Agentic Automation Platform for Document-Intensive Workflows

Overview

The project involved architecting a greenfield agentic AI platform that automates the end-to-end processing of high-volume, document-heavy business cases for a regulated enterprise. A supervisor-style agent graph routes each case through a set of specialist agents that handle ingestion, enrichment, validation, coordination, and resolution, replacing manual review queues while keeping a human-in-the-loop checkpoint on high-stakes transitions. The agent layer sits on top of a cloud-native Databricks data platform with Unity Catalog governance, declarative streaming ingestion from an object-store landing zone, and a multi-region, multi-tenant infrastructure baseline.

Achievements

Shipped the supervisor and specialist agents into production with durable state checkpointing, full execution tracing, and a repeatable end-to-end scenario suite covering happy paths and edge cases. Delivered the underlying Databricks platform as a reusable blueprint that subsequent internal business units onboarded against shared infrastructure modules rather than greenfield environments. Established strictly validated data contracts between agents so that malformed or incomplete messages are caught at the boundary and never propagate through the graph.

Responsibilities

• Designed the supervisor-and-specialist agent topology and the typed contracts exchanged between agents, with versioned schemas validated at every inter-agent boundary.
• Architected the Databricks data platform on a major public cloud: regional metastore provisioning, per-tenant workspaces for non-production and production, declarative streaming ingestion with quality expectations derived from schema definitions, and a quarantine path for records failing validation.
• Built the infrastructure-as-code hierarchy using Terraform and a dependency-orchestration layer, organised by region, business domain, environment, and stack, with reusable modules for metastore, workspace, catalog/schema/external location, and declarative permissions.
• Developed an internal data-engineering framework that wraps pipeline tasks with standardised configuration loading, logging, and schema enforcement, so that engineers author pipelines declaratively rather than by assembling Spark primitives.
• Implemented tenant isolation across storage (prefix/bucket partitioning), encryption (per-tenant keys), metadata (tagging for governance and cost allocation), and configuration (runtime-resolved rather than compile-time coupled).

Technologies Used