Document Agents

Introduction

Professional organizations generate a considerable volume of documents every day: activity reports, meeting minutes, contracts, invoices, technical specifications, correspondence, studies, and official records. This document flow, essential to business operations, quickly becomes unmanageable as it accumulates. A legal department receives hundreds of contracts per month, a technical directorate archives thousands of specifications, a sales department accumulates quotes and customer exchanges. Manually extracting relevant information from this document mass is not only time-consuming but also a source of errors and omissions. Staff spend an increasing share of their time searching, reading, and summarizing documents, to the detriment of value-added tasks.

Classic document processing approaches quickly show their limits. Full-text search, while useful for finding a keyword, captures neither meaning nor relationships between information. It relies on exact lexical matching that ignores synonyms, abbreviations, and context: a search for termination will not find a document mentioning cancellation or contract expiry. Simple OCR, commonly used to digitize scanned documents, extracts raw text without structure or semantic understanding: it produces a string where tables, headers, signatures, and annotations are mixed indiscriminately. Basic RAG systems, which combine a vector database with an LLM, improve semantic search but remain limited: they process each query in isolation, without memory of previous analyses, without the ability to build cumulative synthesis, and without an overall view of the document corpus.

Faced with these limitations, a radically different approach is needed. The Document Agents project explores the design of intelligent agents specialized in professional document processing, equipped with persistent contextual memory, planning capabilities, and specialized tools for interacting with documents. The central idea is to no longer consider a document as a static file to be processed on an ad hoc basis, but as a node in a living knowledge graph, where each analysis enriches the overall understanding of the domain. The agent does not simply answer a question: it progressively builds a structured, interconnected representation of all the information it processes.

The project's vision is a system capable of understanding, organizing, and leveraging the knowledge contained in professional documents with a depth and continuity unattainable by traditional approaches. Where a classic RAG system answers a question and then forgets, the document agent retains the memory of each analysis, each decision, each link established between pieces of information. It can thus detect contradictions between documents, identify trends over a period, connect decisions to their justifications, and produce syntheses that reflect the cumulative knowledge of the corpus. This persistent memory transforms document processing: each document read enriches the knowledge base, each analysis improves the quality of future analyses.

The system architecture relies on a central orchestrator that coordinates several specialized agents. Each agent is designed for a specific mission and exposes its capabilities via the MCP (Model Context Protocol), ensuring interoperability and scalability. The orchestrator receives incoming documents, analyzes the nature of the task to be performed, plans the sequence of agent intervention, distributes work, and ensures overall processing consistency. It manages priorities, dependencies between steps, and error cases. Agents communicate with each other via a message bus, share a common context, and can call upon each other to validate or complete an analysis. This modular architecture allows adding new agents without disrupting the existing system and specializing each agent in its area of expertise.

The analysis agent is the first link in the processing chain. Its mission is deep information extraction from documents: identification of named entities (people, organizations, places, dates, amounts, references), detection of relationships between these entities (a contract linked to a client, a clause associated with a deadline), extraction of facts and events (decisions made, actions to be performed, deadlines), and capture of structural metadata (document type, author, recipient, version, creation date). The analysis agent does not limit itself to superficial extraction: it understands the document structure, distinguishes sections, appendices, and cross-references, and identifies implicit information — for example, a notification obligation that follows from a clause without being explicitly named as such. Analysis results are structured according to a standardized data model and fed into the knowledge graph.

The synthesis agent takes over to produce an aggregated, intelligible view of the extracted information. Unlike a simple automatic summary, it can synthesize multiple documents simultaneously, identifying common points, divergences, and complementarities. Its mission includes generating structured reports: summary sheets per document, thematic reports covering a set of documents on a given subject, comparative tables between several versions or variants, event chronologies reconstructed from multiple sources. The synthesis agent relies on the knowledge graph to navigate between linked documents and uses vector search to find relevant passages beyond explicit links. It adapts the level of detail and output format to the user's need: executive summary of one paragraph, detailed report of several pages, or structured data table.

The classification agent handles document routing and categorization in the system. It analyzes each document to determine its nature (contract, report, invoice, letter, specification), its domain (legal, technical, commercial, financial), its sensitivity level (confidential, internal, public), and its urgency (immediate processing, scheduled, archivable). Classification does not stop at a single label: a document may belong to several categories with different weights. The classification agent also prioritizes documents to be processed according to configurable rules: a contract under negotiation will be prioritized over an archived document, a customer complaint will pass before a periodic report. Classification decisions are stored in the knowledge graph and can be manually revised, enabling learning from user feedback.

The coordination agent plays an essential cross-functional role. It orchestrates processing workflows by defining dependencies between agents: a document must first be classified before being analyzed, and analysis must precede synthesis. It handles complex cases where multiple documents must be processed in a specific order or where one agent's results condition another's work. The coordination agent also ensures cross-validation: when two documents contain contradictory information, it triggers a verification procedure that may involve human review or in-depth analysis by the analysis agent. It supervises workflow execution, detects blockages, reassigns tasks in case of failure, and produces activity reports on processing progress.

