Multi-Agent Document Ingestion for Regulated Industries

An enterprise AI platform serves regulated industries. Its customers send unstructured documents (loan applications, customer complaints, supporting financials, identity documents) and need them turned into structured records that downstream systems can act on.

Today, those records are produced by humans. Operators classify each document by type, extract the fields by hand, validate them against business rules, then route to the next step. Throughput caps at how fast someone can read. Backlogs grow. Customers wait.

AI demos that promise to fix this keep dying in pilot. They produce outputs that look right but aren't shaped right, and the compliance team can't sign off on production use because the system can't tell you why it made a given decision.

Don't replace the operator with a single black-box LLM. Build a pipeline of specialist agents that each do one job well, hand off via deterministic contracts, and ask for human review when their confidence is too low to act.

A pipeline of single-purpose agents running over Anthropic Claude on AWS Bedrock. Each agent does one thing: read and classify the document, pull structured fields against a schema, validate against business rules, handle deterministic numeric work so the LLM doesn't have to do arithmetic. Output that doesn't match the expected shape gets rejected before it reaches the next stage.

A coordinator routes the uncertain cases, the ones the agents aren't confident on, to a human review queue with the reasoning surfaced inline. The reviewer verifies in seconds instead of redoing the work from scratch.

Every step is logged: which agent ran, which model was used, what came in, what went out, what the operator override was. Compliance can trace any decision end-to-end. This is the trust layer that lets the system go to production where black-box AI couldn't.