Hire AI Agent Developers.

• Evidence of production systems, not just demos: ask what happens after launch, not just what was built.
• A concrete answer for evaluation and regression testing, not a vague reference to "testing the model."
• Fluency in the guardrails and permission design covered in our security guide; this is where inexperienced teams cut corners.
• Willingness to scope a narrow pilot rather than sell a broad platform upfront.
• A clear answer for how they'd roll back or disable the agent quickly if it starts misbehaving in production, not just how they'd launch it.

• "Walk me through how you'd evaluate whether this agent is working correctly, before and after launch."
• "What does your incident process look like if the agent takes a wrong action in production?"
• "How do you scope tool access, and who reviews that scope before it ships?"
• "What's a project where the original plan changed significantly, and why?"

A team that answers these with specifics from real projects is a different conversation than one that answers in generalities about "best practices."

• No mention of evaluation or regression testing until you ask directly.
• Pushes a broad, multi-agent platform build before a single workflow has proven the pattern.
• Can't describe how they'd scope tool permissions for your specific systems.
• No clear owner for the system after launch: "we'll hand it off" without a defined transition plan.
• A single aggregate accuracy number offered as proof of quality, with no breakdown by task type or failure mode.

• In-house: best when the workflow is core to your product and you need long-term ownership; slowest to start, since hiring and ramp-up take real time.
• Cloudz as an agency engagement: best for a multi-workflow build with a senior team from day one, and ongoing evaluation ownership as the system grows.
• Cloudz as a freelance engagement: best for a single, well-defined workflow or a scoped pilot, when you want senior-level execution without a full team retainer.