Many organizations have made significant progress exploring AI—building pilots, prototypes, and proofs of concept. Yet a common challenge remains: how do you move from promising experiments to production-ready systems that are secure, scalable, and trusted? Join us for the Path to Production Webinar Series on July 27-28, a two-day deep dive designed to help technical teams operationalize AI and agent-based solutions using proven architecture patterns, governance models, and engineering practices.

This simulive event will be available in two time zones, making it easier to participate.

Use this link to register: https://aka.ms/AccelerateThePathToProduction

Why this series matters

A large percentage of AI initiatives never make it to production—not because of lack of ambition, but because organizations struggle to establish trustworthy, governed AI systems; build scalable architectural foundations; manage risk, cost, and operational complexity; and ensure reliability in non-deterministic systems. This webinar series addresses those challenges head-on with concrete, actionable guidance spanning the full lifecycle of production AI systems.

What attendees will learn

This series delivers an implementation-focused roadmap for building, deploying, and operating AI agents at enterprise scale. Each session dives deep into governance, architecture patterns, orchestration, security, evaluation, and observability - with reference architectures and real-world engineering examples. Learn how to design scalable agent systems, integrate with enterprise data and services, and apply best practices for reliability and performance. 

Attendees will leave with practical techniques and proven patterns to confidently ship production-grade agent solutions. After the workshop, customers who have Unified Contracts are eligible for a packaged set of engagements that will implement this guidance with your Microsoft cloud solution architects. Otherwise, contact your partner to learn more about taking advantage of the Frontier Transformation Offer through the Frontier Accelerate program.

Session overview

Day	Session	Focus	Speaker
July 27	Frontier Center of Excellence (CoE) & Governance	Create a governance framework with quality gates that helps organizations deliver secure, responsible, trustworthy AI at scale.	Akriti Mehta Divye Sheth
July 27	AI Landing Zones	Build a production-ready reference architecture for AI applications and agents with guardrails for networking, identity, security, and cost governance.	Nadeem Ishqair Bilal Amjad
July 27	Agentic Architecture	Adopt a governance-first, multi-agent architecture blueprint that embeds controls from user channels and orchestration through integration layers, data, and models.	Yeliz Kilinc Nour Shaker
July 28	AgentOps	Apply DevOps principles to production AI, including evaluation, CI/CD quality gates, observability, monitoring, red teaming, and incident response.	Paulo Lacerda Richard Healy
July 28	AI Security, Trust & Observability	Address prompt injection, data leakage, autonomous tool misuse, and AI-specific observability requirements for traceability, safety, and auditability.	Yuening Chen Raaid Mahbub
July 28	Solution Optimization	Reduce token cost, cut latency, tune RAG, optimize multi-agent coordination, and apply FinOps practices for sustainable scale.	Tanuja Bhamidipati Fatos Ismali

Day 1: Establishing the foundation for production AI

July 27

AI Center of Excellence (CoE) & Governance

Why do so many AI initiatives die in the PoC graveyard? Because organizations cannot trust the AI. This session shows how an AI CoE plus governance framework creates a uniform quality gate at every layer of your AI application in an organization for delivering a single, organization-wide view of secure, responsible, trustworthy AI that is ready to scale.

AI Landing Zones

Scaling AI from experimentation to production demands a secure, governed, and scalable foundation. This session explores how AI Landing Zones provide a production-ready reference architecture for deploying AI applications and agents with the right guardrails for networking, identity, security, and cost governance, aligned with the Cloud Adoption Framework and Well-Architected best practices. Attendees will learn how to design AI platforms that balance innovation with compliance, accelerate time-to-production using validated architectures and infrastructure-as-code, and integrate AI services into enterprise environments.

Agentic Architecture

Many enterprise AI pilots stall not for lack of technology, but because they lack a trustworthy architecture. This session introduces a governance-first, multi-agent architecture blueprint that closes the trust gap by embedding uniform controls and quality checks at every level, from user channels and agent orchestration through integration layers to core data and models, under a common governance and security framework. Attendees will learn how this layered agentic architecture creates a reliable, enterprise-wide AI fabric that organizations can adopt with confidence, aligning AI initiatives with high standards of trust, interoperability, and scale.

Day 2: Operating and scaling AI in production

July 28

AgentOps

This session covers the full lifecycle of deploying and operating agentic AI solutions in production. We will explore how teams can move from successful prototypes to production-ready agents using evaluation, CI/CD quality gates, observability, continuous monitoring, scheduled red teaming, and incident response practices. We will also cover how to apply DevOps principles to the unique challenges of AI systems, including non-deterministic behavior, prompt regression, model drift, tool-calling risk, and changing user behavior. Attendees will learn a practical AgentOps operating model for improving release confidence, detecting regressions earlier, and connecting agent operations back to Microsoft Foundry and Azure Monitor.

AI Security, Trust & Observability

This session focuses on securing AI systems in production, addressing risks beyond traditional application security such as prompt injection, data leakage, and autonomous tool misuse. It applies a defense-in-depth approach across identity, data protection, orchestration, and runtime controls. It also introduces AI-specific observability for trust and compliance, including traceability, safety and security monitoring, and auditability, ensuring AI systems are secure, controllable, and compliant at scale.

Solution Optimization

Getting AI to production is only half the battle. Once agentic workloads are live, organizations face compounding challenges including rising token costs, latency that degrades user trust, RAG pipelines that return noise instead of signal, and orchestration overhead that multiplies with every agent added to the mesh. This session provides a practical engineering playbook for optimizing agentic AI across the full stack, from model selection and inference routing through prompt compression, RAG tuning, caching strategies, and multi-agent coordination. It also covers the FinOps discipline required to control cost at scale, including capacity sizing, batch processing, and intelligent model routing. Attendees will leave with actionable patterns for reducing inference cost, cutting latency, and scaling reliably across regions.

Who should attend

• Cloud and solution architects
• AI and ML engineers and developers
• Platform engineering and infrastructure teams
• Technical decision-makers driving AI transformation initiatives

If your team is working to move AI beyond prototypes into production-scale systems, this series will provide directly applicable guidance for architecture, governance, operations, and optimization.

Next steps after the webinar series:

We will conduct a personalized assessment of your organization’s readiness to adopt AI agents at scale.

Call to action

Join us on July 27-28 to accelerate your path from AI experimentation to trusted, enterprise-scale production systems. Registration details can be added to this announcement before publication.

Use this link to register: Path to Production for Agents