Why not handle this with a fixed checklist or rules engine instead of an agent?
Rules cover deterministic requirements, but real cases involve substitutes, mislabelled files and conflicting evidence, so the boundary between rules and judgement is where training and evaluation value sits.
**Why not use a fixed checklist or rules engine?**
A rules engine works when requirements are fixed and documents arrive as clean, labelled fields. Real cases are less tidy. Required items depend on case type, circumstances and policy version. Documents arrive mislabelled, combined into one file or partly legible. Two documents can each look fine and still conflict.
An agent has to combine rule-like checks with judgement. Is this letter an acceptable substitute for the form named in the policy? Is a six-week-old statement close enough, or is there a hard cutoff? Is the gap worth stopping the case for, or can the handler proceed and collect the item later?
**Does that mean rules have no place?**
They do. Many requirements are deterministic and are better checked by tools than by the model. The value for training and evaluation is at the boundary: cases where rules are silent or in tension, and where the handler's recorded decision shows which way it went.
**What does MirrorFirm add?**
Generated cases that carry the requirement set, the supplied documents, the decision and the request that followed, shaped by direct observation of how handlers work. The output is synthetic, so a buyer should still inspect how closely it is grounded in any real source and what it could disclose.
Test the agent where rules are silent, ambiguous or in tension, because that is where handlers' recorded decisions carry information.
- Audience:
- Agent developers
- Content type:
- FAQ
- Buyer question:
- What it is and why it matters
Content release: r-a220eb6e099b243b
Related questions
- What does it mean for an agent to decide whether the supplied documents are enough?
- What does a sufficiency decision look like on a real loan application?
- How do I tell whether my process suits sufficiency-focused training or evaluation?
- Where does the sufficiency decision sit in an agent loop, and which tool actions does it need?
- How does a sufficiency decision map onto environment state and tool actions?
- What makes a sufficiency decision worth putting in a simulated environment?