What is an AI agent? How it works, key components, and examples
Blog post from Exa
AI agents are autonomous systems, often powered by large language models, that pursue goals by perceiving inputs, planning multi-step tasks, using tools, evaluating results, and iterating until completion or escalation. Their core capabilities include autonomy, reasoning and planning, tool use, and short- and long-term memory, distinguishing them from chatbots that respond turn by turn and workflows that follow developer-defined paths. Agents can use web search, code execution, file access, and business APIs to access current information and take actions such as querying databases, editing documents, or processing refunds. Production implementations range from code frameworks such as LangGraph to low-code platforms like n8n and prebuilt marketplace offerings, with examples including Intercom’s customer service agent, Uber’s finance data agent, and Anthropic’s Claude Code. The discussion emphasizes that safe production deployment requires limited permissions, step limits, tool-call logging, and human approval for consequential or irreversible actions.
| Trend | Post Mentions | Total Month Mentions | Posts | Companies | MoM |
|---|---|---|---|---|---|
| AI Agents | 20 | 6,890 | 1,628 | 266 | +23% |
| LLM | 8 | 6,496 | 1,223 | 229 | +8% |
| Multi-agent systems | 2 | 325 | 118 | 62 | -30% |
| Exa Connect | 1 | 35 | 34 | 1 | +338% |
| RAG | 1 | 872 | 222 | 82 | +1% |
| Reinforcement learning | 1 | 139 | 79 | 35 | -14% |
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