What are AI agents?

Question

What are AI agents?

Accepted Answer

**AI agents** are systems that use LLMs to **autonomously perform tasks** — planning, using tools, and taking actions to achieve goals, rather than just responding to single prompts. They represent an evolution toward more capable, autonomous AI applications.

## What AI agents are

```text
AI AGENT → an LLM-powered system that autonomously works toward a GOAL:
  → REASONS/plans → breaks a goal into steps
  → uses TOOLS → calls functions, APIs, searches, runs code, accesses data
  → takes ACTIONS and observes results → iterates (a loop of think → act → observe)
  → works toward the goal with some AUTONOMY (multi-step, not a single response)
→ beyond single Q&A → an LLM that can DO things over multiple steps
```

## How agents work

```text
→ the LLM is the 'brain' → decides what to do next based on the goal and observations
→ TOOLS extend its abilities → web search, code execution, APIs, databases, file access
→ a LOOP → the agent plans, acts (uses a tool), observes the result, and continues until
  the goal is met
→ patterns: ReAct (reason+act), tool-calling/function-calling, planning, memory
```

## Capabilities, uses, and challenges

```text
✓ USES → complex multi-step tasks, research, coding agents, automation, assistants that
  take actions, workflow automation
⚠️ CHALLENGES → reliability (errors compound over steps), cost (many LLM calls), can go
  wrong/loop, safety (autonomous actions need guardrails), hard to control/verify
→ a promising but evolving area → powerful but needs careful design, guardrails, oversight
```

## Why it matters

Understanding AI agents is valuable senior-level knowledge because they represent **an important, rapidly-evolving direction in AI applications** (more autonomous, capable AI), so it's increasingly relevant AI knowledge.

AI agents — LLM-powered systems that autonomously perform tasks by planning, using tools, and taking actions toward goals — represent an evolution beyond single-prompt responses toward more capable AI.

Understanding **what AI agents are** — systems that reason and plan (breaking goals into steps), use tools (calling APIs, searching, running code), and take actions while observing results in a loop, working toward goals with autonomy (multi-step, not single responses) — clarifies this important evolution (LLMs that can *do* things, not just respond).

Understanding **how agents work** — the LLM as the brain deciding next steps, tools extending its abilities (web search, code execution, databases), and a loop of plan-act-observe-continue, with patterns like ReAct and function-calling — provides the conceptual model of agent architecture.

Understanding the **capabilities, uses, and challenges** — uses (complex multi-step tasks, research, coding agents, automation, action-taking assistants) and especially the **challenges** (reliability with errors compounding over steps, cost from many LLM calls, going wrong or looping, safety needing guardrails for autonomous actions, and difficulty controlling and verifying) — reflects a balanced understanding of agents as promising but challenging.

The recognition that agents are powerful but need careful design, guardrails, and oversight reflects mature understanding.

AI agents are a rapidly-evolving, increasingly-important direction in AI (toward autonomous, capable applications), making understanding them increasingly relevant for developers and architects building advanced AI systems.

Since AI agents represent an important, rapidly-evolving direction in AI applications (autonomous, tool-using, multi-step systems) with significant capabilities and challenges, and since understanding them is increasingly relevant as AI applications evolve toward agents, understanding AI agents is valuable, increasingly-relevant senior-level AI knowledge — an important, evolving direction in AI (toward autonomous, tool-using, goal-pursuing systems), with key capabilities and challenges (reliability, cost, safety), increasingly relevant as AI applications advance, and important for understanding the direction of capable AI applications.