Contracts must become structured fields at scale. Which Agent Bricks option fits?
Why D is the answer
Information Extraction
Information Extraction pulls fields from text.
In detail
- Information Extraction pulls structured fields from text.
- That is the requirement here.
- Databricks has released a newer version of Information Extraction, so check its current docs.
- Review a sample of the results before you trust the fields.
Use case
An illustrative scenario, not a real incident.
A legal team must turn 50,000 contracts into rows with party, date and value. Information Extraction pulls those fields at scale, ready for a table.
See it in different ways
flowchart TD
A{"What is the goal?"} -->|"Answer from documents"| B["Knowledge Assistant"]
A -->|"Coordinate several agents"| C["Multiagent Supervisor"]
A -->|"Pull fields from text"| D["Information Extraction"]mindmap
root((Agent Bricks))
Knowledge Assistant
Questions on documents
Information Extraction
Fields from text
Multiagent Supervisor
Coordinates agentsflowchart LR S0["Contracts"] --> S1["Information Extraction"] --> S2["Structured fields"] --> S3["Table"]
Key point: Fields from text means Information Extraction.
Why the other options are wrong
- A. Multiagent Supervisor, with one agent for each field in the contract
- A supervisor coordinates agents, and one agent per field is far too much.
- B. Knowledge Assistant, with a separate chat for each document
- Knowledge Assistant answers questions, and does not fill fields at scale.
- C. A Genie Space over the raw PDF files in a volume
- Genie Spaces answer questions over tables, not raw PDFs.