Engine
Engine is the execution layer for multi-agent investigations. It orchestrates the agents, tools, and control flow behind every MouseCat run — in Python, versioned like the rest of your code.
What an Investigation Produces.
A MouseCat investigation pursues several lines of inquiry at once, weighs the evidence for and against each, and resolves them into a single verdict — block, review, or allow — with a confidence level, a fraud type, explicit reasoning, and the specific signals that drove it.
It takes the possibility that nothing is wrong as seriously as the possibility that something is. That is why MouseCat is as useful for clearing false positives as it is for catching fraud.
Findings Are Challenged.
A finding doesn’t become a verdict because an agent asserted it. Findings are challenged before they stand, and the investigation carries the whole trail — what was claimed, what was contested, and what changed.
You can see where the agent changed its mind, and why. A model score can’t tell you that.
Investigations Are Code.
Graph structure, state schema, and agent wiring are all Python — no YAML, no manifest, no drag-and-drop builder. That means your investigations diff, review, version, and roll back like any other production code.
Prompts live as plain Markdown files, so analysts and strategists can edit the reasoning without touching the wiring.
Built to Run Every Case.
Cases run concurrently with a shared concurrency budget across the whole batch, so fan-out never overwhelms your data APIs or model gateway. A failure in one case is isolated to that case.
People in the Loop.
An investigation can pause for analyst input and resume with that input in context — not as a notification after the fact, but as part of the run. You decide which cases warrant it and where in the investigation the agent stops to ask.
Analyst decisions and case notes feed back into the evaluation sets the agents are measured against, so the judgment of your best reviewers compounds instead of evaporating.
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