Customer Challenge

An enterprise SaaS provider needed to deliver a scalable, multi-tenant document intelligence service to large enterprise customers — each with strict data isolation requirements, diverse document types, and high processing volumes. Existing manual and semi-automated workflows were fragmented, lacked intelligent reasoning, and could not scale across multiple enterprise tenants without significant operational overhead. The customer required an agentic AI platform capable of autonomously orchestrating end-to-end document processing pipelines across isolated tenant environments, while maintaining enterprise-grade security, governance, and observability — and supporting continuous model improvement through MLflow experiment tracking and Unity Catalog-governed data management. 

Solution

Aivar designed and deployed a dual-VPC, multi-tenant agentic AI platform on AWS that separates a Control Plane (orchestration and governance) from a Data Plane (per-tenant agent execution) to achieve tenant isolation, operational resilience, and autonomous document processing at enterprise scale. A Meta-Cognition Orchestrator on Amazon EKS, powered by Amazon Bedrock, acts as the master agent — coordinating five specialised AWS Strands tools deployed in per-tenant EKS Fargate namespaces: a Document Ingestion and Classification Agent, an Intelligent Extraction and Enrichment Agent, a Validation and Quality Assurance Agent, an Output Delivery and Integration Agent writing to Delta Lake via Databricks, and an Exception Handling and Resilience Agent backed by Amazon SQS dead-letter queues and Amazon SNS. Multi-tenant isolation is enforced at the network level (namespace-scoped policies), compute level (Fargate pod isolation), and data level (tenant-specific S3 prefixes, SQS queues, DynamoDB partitions, and Unity Catalog schemas). The entire platform is provisioned with Terraform modules and deployed through AWS CodePipeline and CodeBuild with environment-specific configuration sourced from AWS Systems Manager Parameter Store; security is enforced via least-privilege IAM per tenant namespace, AWS Secrets Manager, KMS encryption, AWS GuardDuty, AWS Security Hub, AWS WAF, and CloudTrail across all accounts. 

Architecture

The architecture combines AWS container, AI, data, and security services with Databricks for analytics and model management — including Amazon EKS Fargate for per-tenant agent orchestration and tool execution, Amazon Bedrock for the Meta-Cognition Orchestrator and extraction reasoning, AWS Strands for tool coordination, Amazon SQS and Amazon EventBridge for event-driven job orchestration, AWS Batch with Spot Instance fallback for intensive workloads, Amazon RDS PostgreSQL for job state and audit, Amazon S3 for tenant-isolated ingestion and storage, Databricks with Delta Lake for ACID-compliant structured output, Unity Catalog for tenant data governance and schema isolation, MLflow for agent model versioning and experiment tracking, and Databricks SQL for tenant-facing analytics. Security and observability are delivered through AWS KMS, AWS GuardDuty, AWS Security Hub, AWS WAF, AWS Secrets Manager, CloudTrail, VPC Flow Logs, and per-tenant Amazon CloudWatch alarms and dashboards. 

Key Outcomes

• Multi-tenant scale — multiple enterprise customers onboarded on fully isolated tenant environments with zero cross-tenant data access incidents 

• Autonomous processing — end-to-end document pipeline executed autonomously with no manual intervention required for standard jobs across all tenant environments 

• >95% extraction accuracy — agentic extraction and validation workflow achieved greater than 95% structured output accuracy across diverse enterprise document types 

• Hours-to-onboard — new enterprise customer environments provisioned and live within hours using Terraform automation — compared to days with manual setup 

• Operational resilience — Resilience Agent autonomously detected and recovered from infrastructure anomalies without human escalation, maintaining platform uptime across tenants 

• Governed data plane — Unity Catalog enforced schema isolation and data governance policies across all tenant datasets with full CloudTrail audit trail 

• Model observability — MLflow tracking provided full version history and performance benchmarking for every agent model deployed across tenant environments 

• Lower TCO than dedicated infra — Databricks cluster auto-termination and Fargate per-tenant isolation eliminated idle compute cost vs. dedicated per-tenant infrastructure