Test an AI Agent on Past Cases Before Giving It Live Work

A reliable evaluation batch does not require thousands of entries. For most boutique operations, twenty to thirty archived records provide sufficient variety. Select instances where the historical outcome was clearly examined and documented by senior staff.

• Standard inquiries with complete specifications and normal delivery timelines.
• Incomplete requests where essential details such as dates or attachments were omitted.
• Edge cases involving conflicting client instructions or unusual scope limitations.
• Requests that properly required immediate escalation or a polite refusal.

For example, imagine a consultancy testing an assistant to categorize project intake forms. If two test records lack budget specifications, the evaluation verifies whether the system flags the missing data or invents an arbitrary figure. Observing how software behaves on messy inputs prevents costly surprises.

Automating data extraction or initial drafting does not remove accountability. When testing past interactions, grade the model on whether it respects established operational guardrails. A dependable system knows when its confidence is low and proactively prompts a human colleague for guidance.

Certain responsibilities should remain strictly human. Strategic commitments, commercial discussions, and relationship disputes require contextual judgment that automated routines cannot supply. Establish clear boundaries so team members understand that software drafts suggestions while people retain full ownership of client outcomes.