AI agents are software systems that take over tasks made up of manual, multi-step processes. These often require context, judgment, and adaptation, making them difficult to automate with simple rule-based code. While traditional automation is possible, it usually means hardcoding endless edge cases. Agents offer a more flexible approach. They use context to decide what to do next, reducing manual effort on tedious steps while keeping a review process in place for important decisions. The most effective AI agents are narrow, tightly scoped, and domain-specific. Here's how to approach building one. Link to heading Step 1: Prototype the agent by hand Start by simulating the agent manually, before writing any code. Understand the task the way a person would approach it, step by step. Use real inputs, whether they’re screenshots, API responses, or messy CSV data. Feed those into an LLM manually and guide it through the process with prompts that mirror the actual workflow.…