Document Agent: Processing Documents with AI

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What this article covers

• What a document agent is and how it works.
• How the agent analyzes, extracts, and transforms documents.
• Equipping the agent with tools and RAG.
• Practical examples for invoices, contracts, reports, and forms.
• Best practices for accuracy, security, and scaling.

Introduction: Understanding the document agent

A document agent is an AI agent that processes documents autonomously. It reads, comprehends, extracts data, and takes action. Not just “pull out the text,” but “understand the document and respond to it.”

This article is for anyone building AI agents for document processing. For foundational concepts, check out AI Agents and Document Automation.

Why do you need a document agent?

Imagine receiving 50 invoices daily. A traditional system extracts text. A document agent works differently: “Invoice RE-2024-1234, Company X, €1,234, due 15.03., items: 3x consulting at €400 each. Risk flag: no payment terms specified. Action: move to invoices folder, send payment reminder for 10.03.” The agent understands and acts.

Document agent at a glance

Document → Agent analyzes with LLM → Calls tools (extract, transform, store) → Executes action (archive, notify, forward). Pair with RAG for document Q&A.

The core idea is simple: don’t just read, understand and act.

Who this article is for

• Enterprises processing documents automatically.
• Developers building document agents.
• Office workers reducing routine tasks.
• Self-hosters running agents locally.

Key terms

• AI Agent - Autonomous actor. When useful: the concept.
• Tool Calling - Invoke tools. When useful: for actions.
• RAG - Document Q&A. When useful: for questions.
• Ollama - Local model server. When useful: the backend.
• n8n - Workflow tool. When useful: orchestration.

Architecture

Document arrives (email, upload, scan)
    │
    ▼
Document Agent (Ollama + Tools)
    │
    ├─ Observe: Read document
    ├─ Understand: What is this? What does the sender want?
    ├─ Plan: Which tools? Which actions?
    ├─ Act: Call tools
    │   ├─ extract_text: Extract text
    │   ├─ extract_metadata: Extract metadata
    │   ├─ classify: Classify
    │   ├─ summarize: Summarize
    │   └─ store: Store
    └─ Verify: Did it work?
    │
    ▼
Actions
    ├─ Move to folder
    ├─ Store in database
    ├─ Send notification
    └─ Trigger workflow

Practical example 1: Invoice agent

class InvoiceAgent:
    """Agent for invoice processing"""

    async def process(self, pdf_path):
        """Process invoice"""
        # 1. Extract text
        text = await self.extract_text(pdf_path)

        # 2. LLM analyzes
        analysis = await self.analyze(text)

        # 3. Call tools
        invoice = await self.extract_invoice_data(text)
        risks = await self.check_risks(text)

        # 4. Actions
        await self.store(invoice)
        await self.move_to_folder(pdf_path, "rechnungen")

        if risks:
            await self.notify(f"Risiken in Rechnung {invoice['number']}: {risks}")

        return invoice

    async def analyze(self, text):
        """LLM analyzes the invoice"""
        return await ollama.generate(f"""
Analysiere diese Rechnung:
{text[:4000]}

Extrahiere als JSON:
- rechnungsnummer
- datum
- betrag
- waehrung
- absender
- faelligkeitsdatum
- positionen (Array)
- risiken (Array)""", format="json")

Practical example 2: Contract agent

class ContractAgent:
    """Agent for contract analysis"""

    async def process(self, contract_text):
        """Analyze contract"""
        # Multi-stage analysis
        summary = await self.summarize(contract_text)
        clauses = await self.extract_clauses(contract_text)
        risks = await self.assess_risks(contract_text)

        # Recommendation
        recommendation = await self.recommend(
            summary, clauses, risks
        )

        return {
            "summary": summary,
            "clauses": clauses,
            "risks": risks,
            "recommendation": recommendation
        }

    async def assess_risks(self, text):
        """Assess risks"""
        return await ollama.generate(f"""
Bewerte die Risiken in diesem Vertrag:
{text[:5000]}

Antworte als JSON:
{{"risks": [{{"type": "...", "severity": "low|medium|high", "description": "..."}}],
 "recommendation": "unterschreiben|aendern|ablehnen"}}""", format="json")

Practical example 3: RAG agent for document Q&A

class DocumentQAAgent:
    """Agent for document questions"""

    def __init__(self):
        self.vectorstore = QdrantClient("http://qdrant:6333")
        self.collection = "dokumente"

    async def ask(self, question):
        """Ask question to documents"""
        # RAG: Find relevant documents
        results = await self.vectorstore.search(
            collection=self.collection,
            query_vector=await self.embed(question),
            limit=5
        )

        context = "\n".join([r.payload["text"] for r in results])

        # LLM answers
        answer = await ollama.generate(f"""
Beantworte die Frage basierend auf den Dokumenten.
Dokumente: {context}
Frage: {question}
Antworte mit Quellenangabe.""")

        return {
            "answer": answer,
            "sources": [r.payload["source"] for r in results]
        }

Tools for document agents

tools = [
    {
        "name": "extract_text",
        "description": "Text aus Dokument extrahieren (PDF, DOCX, Bild)",
        "function": extract_text
    },
    {
        "name": "extract_metadata",
        "description": "Metadaten extrahieren (Datum, Autor, Betrag, ...)",
        "function": extract_metadata
    },
    {
        "name": "classify_document",
        "description": "Dokument klassifizieren (Rechnung, Vertrag, ...)",
        "function": classify_document
    },
    {
        "name": "summarize",
        "description": "Dokument zusammenfassen",
        "function": summarize
    },
    {
        "name": "store_document",
        "description": "Dokument in Datenbank speichern",
        "function": store_document
    },
    {
        "name": "search_documents",
        "description": "Dokumente durchsuchen (RAG)",
        "function": search_documents
    }
]

Security considerations

• Confidential documents: All data stays local. See Data Protection.
• Prompt injection: Documents can contain injection attacks. See Prompt Injection.
• Validation: Critical extractions should be validated.
• Permissions: The agent should have only necessary permissions. See Tool Permissions.

Common pitfalls

• OCR errors: Poor scans mean poor extraction. Check quality first.
• Context length: Large documents need chunking.
• Misclassification: The AI can classify incorrectly. For critical documents, verify manually.
• Too many tools: More than 5-7 tools overwhelm the model.
• No fallback: If the agent fails, have a manual process ready.

Further reading

• AI Agents - Fundamentals.
• Document Automation - Workflow version.
• Document analysis - Techniques.
• Local RAG - Document Q&A.
• Tool Permissions - Security.
• Ollama - Model server.

Key takeaways:

• Document agent: reads, understands, extracts, acts, autonomously.
• Tools: extract_text, extract_metadata, classify, summarize, store, search.
• Works for invoices, contracts, reports, forms.
• Use RAG for document Q&A.
• Run locally with Ollama: all data stays private.

FAQ

Sources and further reading

• Ollama - Local model server.
• LangChain Agents - Agent concepts.
• Qdrant - Vector database.

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