By Sahej Bahl (@Sahej.eng on instagram/tiktok), if you find value in this guide, please drop a follow for more similar content and guides!

What (exactly) is an AI agent?

An AI agent is a system that uses an LLM to decide and act toward a goal, usually by:

• Reasoning (planning & reflection),
• Using tools (APIs, web, files, a browser, a code runner),
• Keeping state (memory, scratchpad, conversation history),
• Interacting with environments (the web, apps, your data), often repeatedly.

Foundational patterns you’ll see everywhere:

• ReAct: interleave reasoning and acting; think → tool → observe → think… Dramatically reduces hallucinations vs pure CoT when tools are involved. arXiv+1
• Reflexion / self-reflection: after each attempt, the agent “talks to itself,” stores lessons, and tries again better. arXiv+1
• Graph/Workflow agents: you wire nodes (LLM/tool steps) as a stateful graph for control, retries, and observability (e.g., LangGraph). LangChain Academy

When to use agents (and when not)

Great for

• Long, multi-step workflows (research → plan → execute → summarize), web automations, ops runbooks, data scraping/cleaning, customer ops, QA triage, code maintenance.
• Tasks needing tool use (search, DB queries, spreadsheets, browsers) and “try-observe-retry” loops.

Not ideal

• One-shot Q&A on known data (use plain RAG).
• High-stakes actions with low tolerance for error unless you insert strong guardrails/human-review.