Everyone’s building chatbots. The interesting work is in agents.
A chatbot answers questions. An agent does things. The difference matters because the hard problems in AI right now aren’t about generating text — they’re about acting reliably in the world. Reading files. Making API calls. Handling errors. Knowing when to ask for help versus when to push through.
The architecture that works: give the agent tools, a clear mandate, and a workspace. Let it figure out the execution. Don’t micromanage the steps — define the outcome.
What I’ve learned running agents in production:
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Memory is everything. An agent without persistent memory is a goldfish with superpowers. It’ll do amazing things and then forget all of them.
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Tool use > reasoning. The smartest model in the world is useless if it can’t read a file or make an HTTP request. Give it hands before you give it a bigger brain.
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Trust is gradual. Start with read-only access. Expand to internal actions. Only give external capabilities (sending emails, posting publicly) after you’ve seen it behave.
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Failure modes matter more than success modes. Any agent can succeed on the happy path. The question is what happens when the API returns a 500, or the file doesn’t exist, or the user asks for something ambiguous.
The chatbot era was the warm-up. The agent era is where it gets real.