What are AI agents?

“Agent” is an overused word, but it points at a real change in how people use AI at work.

Chat versus agent

In a chat, you send a message, get a reply, and any action is up to you. If you ask, “How should I clean this spreadsheet?”, it explains. With an agent, you say, “Clean this spreadsheet,” and it opens the file, applies the changes, checks for problems and tells you what it did.

Anthropic’s engineering team defines it this way: agents are systems where LLMs dynamically direct their own processes and tool usage, maintaining control over how they accomplish tasks. The difference is the loop: the agent looks at the situation, chooses an action, does it, looks at the result, and continues until the goal is met or it needs your input.

What makes something an agent

Three ingredients:

1. A goal you state in ordinary language.
2. Tools it can use: read and write files, run programs, search the web, connect to apps. Standards like MCP make it easier to connect them.
3. Autonomy within limits: it decides intermediate steps, and you decide what it may touch and when it must ask.

Examples you can look up

The Anthropic Claude Code docs describe Claude Code as an agentic coding tool that reads your codebase, edits files, runs commands, and integrates with your development tools. OpenAI describes Codex CLI as a way to inspect code, make changes, run commands, and automate repeatable work. The OpenClaw project describes OpenClaw as an open-source AI assistant that runs on your own computer, reachable through messaging apps. See Claude Code, Codex and OpenClaw.

How reliable are agents today

The Stanford AI Index reports that AI agents went from 12% to about 66% task success on OSWorld, a benchmark of real computer tasks across operating systems. The Stanford AI Index adds that agents still fail roughly 1 in 3 attempts on structured benchmarks. That is why the review step in the pattern below matters. Anthropic says the autonomous nature of agents means higher costs and the potential for compounding errors. For agents, Anthropic recommends extensive testing in sandboxed environments, along with the appropriate guardrails.

Office tasks agents fit

• Assemble a weekly report from several source files.
• Triage an exported inbox, draft replies, and flag action items.
• Pull figures from a spreadsheet, check them against a rule set, and write a summary.
• Monitor a topic and produce a brief on a schedule.

The pattern in Learn AI Fast’s workflow design hour is: trigger, AI processing, human review, output. Including a review step is what makes an agent safe to rely on.

The risks are different from chat

Because agents act, mistakes are more than bad text:

• Wrong action: deleting or overwriting the wrong file.
• Compounding errors: a mistake in step two carries into steps three to ten.
• Manipulation: if an agent reads content containing hidden instructions, it can be steered. The OWASP LLM01:2025 Prompt Injection entry says these inputs can affect the model even if they are imperceptible to humans.
• Overreach: it may have more access than the task needs.

How to use agents responsibly

1. Start on copies, in a dedicated folder.
2. Give the minimum access needed.
3. Require approval before anything irreversible or external, such as sending email.
4. Spot-check results against known numbers.
5. Log what it did, so you can retrace.

Also read how to stop AI hallucinations, since agents can act on wrong information.

Should you care now?

If your work has repetitive, multi-step tasks on documents and data, yes. If you only need occasional writing help, a chat assistant is enough.

Sources

• Anthropic, Building effective agents: “systems where LLMs dynamically direct their own processes and tool usage”
• Anthropic, Claude Code overview: “Claude Code is an agentic coding tool that reads your codebase, edits files, runs commands, and integrates with your development tools”
• OpenAI, Codex CLI: “Inspect code, make changes, run commands, and automate repeatable work”
• GitHub, openclaw/openclaw README: “OpenClaw is an open-source AI assistant that runs on your own computer”
• Stanford HAI, 2026 AI Index Report: “AI agents made a leap from 12% to ~66% task success on OSWorld”
• Stanford HAI, 2026 AI Index Report: “they still fail roughly 1 in 3 attempts on structured benchmarks”
• Anthropic, Building effective agents: “the potential for compounding errors”
• Anthropic, Building effective agents: “extensive testing in sandboxed environments, along with the appropriate guardrails”
• OWASP, LLM01:2025 Prompt Injection: “These inputs can affect the model even if they are imperceptible to humans”