Multi-Agent AI
Multi-agent AI refers to a system where multiple specialized AI models work together on a task, each handling a distinct part of the problem. In pharmacy automation, a multi-agent approach means different models handle document reading, clinical reasoning, catalog lookup, and Kroll write — rather than one model attempting to do everything.
How multi-agent AI differs from single-model OCR:
An OCR-only tool uses one model: read the image, return the text. The output is the text on the page, nothing more. A pharmacist or technician then interprets that text and looks up the correct DIN.
A multi-agent system adds layers:
| Layer | What it does |
|---|---|
| Vision / extraction agent | Reads the document image, extracts fields |
| Comprehension agent | Interprets field values, resolves abbreviations, validates internal consistency |
| Context agent | Queries Kroll for patient profile, drug catalog, and history |
| Match agent | Selects the correct DIN from your formulary given all available context |
| Write agent | Constructs and executes the Kroll write with the confirmed values |
Each agent is optimized for its specific task. The vision agent is tuned for document reading; the context agent is tuned for querying Kroll’s data model. Neither has to be a general-purpose system that’s mediocre at everything.
Why this matters for accuracy:
In a single-model approach, accuracy is limited by how much context the model can use at once. In a multi-agent approach, the match agent receives the prescription fields, the patient record, and the drug catalog simultaneously — making a more informed DIN selection than any single model working from OCR output alone.
AutoRx’s multi-agent architecture is why DIN match rates exceed 95% on first attempt and why sig line interpretation handles notation that defeats simple pattern matching.