What I Learned Deploying AI Agents
Most of what makes an AI agent reliable isn't the model itself. I learned that by putting agents into real projects and watching where they broke.
An AI agent is software that uses an AI model to take actions, like looking up an order or sending an email. Each kind of action is a tool it can call.
An agent calls one of its tools, and the call times out. The model makes up a file path that doesn't exist. A long task outgrows how much text the model can hold at once (its context window), and the agent carries on as if nothing happened. Demos rarely show any of this, but it's what goes wrong once an agent does real work.
1. Build the Plumbing First
Getting a language model to give a reasonable answer doesn't take long. Everything around it takes longer: error handling, retries, tracking where the agent is in a task, logging, and knowing when to hand control back to a person. The agent failures I've seen usually started in that plumbing.
2. Give Each Agent One Job
Agents hold up when they have one narrow job. Before you build one, write a sentence that says exactly what it does. If you can't, the job is too big, so split it or cut it down until that sentence is easy to write.
3. Keep a Human in the Loop
A lot of teams treat human review as something to remove. I'd keep it until the agent has proven itself on your work. If an agent can send an email, change data, or do anything else that can't be undone, add a step where a person approves the action first. Without that step, one bad run can do real damage before anyone notices.
4. Treat Prompts Like Code
I used to think prompt engineering was a stopgap that smarter models would make unnecessary. I don't think that anymore. A prompt with clear instructions, a few examples, and a defined output format beats a vague one with every model I've tried. I keep prompts in version control, write tests for them, and review changes to them the way I review any other code.
Where to Start
If you're thinking about adding AI agents to your business, start smaller than you think you need to. Pick one repetitive task that someone does by hand every week. Automate that one task well, measure the time it saves and the mistakes it makes, and only then move on to the next. The teams I've seen start big usually end up rebuilding from scratch.
If you'd like a second opinion on which task to start with, tell me what you're working on. I reply by email within one business day, and it costs nothing.
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