Building AI Agents

An AI agent is a system that can independently plan and execute tasks, like a virtual assistant that writes code or manages a project. Instead of just answering questions, an agent takes action: it can scan a codebase, draft implementation, write tests, or even build other agents. This matters for AI development because agents turn raw AI power into real-world results.

Building with agents means designing how they work together. You might create agent teams (groups of specialized agents, each with a dedicated role) — one scouts files, one plans, one writes code, one reviews. Or use agent chains (agents that pass work in sequence), where the first generates a schema, the second validates it, and so on. Each agent can run in its own isolated context, preventing information from leaking between tasks. This is called building multi-agent systems.

Why does this matter? Most teams get stuck moving an agent from a demo to production — the gap between a prototype that works on a laptop and one that works at scale can take 3 to 9 months. To close that gap, you need to solve four problems: customization, evaluation (measuring quality before users see it), deployment, and observability. The real skill isn’t coding — it’s designing what agents should actually do and how they should be proactive.

Start with workflow fundamentals before building agents. Learn how automation functions first. Once you understand that, you can design agents that save businesses time, money, and focus — shifting from selling AI to selling solutions.

Building an AI agent with Claude starts with a clear scope, not code. If you can’t explain what you want, neither a human nor an AI can build it. Begin with plan mode, then write simple instructions describing the problem, the tools you’ll use, and the end result. Save those instructions as a markdown file (a text file an AI can read) — an agent is basically that file plus three things: instructions, tool access, and lessons you teach it.

Treat your agent as a harness (a shell that runs the AI around your project). You don’t build the model; you build the information it reads. Set up a folder with your agent’s file and any project details. Claude reads that folder and becomes your agent — switch to another AI later and it can read the same setup. No coding required, just clear communication.

Three steps to start: step one, write instructions; step two, give it tool access; step three, teach your agent by storing memory or examples. For real projects, use a plugin like Skill Smith to generate this structure in about ten minutes. Then test and publish. Remember, fixed-step workflows are different from dynamic reasoning — start simple with fixed steps.

A common mistake is overcomplicating. Agents are just harnesses; different tools look different on the surface but share the same core. Start with one main agent, like a chief of staff, then expand. If you get stuck, use Claude’s plan mode to refine your prompt before letting it run loose.

Building an AI agent (a system that completes tasks using a large language model) is simpler than it looks, but most beginners make the same avoidable mistakes. The biggest pitfall is overcomplicating the build. You do not need complex code. An agent is just a folder with three things: clear instructions, the tools it can use, and access to the information it needs. In fact, one creator says you don't build agents at all—you build the information they read. The agent is whatever AI you point at that information, whether it's Claude, Grok, or something else.

The best practice is to think in skills (a markdown file, a text file the AI reads, with instructions). A skill is just a text file with clear instructions. Agents, skills, and commands are pretty much the same thing. So instead of building a custom agent from scratch, write a simple skill file. The old way of building agents through NAN workflows or custom code is harder and unnecessary.

Another common mistake is not testing your agent. At a checkpoint, you should have an evaluated agent with known failure modes (specific ways it goes wrong) and measured costs. Expect the first version to get things wrong, but it should improve over time.

A smart practice is to match the right intelligence to the right task. Using a powerful, expensive model for every job wastes money. Run multiple agents with different models for different tasks, which can save up to 50% on costs. Even big labs do this. Finally, manage your agents like a board: give high-level goals and metrics, then step back and let them work. Start small, use skills, test, and keep information clean.