A lot of agent projects spend months improving prompts, models, and workflows, then ship to production without asking a few fairly basic questions. Before trusting an AI agent with real work, I'd want clear answers to these: 1. What actions is the agent allowed to take?
Define the boundaries before deployment, not after. 2. Which tools and systems can it access? Give access based on necessity, not convenience. 3. Are sensitive actions gated by approval? Not every step needs review, but some decisions probably should. 4. Is there a record of what the agent did? If something goes wrong, you should be able to reconstruct the sequence of events. 5. What happens when the agent is uncertain? Low-confidence outputs need a clear path instead of silently moving forward. 6. Can you roll back mistakes? Every production workflow should have a recovery plan. 7. Who owns the outcome? At some point, a person needs responsibility for the workflow, even if an agent is doing most of the work. None of these questions are particularly complicated. What's surprising is how often they only come up after an agent is already running in production. What would you add to the list before trusting an agent in production?
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For a long time, most AI conversations were about answers.
Could a chatbot write a better email? Summarize a document? Explain a concept? Even when the output was wrong, the result was usually a suggestion that a person could accept, reject, or ignore.
Agents change that dynamic a bit.
An AI agent might update a CRM, approve a workflow, modify a database record, send a message to a customer, or trigger actions across multiple systems. At that point, the conversation is no longer just about the quality of the answer. It's also about the consequences of the action.
