An AI agent is different from a chatbot because it can act: search systems, write records, send messages and invoke external tools. Every tool creates leverage and a new risk surface, so useful agent design begins with permissions before prompts.
Write a permission contract
Document exactly what the agent can read, modify and execute; the financial or commercial limits; and which cases require escalation. A sales agent may read lead context and draft outreach while being explicitly forbidden from changing pricing or sending binding agreements.
Apply least privilege
Create scoped service accounts instead of connecting with administrator credentials. Separate test and production. Do not grant delete access to an agent that never needs to delete.
Log operational traces
Record inputs, tool calls, parameters, outputs and the user or process that initiated the task. You need an auditable record of what happened without relying on opaque internal reasoning.
Bound time, cost and loops
Set maximum steps, timeouts and per-task cost budgets. If an agent exceeds a boundary, it should stop and escalate rather than continue indefinitely.
Test failure modes intentionally
Use missing data, conflicting instructions, prompt-injection documents and failing APIs. Measure whether the system fails safely. Production readiness is defined more by controlled failure than by a polished happy-path demo.
Turn the Idea Into a Measurable Workflow
If your organization faces a similar challenge, describe the current workflow and the outcome you want. Use that as the starting point for a practical diagnostic.
Discuss the Use Case
