Agentic AI Architecture Patterns for Document Extraction

Agentic AI systems combine autonomous agents, orchestration logic, and guardrails to automate document-heavy workflows while keeping humans in control. Drawing on official AWS Prescriptive Guidance for agentic patterns and established architecture practices from Designing Data-Intensive Applications (Kleppmann, O’Reilly) and Building Evolutionary Architectures (Ford et al., O’Reilly), this article outlines a production-ready pattern for extracting, enriching, and validating documents.

Core Pattern Overview

The workflow blends deterministic steps with LLM-driven agents:

1. Ingestion & Storage: Documents arrive via S3, versioned, and tagged with metadata. EventBridge raises an event per drop.
2. Classification Agent (Bedrock Agent over Claude/Sonnet): Routes documents to the right policy based on schema expectations and business rules.
3. Extraction Agent: Uses Amazon Textract for structured primitives, then LLM post-processing to normalize fields (dates, amounts, parties).
4. Validation & Grounding: Cross-check extracted values against a system of record (RDS/Aurora or DynamoDB) with retrieval-augmented prompts. Confidence scores determine automatic acceptance vs. human review.
5. Enrichment: Adds derived facts (totals, payment terms, key entities) and emits knowledge graph facts into Neptune or OpenSearch vectors for downstream search/RAG.
6. Human-in-the-loop: Bedrock Knowledge Bases + AppSync or Amazon Q Business UI for review/override; feedback is logged to S3 + DynamoDB for continual improvement.
7. Observability & Governance: CloudWatch metrics/traces, model usage audit (Bedrock InvokeModel logs), and drift checks using canary documents.

High-Level Flow

User/SaaS -> S3 (ingest) -> EventBridge -> Step Functions (orchestration)
   -> Bedrock Agent (classification) -> Textract -> Bedrock LLM (field normalization)
   -> Validation Lambda (RDS/DynamoDB checks) -> Enrichment Lambda
   -> SQS "needs-review" queue -> AppSync/QUX UI
   -> Approved docs -> DynamoDB/RDS + OpenSearch/Neptune
   -> CloudWatch/CloudTrail/Audit logs

Design Principles

• Separation of concerns: Deterministic parsing (Textract) before probabilistic reasoning (LLM). Mirrors data pipeline best practices in Designing Data-Intensive Applications.
• Policy-driven agents: Bedrock Agents tools restrict actions to approved APIs, minimizing prompt injection risk.
• Idempotent orchestration: Step Functions with execution IDs; S3 object version IDs allow safe retries.
• Structured outputs: JSON schemas validated with ion-schema or jsonschema to keep LLM responses contract-bound.
• Feedback loops: Human corrections captured and replayed as few-shot exemplars; monitored with CloudWatch + Evidently for A/B of prompt versions.

Reference Architecture on AWS

• Storage & Events: Amazon S3, EventBridge, SQS DLQ
• Orchestration: AWS Step Functions (callback pattern for human review), AWS Lambda for light transforms
• Extraction: Amazon Textract for OCR + forms/tables, Amazon Bedrock (Claude) for semantic cleanup
• Tools for Agents: Secure Lambda endpoints for data lookup/update; IAM-scoped execution roles
• Persistence: DynamoDB (extractions, lineage), RDS/Aurora (system-of-record validation), OpenSearch vectors (semantic retrieval), Neptune (graph edges)
• Security & Compliance: KMS for all at-rest encryption, VPC endpoints for Bedrock/Textract, CloudTrail for API audit, automatic PII redaction in prompts

Implementation Checklist

• Define a document policy catalog (YAML/JSON) that maps doc types to required fields, validation rules, and escalation criteria.
• Create prompt templates that include: task, tools list, schema, allowed values, and “must cite source span” constraints.
• Use structured logging per document ID across Lambda/Step Functions to enable traceability.
• Establish confidence thresholds (e.g., Textract + LLM combined score) that route to human review when below target.
• Automate golden-doc regression: nightly run through Step Functions with held-out samples; diff outputs and alert on drift.

Sources

• AWS Prescriptive Guidance: Agentic AI patterns
• Martin Kleppmann, Designing Data-Intensive Applications (O’Reilly)
• Neal Ford et al., Building Evolutionary Architectures (O’Reilly)