The Enterprise Agentic AI Blueprint
How modern organizations are building secure AI agents on Microsoft Azure. Learn the architecture, security, governance and implementation patterns required to move enterprise AI agents from experimentation to production.
- Live 90-minute briefing
- Online — link emailed instantly
- For architects & tech leaders
- Azure reference architecture
Manish Kumar
Microsoft Certified Agentic AI Business Solutions Architect
Reserve your free seat
Takes 20 seconds. Access link emailed instantly.
The live Q&A room is capped so questions can actually be answered.
No catch, no card
Registration is free, your details are never sold, and one click unsubscribes you from everything. You will receive webinar reminders and relevant resources from ManishKumarAI only.
Enterprise experience across
- Banking
- Insurance
- Telecom
- Retail & E-Commerce
- Real Estate
- Mortgage
- 13+ yrs
- Enterprise practitioner experience
- 7 layers
- Enterprise blueprint framework
- 90 min
- Live architecture briefing
- Azure
- Reference architecture focus
The agent demo works. The enterprise reality is different.
A prototype answers questions. A production agent has to act inside real systems, with real identities, real permissions and real accountability.
- You have AI POCs that impress in a demo but have no path into production.
- Nobody can answer what the agent is allowed to access, or who approved it.
- Security, governance and monitoring are being bolted on after the build.
- Enterprise data, retrieval and grounding are inconsistent and untraceable.
- Leadership is asking for ROI and you have a demo, not a business case.
The next generation of AI systems will not be judged by how impressive the demo looks. They will be judged by whether the enterprise can trust them to act.
The three reveals we unpack together
Each one replaces a common assumption about agents with the way enterprise systems actually have to be designed.
The agent is not the architecture
Most teams believe the work is building the agent.
A production agent is one component of an enterprise system made of identity, data, tools, policies, governance, observability and human control. Stop asking how to build an agent and start architecting the system in which an agent can operate safely.
The failure path matters as much as the happy path
Most demos only ask whether the agent can complete the task.
Enterprises ask what happens when it is wrong, when the API fails, when the data is incomplete, or when the agent attempts an unauthorized action. Who is notified, can we stop it, and can we trace the decision?
Agentic AI is a systems problem, not a model problem
Most teams believe a better model solves the enterprise problem.
Decide what the agent should do, decide what it is allowed to do, and decide how you will know it is working. That sequence — not model choice — is what makes agents deployable.
The next architecture briefing is filling up
Registration is free and seats for the live Q&A are limited.
The missing layer between AI experimentation and enterprise deployment
Six areas that decide whether an agent stays a prototype or becomes a governed enterprise system.
Enterprise agent architecture
Understand the architecture behind production-ready AI agents, and what belongs in the agent versus deterministic workflows.
Azure reference architecture
See how Microsoft Azure services fit together across the agentic AI stack — AI platform, identity, data, integration and monitoring.
Security by design
Identity, authorization, least privilege, secrets management and secure tool access for agents that take real actions.
Governance and human control
Approval gates, policy enforcement, auditability, escalation and clear accountability for every agent in production.
Failure and observability
Design for failures, hallucination detection, evaluation, traceability, rollback and continuous monitoring.
POC to production roadmap
How to move from an AI experiment to a governed enterprise implementation with measurable business outcomes.
Who should attend (and who should skip it)
If your goal is simply to experiment with AI, there are many resources for that. If your goal is to understand how AI agents can safely operate inside a real enterprise, this briefing is for you.
You should attend if…
- You are responsible for AI adoption, architecture or transformation.
- Your organization is experimenting with GenAI or agents already.
- You have an AI POC that needs a credible production roadmap.
- You are designing Azure-based AI solutions.
- You are accountable for enterprise security, governance or standards.
- You need to communicate an AI architecture to leadership.
You should skip this if…
- You only want prompt tricks or a beginner ChatGPT walkthrough.
- You want a motivational "future of AI" talk.
- You want a coding bootcamp or a certification in 90 minutes.
- You have no interest in enterprise AI implementation.
Design trust in from day one
The old sequence was POC, demo, approval, production — with security, governance and control added late. The new sequence starts with the business outcome and the risk.
Can it answer?
Can it act safely?
Prompt
Policy
Model
Architecture
Response
Authorized decision
Accuracy
Reliability
Chat history
Audit trail
Human curiosity
Business accountability
Everything included when you register
The live 90-minute architecture briefing
IncludedThe full 7-layer Enterprise Agentic AI Blueprint, walked through layer by layer.
Live architecture walkthrough and Q&A
IncludedA representative enterprise scenario, then your questions answered live.
The 7-layer blueprint summary
IncludedA reference sheet covering intent, agent design, data, tools, security, governance and operations.
Agentic AI readiness scorecard
IncludedA 25-point checklist to assess whether your enterprise is ready for AI agents.
Your investment
90 minutes
Cost to attend
Free
How the session runs
We work through the blueprint layer by layer, then walk a representative enterprise scenario end to end.
- 00:00
Business intent and agent design
Define the business outcome first, then agent responsibilities, boundaries and decision authority.
- 00:20
Data, knowledge, tools and actions
Retrieval, grounding, permissions and traceability — plus which actions an agent may invoke.
- 00:45
Security, identity and governance
Authentication, least privilege, approval gates, policy, auditability and human control.
- 01:05
Live architecture walkthrough and Q&A
See how an enterprise agent interacts with data, tools, APIs, identity, policies and human approvals — then ask questions live.
Manish Kumar
Microsoft Certified Agentic AI Business Solutions Architect
Manish Kumar is a Microsoft Certified Agentic AI Business Solutions Architect with 13+ years of experience across data analytics, AI/ML, business process automation, Power Platform, Azure and enterprise digital transformation.
His work spans banking and financial services, insurance, telecom, retail and e-commerce, real estate and mortgage. The teaching is practitioner-led: business problem to use case, data, architecture, agents, security, governance, integration, deployment, monitoring and business outcome.
- Microsoft Certified Agentic AI Business Solutions Architect
- 13+ years across Azure, AI/ML, data and automation
- Enterprise delivery across banking, insurance, telecom and retail
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Frequently asked
AI agents are easy to build. Enterprise AI agents must be trusted.
Every Saturday at 11:00 PM IST. Don't just build an agent — build the enterprise system that makes the agent safe to act. Register free for the live briefing.