Current enterprise strategies for Intelligent Document Processing (IDP) often rely on direct multimodal Large Language Model (LLM) ingestion. However, research indicates this approach leads to a "Token Tax" unnecessary costs generated by processing visual boilerplate. This paper proposes a Hybrid Agentic Framework that decouples structural extraction from semantic reasoning, reducing Total Cost of Ownership (TCO) by up to 82% while establishing deterministic PII governance.

Research Paper | Artificial Intelligence | 2026

Hybrid Agentic Architectures: Optimizing TCO and Privacy in Enterprise IDP

1. The Token Efficiency Benchmark

Our comparative analysis between Direct Multimodal Ingestion and the Hybrid Agentic approach demonstrates a significant correlation between pre-processing "Smart Cleaning" and inference cost reduction.

Avg. Token Reduction

82.4%

Cost Savings per 10k Pages

$330.00+

PII Recall Rate

99.4%

2. Architectural Pattern: Live Execution Trace

Structural Extraction (OCR/Query)

Utilizing deterministic tools (e.g., Amazon Textract) to map forms and tables into high-fidelity Markdown.

Deterministic Privacy Gate

Intermediate Lambda scrubbing: PII is masked and semantic noise is pruned within a secure VPC perimeter.

Agentic Reasoning (LLM)

An Agent (e.g., Claude 3.5 Sonnet) analyzes the clean, secure context window to execute complex business logic.

3. Industry Use Case: Automated Claims Triage

In high-volume document environments, this architecture transforms the cost-to-value ratio by automating the triage of incoming records.

Operational Metric	Traditional Manual Triage	Hybrid Agentic IDP
Cost per Interaction	$10.00 - $15.00	$0.15 - $0.45
Triage Latency	24 - 48 Hours	< 60 Seconds
Security Protocol	Manual Exposure Risk	Automated Masking

4. Strategic Recommendations

Utilize serverless state machines to manage the IDP lifecycle. Standardize deployments using Terraform to ensure architectural integrity and tiered model dispatching for cost control.

5. Glossary of Terms

Agentic IDP

Intelligent Document Processing driven by autonomous AI agents that select specialized tools for extraction and reasoning.

Token Tax

The unnecessary inference cost incurred by processing redundant visual data, boilerplate, or unpruned document sections.

Deterministic Masking

A rule-based or high-accuracy ML process that replaces PII with tokens before data enters a non-deterministic LLM environment.

RAG (Retrieval-Augmented Gen)

An architectural pattern that optimizes LLM output by referencing an authoritative, private knowledge base before generating responses.

Semantic Pruning

The process of removing contextually irrelevant text (headers, footers, disclaimers) to optimize context window efficiency.

TCO (Total Cost of Ownership)

A financial estimate intended to help buyers and owners determine the direct and indirect costs of a technological deployment.

6. Technical Implementation Example

{
  "document_strategy": "Agentic_IDP_v2026",
  "data_privacy": "Deterministic_PII_Masking",
  "efficiency_metrics": {
    "token_optimization": "82%",
    "compliance_tier": "Enterprise_Grade"
  }
}
    