Persistent contextual memory is the project's central innovation, distinct from stateless RAG systems. Each analysis session retains the complete history of interactions, decisions made, and results produced. This memory allows the user to resume an interrupted analysis, consult justifications for a past decision, or request an evolving synthesis incorporating results from multiple sessions. Memory relies on two complementary technologies. Neo4j, the graph database, stores the knowledge structure: documents, entities, relationships, categories, decisions, forming a navigable semantic network. Each node and each relationship carries metadata — creation date, source, confidence level — ensuring complete traceability. Graph queries enable finding paths between pieces of information, discovering indirect relationships, and exploring the corpus in a non-linear way.

Qdrant, the vector database, complements the graph by providing semantic search on textual content. Each document, paragraph, and analysis extract is converted into an embedding vector that captures its semantic meaning. Vector search allows retrieving relevant passages even when exact terms do not match: a search for contractual liability will find documents dealing with fault, damages, and causation, even if these terms do not appear explicitly. The combination of graph and vectors offers the best of both worlds: the precision of Neo4j's structured relationships and the richness of Qdrant's semantic search. The agent can navigate from the graph to vectors to find related documents, and from vectors to the graph to explore the relationships of a relevant passage.

The project's use cases cover three main domains. In the legal domain, the system assists teams in contract analysis: extraction of essential clauses, identification of reciprocal obligations, detection of deadlines and renewal conditions, comparison between successive versions of the same contract, and alerts on atypical or risky clauses. For a law firm or corporate legal department, the agent can process hundreds of contracts in minutes, where manual analysis would take days. The legal knowledge graph allows linking contracts to each other by stakeholders, subjects, or clauses, offering a consolidated view of the organization's commitments.

In the technical domain, the system processes specifications, API documentation, user manuals, and test reports. The analysis agent extracts software architectures, component dependencies, versions, and technical prerequisites. The synthesis agent produces consolidated documentation from heterogeneous sources or generates a non-regression report between two versions of a system. The classification agent organizes documents by component, version, and criticality. For a development team, the system becomes a document hub that maintains up-to-date knowledge of architecture and technical decisions, even when teams change or documentation accumulates.

In the commercial domain, the system analyzes quotes, proposals, meeting minutes, and customer exchanges. It extracts commitments made, agreed delivery dates, contracted scopes, and blocking points. The synthesis agent produces consolidated prospect sheets grouping all exchanges and documents related to a client. The classification agent prioritizes sales opportunities according to their maturity and amount. For a sales director, the system offers an overview of commercial commitments, detects gaps between proposals and deliveries, and alerts on non-compliance risks.

The system's benefits are multiple and measurable. Decision traceability is ensured by persistent contextual memory: each extraction, classification, and synthesis retains the provenance of the information used. For a regulatory question or audit, it is possible to trace back to the exact source of each piece of information, the original document, the precise passage. Analysis quality improves over time: the knowledge graph is enriched with each new document processed, refining domain understanding and the accuracy of future extractions. Team productivity increases significantly: time spent searching, reading, and summarizing documents is reduced by several hours per week per staff member, freeing time for value-added tasks — strategic analysis, decision-making, client relations.

Reliability is reinforced by cross-validation between agents and retention of complete analysis history. Extraction errors are detected through cross-referencing between documents and by coordination agent alerts. Information consistency is maintained in the knowledge graph, which detects contradictions and inconsistencies. For an organization subject to compliance obligations (GDPR, sector regulation), the ability to demonstrate the provenance and reliability of processed information is a decisive asset. Processing standardization guarantees homogeneous quality across all documents, regardless of the person or department that introduced them into the system.

The project's evolution prospects are promising. Continuous learning is a major avenue: corrections and validations made by human users are fed back to improve extraction and classification models. A user who corrects a misidentified entity or erroneous classification enriches the system, which adapts its behavior for future documents. Human feedback, integrated as feedback loops, allows progressively fine-tuning each agent's performance. Ultimately, the system could offer preventive analyses, anticipating information needs even before the user formulates them. Automatic enrichment of the knowledge graph through cross-referencing with external sources — public data, legal databases, technical repositories — would pave the way for ever richer, more contextual knowledge.

Generalization to other document types and business domains is natural thanks to the modular architecture. Each new domain can be covered by adding a specialized agent and enriching the corresponding knowledge model. Integration with existing systems — DMS, ERP, CRM — via the MCP protocol would feed the system directly from the organization's document flows. Evolution toward agents capable of proposing action recommendations — suggesting a contract modification, alerting on an imminent deadline, proposing a standard response to a client — would transform the system from a passive analysis tool into an active decision assistant. Document Agents lays the foundations for a new generation of professional knowledge management systems, where artificial intelligence does not just respond but understands, remembers, and anticipates.