AI Agents in Practice

AI Agents in practice are systems that combine an AI model with planning, tool use, memory, and goal-directed autonomy (the ability to work toward a target without step-by-step human guidance). Unlike a simple chat answer—like a cook following one order—an agentic system (a setup where AI acts independently) is more like an operator managing the whole job: it perceives data from applications, plans actions, uses tools, and self-corrects.

Why this matters for AI development is that the skill is no longer just coding. The key ability is designing what agents should actually do, where they should be proactive, and how to map out processes. If you are good at managing people, you are likely to become good at managing agents, because communicating with them will feel like communicating with a human, except they work 24/7. Agents work asynchronously (not requiring real-time human input), so you can set a task and let it run.

A critical practical distinction is knowing when something needs an AI agent versus a simple workflow. Think of a vending machine (deterministic—same input always gives same output) versus a slot machine (non-deterministic—results vary). For deterministic tasks, you do not need an agent. For tasks that require planning and adaptation, you do.

To build effectively, you should write down standard operating procedures (SOPs—step-by-step instructions) and give agents access to those building blocks. This shortens the feedback loop (time between action and result) and lets you iterate faster. The four core traits of a true AI agent are planning, tool use, autonomy, and self-correction. Master those distinctions, and you become the person at your company most effective with AI agents.

To use an AI agent in practice, start by defining your outcome. Unlike traditional automation where you script every step, an agentic system lets you simply say, "Here's the outcome I want," and the AI figures out the path. For example, in Claude Code (a coding agent that runs in your terminal), you give a high-level instruction like "research YC companies building AI agents," and it handles the execution.

Building an agent follows three steps. Step one is instructions — how you tell the AI what to do. Create a file like `Claude.md` or `agents.md` in your project folder. This contains system instructions (the rules the agent follows) for high-level guidance. Step two is tools — give the agent access to your codebase, files, or APIs. Step three is teach — you refine the agent by feeding it examples of what you want.

A practical example: you want an agent that handles customer research. Use a two-agent setup to save money. One cheap agent scans lists; one smarter agent analyzes results. You literally paste your goal into Claude Code, ask it how to proceed, and it builds the workflow. If you struggle, ask the agent itself: "How does this work?" For new users, copy a proven system — barely type prompts, just describe the outcome and let the agent code the rest.

When building AI agents (autonomous programs that complete tasks for you), beginners often treat the agent as the main focus. A common mistake is trying to build a custom agent from scratch using complex code or NAN workflows (a type of manual step-by-step automation). This is harder and more fragile than needed. Instead, you should build the information the agent reads. Create a clear hierarchy of instructions and context files. The agent is just whatever AI you point at that information — switch from Claude to ChatGPT tomorrow, and the same setup works.

Another pitfall is using one agent for every task. This wastes money and speed. You get better results by matching the right intelligence to the right task. Use a cheaper, faster model for simple lookups and a powerful one for complex coding. Even OpenAI ships an official plugin inside Claude Code to run two agents together, using each where it excels.

A key best practice is to let the agent handle planning itself. Instead of you breaking a job into pieces manually, let Claude Code write a plan, split the work into sub-agents (smaller helper agents that run in parallel), and check its own work. Some sub-agents do tasks while others try to break the results, continuing until they agree. This "agentic" approach (where the AI autonomously decides what to do next) reduces mistakes. Always review the generated code manually — AI still makes errors frequently. Mastering one agentic coding tool deeply, like Claude Code, will make you far more effective than jumping between tools.