from a company’s help center. Intercom reports Fin resolves up to 50% of inbound tickets end-to-end for clients like Anthropic and Retool, freeing tier-1 humans for escalations.

Anthropic’s Computer Use (October 2024). Anthropic gave Claude the ability to move a cursor, click, and type inside a normal desktop, an experimental capability that unlocks agents against legacy apps with no API. Replit and Asana were named launch partners.

Related terms

AI agents don’t live alone in the tech stack. They sit inside — and sometimes replace — a family of older automation and outsourcing terms. Knowing the neighbours helps you scope where an agent belongs and where a cheaper tool wins.

• Robotic process automation (RPA): rule-based bots that click and type through fixed UIs; AI agents reason and decide, RPA doesn’t.
• Virtual assistant: a remote human worker; an AI agent is software that handles some of that same workload.
• Contact center: the multichannel support operation where deflection agents most often deploy first.
• Standard operating procedure (SOP): the written playbook an agent’s planner encodes into machine-executable steps.
• Quality assurance: the human-review layer that samples agent output before scale, then continuously after.

FAQ

AI agents attract a lot of noise, and a lot of questions. The ones below cover what buyers ask most on discovery calls: how agents differ from chatbots, when they save money, and what a human still has to do.

What’s the difference between an AI agent and a chatbot?

A chatbot answers one turn at a time. You ask, it replies, done. An agent runs a loop: it can plan multi-step work, call APIs, wait for results, and retry.

How much does an AI agent cost to run?

Pricing varies by vendor and task. Salesforce Agentforce launched at $2 per conversation in late 2024. Building your own agent runs mostly on LLM token spend, often cents per completed task, but heavy planning loops can push that into dollars.

Will AI agents replace BPO staff?

Not wholesale. Agents shift the skill mix: tier-1 customer service volume drops, but exception handling, quality assurance, and prompt engineering all grow. Klarna has shown the top-line effect; the workforce still exists, just aimed at higher-value work.

What governance framework applies to AI agents?

NIST’s AI Risk Management Framework (AI RMF), released in January 2023 and expanded with a Generative AI Profile in July 2024, is the reference most enterprises anchor to. It covers governance, mapping, measurement, and management of AI risk.

For a buyer’s-eye view of how AI agents plug into an outsourcing plan, browse the resources at Outsource Accelerator.