The approach I'm setting up for agents that approve things is a small logic model that double checks the decision
I build internal automations for a mid size company's finance team, mostly agents that read our policies and handle routine requests like expenses and refunds.
A common way these agents go wrong is that they read a policy, read a request, and then confidently make a call the policy doesn't actually allow. The tools all worked fine. The reasoning slipped.
The approach I'm setting up is a second opinion step. Before the agent does anything hard to undo, a small separate model reads the policy, the facts and the agent's decision, and answers one question, does this decision actually follow from the rules? If the answer is no, the request goes to a human instead ( It's tricky ik )The use case is an expense approval agent. Say the policy says meals over $300 need a manager's sign off unless the employee is a director. A request comes in for a $400 client dinner from someone who isn't a director. The main agent sees "client dinner," decides it's fine and approves it. The checker's only job is to notice that the director exception doesn't apply, so the approval doesn't follow from the policy.
The usual pick for a checker like this is a general model like Qwen3-8B. I went with TwIL-LM3 Pro instead, which webAI released recently. TwIL LM3 Pro is a 3.66B model trained only on logic, meaning whether a conclusion really follows from the facts you give it, which is exactly the checker's jobTheir eval chart is attached. On their logic test it scores 55.4, about the same as Qwen3-8B at 53.4, at less than half the size. Similar small models like VibeThinker 3B score 41.2. On questions that ask whether a conclusion follows from the given facts, it gets 67% right, and on a standard logic puzzle set it scores 95.4%. In webAI's speed test it also got through more than twice as many logic checks per second as Qwen3-8B on the same hardware.It has clear limits. On everyday tasks Qwen3-8B and other general models score higher, so it only makes sense as the checker, not the main agent. OpenAI's 120B open model still does better on logic. It thinks for a long time before answering, and about a quarter of its answers get cut off if you don't give it enough room. The upside is that it's a 2 GB download that runs on a normal laptop, so the checker doesn't need another GPU and none of the company data leaves the machine. The license is non commercial, so this is for testing, not for a product you sell.
I'll post how it does on real requests once it's running. Until then, for people running agents that approve or send things like do you have a second check like this, or do you trust the main model? And would you use a small model built only for logic ?