If you’re building AI Agents, it’s super important to figure out the optimal use-cases that maximize what agents are good vs. what they’re not ready for *yet*. There are so many categories of work that AI Agents can help automate or augment. Choosing the right ones that can deliver value in the near term and get better over time with model improvements is critical. Here are a few characteristics that seem to be working right now: * Work that requires a heavy amount of unstructured data and information. This could be documents, visual data on a screen, video content, and more. This is the domain that computers and software have never been able to do before, and the use-cases here are vast. * AI Agents are useful for things that otherwise require human judgment or interpretation, and that may always be the case. The moment you find yourself hoping to replicate something with very strict rules that happen over and over again, you probably want software, not agents. * The more complex work that’s being automated, the more that there’s a need for a human in the loop element. This is why code agents work super well right now is you can eventually test and study the output of the agent to figure out what came back right or wrong. Even when these agents do things wrong, intervention is relatively straightforward for any skilled user. * Bet on use-cases where the core intelligence of models getting better will continue to accrue to your agents. If you can solve everything about your use-case with AI today, it’s probably not an interesting enough market to go after. Go after scenarios where there’s incremental value that gets added with model improvements. Tons of more characteristics determine which use-cases are good for agents at this stage, but ultimately tons of opportunities in every category of work to go after.