Multi-agent Intelligence for Navigating Complex Documents (M.I.N.C.D)

System Architect: Nick Norman Status: Operational

MINCD is a multi-agent system designed to navigate, interpret and extract insights from complex documents—like legal texts, financial documents, contracts, archives, and historical records—at scale. It equips agents with reasoning tools and context awareness to extract insight from materials traditional AI and OCR tools often struggle to handle.

About This Project

The goal of MINCD was to build AI agents capable of navigating complex, analog-era documents—like historical government records and legal opinions—where traditional OCR and search tools often fail.

I began training AI agents on the historical government documents in UC Berkeley's Institute of Governmental Studies Library—specifically because of how difficult these materials are to navigate online. Many of these documents—rare government records from UC Berkeley’s Institute of Governmental Studies—were created before the digital era and posed unique challenges: handwritten notes, foldouts, inconsistent pagination, typewritten marginalia, and evolving amendments.

To help agents navigate this complexity, I equipped them with research logic—how to think through structure, context, and metadata the way a trained archivist might—embedding that intelligence directly into their system DNA.

After initial success with the IGSL Local Dig collection, I pushed the system into a new domain: U.S. Supreme Court opinions. These documents are dense, legally complex, and highly formatted. Summarizing them isn’t just about compression—it’s about precision, logic, and compliance. I designed a multi-agent process to test whether the intelligence embedded in earlier training could transfer across domains—expanding agent capabilities while preserving structure, clarity, and legal rigor.

Agent Roles at a Glance

While MINCD relies on just three core agents, the framework is intentionally designed to scale. New agents can be added—or existing ones adapted—to suit different document types, domains, or archival contexts. From legal briefs to historical pamphlets, the system can flex to meet the shape and logic of nearly any set of documents or collection.
Here’s how the agents operate:

Understand & Condense (Summarizer Agent)

This agent condenses dense, complex documents into clear, concise summaries. Designed for high-trust contexts like judicial research, this agent balances clarity with compliance, aligning with institutional meaning, strict formatting rules and linking summary sentences to their original sources.

Evaluate & Refine (Reviewer Agent)

This agent audits the Summarizer's work for accuracy, clarity, verification strength, and compliance. It flags inconsistencies, offers feedback, and ensures final outputs meet defined standards and are ready for institutional or archival use—without overcorrecting where none is needed.

Forecast & Recommend (Look-Ahead Agent)

Analyzes documents in advance to assess complexity and recommend processing strategies for edge cases or atypical materials. Helps determine whether to assign standard or specialized agents—improving efficiency and reducing failure points.

Review & Escalate (Human-in-the-Loop)

A human remains involved at key checkpoints to interpret edge cases, provide escalation pathways, and maintain institutional trust. This ensures outputs stay aligned with real-world standards, evolving needs, and nuanced judgment.

Who is MINCD for?

MINCD is designed for researchers, librarians, legal analysts, and institutions that work with complex or historical documents—offering a modular agent framework that adapts to the challenges of analog-era materials and modern digital demands alike. If you’re interested in testing ideas, collaborating, or just thinking aloud together, I’d love to connect.
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