What actually makes an AI "agentic"? The word is everywhere now - in product pages, funding announcements, LinkedIn posts from people who've never shipped anything. But the underlying concept is worth understanding because it directly affects which tools you should use and how to configure them.
An AI agent, stripped of the hype, is just a language model that doesn't stop at answering a question. Instead of giving you one response and waiting, an agent takes an action, observes the result, decides on the next action, and keeps going until the task is done or it gets stuck. That loop is the core idea. The patterns that sit inside that loop are worth knowing.
ReAct: Think, Then Act
The most foundational agentic pattern is called ReAct - short for Reasoning + Acting. The model writes out its reasoning ("I need to find today's stock price for Apple"), then takes an action (calling a search tool), reads the result, reasons again, and repeats. This explicit reasoning-before-acting approach reduces errors compared to models that just dive into tool calls without thinking through the plan first. Most production AI agents - whether in Claude, ChatGPT, or tools like Cursor - use some version of this internally.
Tool use is what gives agents access to the real world: web search, code execution, database queries, sending emails, browsing URLs. When you see an AI assistant browse a website or run Python code, that's function calling. The model decides which tool to invoke, generates the parameters, and gets back a result it can act on. The quality of the tool set determines what an agent can actually accomplish - a powerful model with weak tools is still a weak agent.
RAG and Reflection
Retrieval Augmented Generation (RAG) means fetching relevant information from a knowledge base and feeding it to the model before it generates a response. Think of it as giving the AI a cheat sheet pulled from your own documents rather than relying on what it memorized during training. Most enterprise AI products - chatbots trained on internal wikis, customer support tools, document Q&A systems - are RAG systems under the hood. The retrieval quality matters as much as the model quality.
Some agents include a reflection step where the model reviews its own output before returning it. It generates a draft answer, then critiques that draft, then revises. This pattern reduces obvious errors but adds latency and cost. For high-stakes tasks where accuracy matters more than speed, the tradeoff is usually worth it.
Multi-Agent: Where Things Get Complicated
The most ambitious pattern is multiple AI agents working in parallel or sequence, each handling a specialized part of a larger task. One agent researches, another writes, a third edits. Tools like Claude Code and some advanced workflow builders are moving in this direction. The appeal is real - specialized agents can outperform generalist ones on complex work.
The failure modes are more complicated, though. Multi-agent systems can get into coordination loops, pass errors down the chain, or produce inconsistent outputs when agents contradict each other. Understanding why a single-agent system failed is hard enough. Debugging a six-agent pipeline is a different category of problem.
None of this requires becoming an ML engineer. But understanding which patterns a tool uses - and where those patterns tend to break down - is how you avoid being surprised when an AI agent does something unexpected at 2am on a deadline.