AI Agents for Document Processing and Workflow Automation

Document processing is where AI delivers some of its clearest ROI.

Every business has documents flowing in—invoices, applications, contracts, forms, emails. People spend hours reading, extracting, validating, and routing. AI agents can do most of this faster and more consistently.

We've built document AI systems for Australian businesses. Here's what we've learned about making it work.

The Document Processing Challenge

Typical document workflow:

1. Document arrives (email, upload, mail)
2. Someone identifies what type it is
3. They extract relevant information
4. They validate against other data
5. They route for approval or action
6. They enter data into systems
7. They file the document

Each step takes time, introduces error risk, and creates bottlenecks. With enough volume, you need a team just to process paper.

What AI Agents Can Do

Classification

"What type of document is this?"

AI can classify documents by:

• Document type (invoice, contract, application, etc.)
• Urgency level
• Customer segment
• Department/team for routing
• Required action

Accuracy expectations: 95%+ on common types with good training data.

Extraction

"What information is in this document?"

AI can extract:

• Structured fields (dates, amounts, names, addresses)
• Semi-structured data (line items, conditions, clauses)
• Key entities (companies, people, products mentioned)
• Sentiment or intent (for correspondence)

Accuracy expectations: 85-95% depending on document quality and complexity.

Validation

"Is this information correct and complete?"

AI can validate:

• Field formatting (dates, phone numbers, amounts)
• Cross-field consistency (totals match line items)
• Reference data matching (customer exists, product valid)
• Business rule compliance (within limits, proper approvals)

This catches errors before they enter systems.

Routing

"Where should this go next?"

AI can route based on:

• Document type
• Content analysis (urgency, complexity)
• Business rules
• Workload balancing

Intelligent routing gets documents to the right person faster.

Action

"What should happen with this?"

AI agents can:

• Enter data into systems (ERP, CRM, etc.)
• Create follow-up tasks
• Send notifications
• Generate responses
• Flag for human review

The goal: documents flow through the system with minimal human intervention.

Architecture Patterns

Pattern 1: OCR + LLM Extraction

For scanned documents or images:

Image → OCR → Text → LLM Extraction → Structured Data
                          ↓
                   Validation Rules
                          ↓
                   Confidence Score

Use dedicated OCR (Azure Document Intelligence, Google Document AI) then LLM for semantic extraction.

Pattern 2: Direct LLM Processing

For digital documents (PDFs, emails):

Document → PDF/Text Extraction → LLM Processing → Structured Data
                                      ↓
                              Tool Calls for Validation

Modern LLMs with vision can process document images directly for some use cases.

Pattern 3: Hybrid Human-in-the-Loop

For high-stakes documents:

Document → AI Processing → Confidence Score
                               ↓
                    High? → Auto-process
                    Low?  → Human Review → Feedback Loop

Automation where confidence is high, human review where it's low.

Implementation Example

Here's how we'd structure a document agent:

class DocumentAgent:
    async def process(self, document: Document) -> ProcessingResult:
        # 1. Classify
        doc_type = await self.classify(document)

        # 2. Extract based on type
        schema = self.get_schema(doc_type)
        extracted = await self.extract(document, schema)

        # 3. Validate
        validation = await self.validate(extracted, doc_type)

        # 4. Route or act based on confidence
        if validation.confidence > 0.95 and validation.all_valid:
            result = await self.auto_process(extracted, doc_type)
        elif validation.confidence > 0.7:
            result = await self.queue_for_review(document, extracted, validation)
        else:
            result = await self.escalate_to_human(document, validation.issues)

        return result

What Works Well

High-Volume, Standard Documents

Best ROI when:

• Thousands of similar documents monthly
• Consistent format (or limited variations)
• Clear extraction requirements
• Existing process to compare against

Examples: Invoices from known suppliers, standard form submissions, routine correspondence.

Structured with Variations

Good results when:

• Document has consistent structure but varied content
• Key fields are reliably located
• Quality is generally good (not handwritten, not damaged)

Examples: Applications, contracts, statements.

Semi-Structured Extraction

Achievable with:

• Documents that follow conventions but aren't templated
• Extraction of key entities and relationships
• Tolerance for some manual review

Examples: Emails, reports, correspondence.

What's Still Hard

Handwritten Documents

AI can read handwriting (better than many humans), but accuracy is lower. Expect 70-80% for clean handwriting, lower for poor quality.

Solution: Design for human review of low-confidence extractions.

Poor Quality Scans

Faded, skewed, or partial documents challenge OCR.

Solution: Quality scoring to route poor scans for human handling.

Highly Variable Formats

When every document is different, AI can't learn patterns well.

Solution: May need human processing or more sophisticated (and expensive) AI approaches.

Complex Reasoning

"Is this contract favorable to us?" requires understanding context that extraction doesn't capture.

Solution: AI extracts; humans analyze.

Measuring Success

Track:

Automation rate: % of documents processed without human intervention

Accuracy: % of extracted fields correct (requires sampling and checking)

Processing time: Hours from receipt to completion

Cost per document: Total cost including AI, infrastructure, and human time

Error rate: Errors that reach downstream systems

Compare to baseline (manual processing) to quantify ROI.

Implementation Approach

Phase 1: Document Audit

• What documents flow through?
• What's the volume of each type?
• What information needs extraction?
• What validation is required?
• Where does it go after processing?

Phase 2: Pilot One Document Type

• Choose high-volume, standard format
• Build extraction and validation
• Run parallel (AI + human) to measure accuracy
• Iterate until accuracy meets threshold

Phase 3: Expand and Integrate

• Add more document types
• Connect to downstream systems
• Build routing logic
• Implement exception handling

Phase 4: Optimize and Monitor

• Continuous accuracy monitoring
• Feedback loop for improvements
• Edge case handling
• Cost optimization

Cost Considerations

Document AI costs:

Per document: $0.01-$0.50 depending on complexity and tools used

Fixed infrastructure: Platform costs, storage, orchestration

Human review: For exceptions and quality assurance

At scale (10,000+ documents/month), per-document costs become meaningful. Design for efficiency.

Our Document AI Experience

We've built document processing systems that:

• Extract invoice data for automated processing
• Process applications for faster turnaround
• Handle compliance documentation
• Classify and route correspondence

The pattern works. The specifics depend on your documents, volumes, and accuracy requirements.

Talk to us about your document processing challenges.