Agents Usecases

Before you build an agent, you need a clear picture of where agents actually earn their keep. A use case is a fit between a real workflow and what agents do well: multi-step tasks with external tools, fuzzy inputs, and a verifiable outcome. The strongest fits share a shape — repetitive knowledge work that is too variable for rigid automation but too structured to need a human every time. Coding assistants, research and data analysis, customer support triage, personal assistants, scraping pipelines, and game NPCs all fit that mold.

Picking the right use case matters more than picking the right framework. Agents add latency, cost, and nondeterminism compared to a plain script, so they only pay off when the task genuinely requires judgment at each step. Choose a task a cron job could do and you have overengineered; choose one requiring perfect accuracy with no verification path and you have built a liability. The winning projects sit in between: high volume, tolerable error rates, and a human or automated check on the output.

In practice, you’ll evaluate a candidate workflow by asking three questions: can the agent access the tools and data it needs via APIs, can you measure whether a run succeeded, and what happens when it fails? Then start with the narrowest slice — one ticket type, one report, one repo — ship it behind a review step, and expand as your evals prove it out. The next topics walk through the major categories one by one.