Azure · AWS · Snowflake · Agentic AI · Governance · Digital Modernization
I started as a software engineer writing ERP code. Over 18 years I grew into someone who sits at the intersection of business strategy and AI engineering — leading programs that don't just move data from A to B, but make organizations fundamentally smarter. Today I design Agentic AI systems — multi-agent architectures that make autonomous decisions at machine speed, with the governance and security frameworks that make executives comfortable enough to trust them.
"What if the gap between a customer's click and warehouse execution was under 5 seconds — fully autonomous?"
A production-grade multi-agent AI system designed for omnichannel retail (Walmart use case).
| Agent | Role | Key Challenge Solved |
|---|---|---|
| 🔍 The Auditor | Real-time ATP inventory verification | Race conditions — millisecond soft-locking |
| 🗺️ The Router | Node selection (SLA + cost + capacity) | Stale WMS data — data freshness thresholds |
| 🛡️ The Watchdog | Governance, policy enforcement, audit | Hallucinations — blocks bad decisions before they reach the warehouse |
The numbers:
- ⏱️ Order-to-fulfillment: 8–15 min → <5 seconds
- 💰 Projected savings: $5M+/year on $50M fulfillment base
- 🎯 Shadow Mode gate: 95%+ Decision Alignment Rate before any live action
- 📋 OTIF delivery target: 88% → 95%+
Tech: Amazon Bedrock (Claude Sonnet) · EventBridge · IBM Cloud Pak for Data · Azure Purview · AWS API Gateway
"Google shelved its AI trip planner. We built ours."
A 6-agent AI travel product that turns messy multi-tab trip planning into one guided flow.
User inputs preferences
↓
Orchestrator
┌──────────────────────────────────┐
│ 🔍 Research Agent │ → Fetches real prices (no tracking bias)
│ 💰 Budget Agent │ → Locks true total cost upfront
│ 📅 Booking Agent [HUMAN GATE] │ → Human approves before booking
│ 🗺️ Itinerary Agent │ → Conflict-free schedule building
│ 📍 Context Agent │ → Hyperlocal recommendations
│ 🔔 Monitor Agent │ → Pre-trip alerts & rebooking
└──────────────────────────────────┘
↓
Itinerary + bookings + budget dashboard + alerts
Grounded in real pain: 10 validated user pain points from MakeMyTrip, TripAdvisor, and Google Flights real consumer reviews (ConsumerAffairs, Skift, SmartCustomer sources).
Design principle: "Human control for risky steps. Automation for repetitive steps. Always explainable."
"What if 67 counties × 10 years of government property tax reports could be analyzed by AI in minutes — without the data ever leaving your secure environment?"
An 11-module automated document intelligence pipeline for Florida Department of Revenue.
-- The magic in one line:
SELECT SNOWFLAKE.CORTEX.COMPLETE(
'claude-4-sonnet',
CONCAT('Is this county IN COMPLIANCE with IAAO standards? ',
'Answer: COMPLIANT, NEEDS_REVIEW, or NON_COMPLIANT. ',
'Document: ', text_content)
) AS compliance_status
FROM parsed_documents;What it does:
- 📄 Parses Word/PDF documents using
SNOWFLAKE.CORTEX.PARSE_DOCUMENT(OCR + Layout modes) - 🤖 Runs AI compliance checks against IAAO standards (COD/PRD thresholds) using Claude Sonnet
- 📊 Classifies property study types using
AI_CLASSIFY - 📝 Auto-generates executive summary memos for county officials
- 🔒 Data never leaves Snowflake's security boundary
Security analysis I wrote: Identified 5 critical gaps for production government use:
| Gap | Risk | Fix |
|---|---|---|
| No PII masking | Raw tax data visible to all role users | Dynamic data masking policy on text_content |
| No network policy | Access from anywhere in the world | Agency IP allowlist + private link |
| No MFA enforcement | Single password = full access | Account-level MFA policy |
| Incomplete audit logging | Can't prove who saw what | Access History + Object Tagging + SIEM |
| No AI model governance | Compliance labels with no version control | Model version logging + human review gate |
Plus: Identified model drift as a data integrity risk in government AI — proposed model versioning strategy for reproducible audit trails.
AGENTIC AI CLOUD PLATFORMS PROGRAM DELIVERY
─────────── ─────────────── ────────────────
Multi-agent AWS: Glue, Lambda, End-to-end PM
architecture S3, Step Functions, (initiation → closure)
EventBridge, IAM
LLM orchestration Agile / Waterfall /
(Bedrock, Cortex, Snowflake: RBAC, Hybrid
Azure AI) Cortex, Data Sharing,
Resource Monitors RAID logs
Shadow Mode Vendor management
deployment Azure: Purview,
Data Lake, ADF, Executive reporting
Prompt engineering Synapse & stakeholder comms
& guardrails
OCI: Oracle Cloud HIPAA · GDPR · CCPA
AI governance Infrastructure FISMA · SOC 2
frameworks compliance
- "Our data exists but nobody trusts it" → I build lineage, governance, and observability layers
- "We know AI could help but we don't know where to start" → I design POCs that prove value in weeks
- "Our processes are too slow for the speed AI enables" → I redesign workflows around autonomous agents
- "Leadership won't approve AI without seeing the guardrails" → I build the governance framework that makes them comfortable
I have bandwidth for AI POC collaborations — free or paid.
If your team is asking "can AI actually solve this?" — let's find out together.
- 🔗 LinkedIn — Ashok Ankalla
- 📧 Reach out via LinkedIn DM
18 years of enterprise delivery. 3 Agentic AI projects. 1 simple goal: make organizations faster and smarter.