Agentic AI for Intelligent Document Processing

The Scenario: Claims Processing at "Global Insure" Global Insure receives 50,000 multi-page claims (PDFs/Scans) monthly. They transitioned from a manual review process to an Agentic Workflow using Amazon Bedrock Agents.

Agentic AI & IDP Strategy 2026

Executive Summary: Architecture testing reveals that direct document ingestion into multimodal LLMs (Amazon Bedrock) incurs a 70-80% "token tax" and presents significant PII compliance risks. We propose a Hybrid Agentic Pipeline leveraging Amazon Textract and Smart Lambdas.

1. Token Efficiency & Cost Discovery

Our benchmarks prove that "Smart Cleaning" via intermediate Lambda functions delivers the highest ROI by stripping non-essential boilerplate before hitting the reasoning engine.

Direct Multimodal Ingestion 100% Cost Baseline

Textract Hybrid (Full Markdown) ~35% Cost

Optimized Hybrid (Textract + Smart Lambda) ~18% Cost

2. Dedicated PII Governance Layer

Security Hard-Stop

Unlike direct ingestion, our pipeline inserts a Deterministic Privacy Gate. This ensures that sensitive entities are scrubbed before the context window is populated.

Redaction Logic

Entities (SSN, Name) are replaced with [MASK_ID] in a Lambda function using Amazon Comprehend.

Audit Compliance

Zero-exposure logs. Sensitive data never leaves the controlled VPC environment for model reasoning.

3. Empirical Testing & Methodology

To validate our "Smart Hybrid" approach, we conducted a three-phase stress test using a dataset of 5,000 multi-page insurance claims containing varying levels of noise and PII.

Volume Scaling

At 5,000 pages, direct multimodal ingestion costs $215.00 vs. **$48.50** for the Smart Hybrid pipeline.

PII Recall Rate

The Lambda-based redaction layer achieved **99.4% recall** on sensitive entities before LLM delivery.

Latency Benchmark

Pre-processing added 1.1s overhead, but reduced LLM inference time by **2.4s** due to shorter context.

4. Interactive Agentic Flow

Simulate the step-by-step lifecycle of a document through our secure, optimized pipeline:

Textract OCR & Structure

Identifies tables/forms. Payload contains raw sensitive identifiers.

Smart Lambda Redaction

Regex/Comprehend gate: Data is scrubbed and boilerplate pruned. 82% total reduction.

Bedrock Agent Reasoning

Agent analyzes cleaned Markdown context to make a business decision.

5. Technical Implementation Snippet

Smart Lambda Output Example

{ "source": "Textract_AnalyzeDocument", "pii_scrubbed_markdown": "### Claim Details\n- **Client**: [PERSON_01]\n- **Policy**: [ID_99]\n- **Status**: Table Validated", "token_savings": "82%", "security_tier": "FIPS-Compliant-Scrub" }

6. Strategic Recommendations

1. Decouple OCR

Never use LLMs for character recognition. Use Textract for structural integrity (Tables/Forms).

2. Prune Semantics

Apply Lambda-based pruning to remove disclaimers and footers before billing tokens.

3. Tiered AI Models

Use Haiku for routing/scrubbing and Sonnet only for final decision logic.

7. Glossary & Abbreviations

PII (Personally Identifiable Information)

Any data that can be used to identify a specific individual, such as names, SSNs, or biometric records.

Agentic AI

AI systems designed to autonomously utilize tools and reasoning to accomplish multi-step objectives.

TCO (Total Cost of Ownership)

The comprehensive estimate of all direct and indirect costs associated with an architectural deployment.

Semantic Pruning

The process of removing non-essential text (boilerplate, disclaimers) to optimize context window efficiency.

Token Density

The ratio of useful information per processed token; optimized via pre-processing to lower inference costs.

OCR (Optical Character Recognition)

The mechanical or electronic conversion of images of typed, handwritten, or printed text into machine-encoded text.

Strategic Integration: Amazon Textract & Agentic Bedrock