Agentic AI Platform Series
How to Deploy Enterprise Agentic AI Across Azure, AWS, and Google Cloud
Architecture and operational discipline are necessary conditions. This final paper addresses the most consequential question: how does an organization actually get to a governed, scalable agentic capability that delivers measurable business value? A phased roadmap, a concrete reference use case, and cloud implementation patterns for Azure, AWS, and GCP — all in one document.
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Why Agentic AI Programs Struggle to Reach Production
Alexey Girzhadovich
Chief Enterprise AI and Solutions Officer
Pilots are scaled before controls exist
Most agentic programs fail because the delivery approach is wrong. Automation is added faster than quality can be proven, and authority is expanded based on confidence rather than demonstrated readiness.
The result is a program that cannot survive its first incident.
Alexey Girzhadovich
Chief Enterprise AI and Solutions Officer
Cloud implementation creates different architectures
Azure, AWS, and GCP each offer distinct strengths, but the same business outcomes still require a common blueprint.
Without one, deployments evolve independently, governance expectations drift, and what should be one enterprise program becomes three separate initiatives with little shared evidence or reuse.
Alexey Girzhadovich
Chief Enterprise AI and Solutions Officer
Audit readiness is treated as a final step, not a design principle
The question that matters is not whether the model is safe — it is whether the organization can prove who acted, what was accessed, what was approved, and what changed.
Programs that design evidence generation from the start are the ones that scale safely.
Agentic AI Platfrom Series
What You’ll Get in the Whitepaper
A phased implementation roadmap with explicit entry and exit criteria
Five phases from assessment to scaled operations — each with clear gates, measurable outcomes, and the rule that authority increases only when evidence demonstrates readiness.
A concrete reference use case showing governed autonomy end-to-end
A step-by-step walkthrough from intent capture through controlled actuation and evidence production — with controls, HITL gates, and safe escalation built into every step.
Cloud implementation patterns for Azure, AWS, and GCP
How to realize the same enterprise blueprint on each major hyperscaler — with service-level mappings, distinctive strengths per platform, and portability guidance that keeps the architecture consistent across clouds.
Security, risk, compliance, and responsible AI oversight
Policy-gated access, data handling controls, change discipline, incident response, and the audit evidence requirements that make enterprise-scale agentic AI defensible under scrutiny.
Phase 00
Assess
Prioritized backlog + target architecture
Phase 01
Foundation
Pilot-ready platform
Phase 03
Supervised Agents
Measurable productivity + quality
Phase 04
Controlled Automation
Scaled operations
Phase 04
Scale and Optimize
Predictable performance at scale
Cloud Implementation — Azure, AWS, and GCP
The same enterprise blueprint realized on each major hyperscaler. The architectural contracts stay stable — authority levels, tool governance, audit trails, and eval release discipline. The managed services change.
Azure
Strong fit for: Microsoft identity estates and regulated environments
Key services: Foundry Hosted Agents + Durable Functions · Cosmos DB (session) + Foundry IQ (vector) · Entra ID + Azure AI Gateway + MCP Servers · Content Safety + Monitor + Cost Management
AWS
Strong fit for: Event-driven integration and explicit workflow control
Key services: Bedrock Agents + Step Functions + Lambda · Bedrock Memory + Knowledge Bases + OpenSearch · AgentCore Gateway + IAM + Bedrock Guardrails · CloudWatch + Budgets
GCP
Strong fit for: Analytical grounding and decentralized multi-agent models
Key services: Vertex AI Agent Engine + Workflows + Cloud Run · Agent Engine Sessions + Vertex AI Search + BigQuery · MCP + A2A protocol + Cloud Functions · IAM + VPC-SC + Model Armor + Cloud Trace
Who This Is For
Written for Teams Delivering Enterprise Agentic AI
This whitepaper is for CTOs, VPs of Engineering, AI Platform Leads, Enterprise Architects, and Heads of Cloud or Platform Engineering at organizations with active agentic AI programs ready to move from operational foundations to production deployment. It assumes familiarity with Whitepapers 1 and 2 and addresses the delivery, cloud, security, and compliance decisions that determine whether a program reaches governed scale.
This is the final paper in a three-part series. Whitepaper 1 covers Architecture & Governance. Whitepaper 2 covers Operational Excellence.
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VP Engineering
AI Platform Lead
Enterprise Architect
Head of Cloud
Head of Platform Engineering
Authored by Exadel's Senior AI Architecture and Solutions Leaders
Alexey Girzhadovich
Chief Enterprise AI and Solutions Officer
Section title
Defines AI strategy for Exadel’s clients and translates it into client outcomes across industries. Lead voice behind the Agentic AI Platforms whitepaper series.
Iliyan Nenov
Director of Solution Architecture & Strategy, Exadel
Section title
Leads solution architecture at Exadel, working with enterprise clients on the transition from AI experimentation to governed, production-scale deployment.
Dzmitry Pauliu
Solution Architect, Exadel
Section title
Specializes in enterprise AI platform design and the structural patterns that allow agentic systems to scale safely in production environments.
Andres Naranjo
Solution Architect, Exadel
Section title
Focused on enterprise AI platform delivery and integration architecture, ensuring governance frameworks hold up under real enterprise conditions.
Free download
Download the Free Whitepaper
The complete implementation guide for enterprise agentic AI — phased roadmap, reference use case, implementation accelerators, cloud deployment patterns for Azure, AWS, and Google Cloud, and security and compliance framework. All in one document.
Recognized by the industry
Clutch Awards 2024
Techbehemoths
AWS Advanced Partner - 2022
Adobe AEM Sites Specialization — Americas
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