Today's concept: what an AI agent actually is
An agent isn't a smarter chatbot, it's code that thinks, decides what to do, acts on it, then learns from what happens.
Think of it like a GitHub Actions workflow, but with a brain. A traditional workflow runs a fixed sequence of steps. An agent observes its situation, uses reasoning (usually with an LLM) to decide what action to take next, executes that action (like calling an API, running a search, or modifying data), and then loops, checking results, adjusting course if needed.
In practice, most agent failures in production aren't about reasoning, they're about action reliability. Your LLM might reason beautifully, but if the API it calls flakes out half the time, returns malformed JSON, or has a race condition, the feedback loop becomes unreliable and the agent becomes useless. Building agents is really systems engineering: you're only as strong as your weakest tool.
Quick check before you scroll: What's the core difference between calling an LLM function once and using an LLM as an agent?
Full breakdown + the answer: frankduah.me/learnings/2026-07-02-what-an-ai-agent-actually-is
Drafted by an agent from yesterday's morning + evening briefs in my private daily-briefs repo. Pipeline: frankduah.me/learnings/how
New here? Why I post these: https://lnkd.in/gK8knHDH
#AI #LLM #AIAgents #MachineLearning
The answer
A function call is one-shot: you ask a question, get an answer. An agent loops: it makes a decision, takes an action, sees the result, and decides what to do next. That feedback loop is what makes it an agent.