Manoj Annavajjala
Enterprise AI Architect
Detroit, Michigan, United States
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I’m Manoj Annavajjala, a Power Platform and Enterprise Architecture leader specializing in designing secure, scalable, and AI-ready solutions across the Microsoft ecosystem.
With over 15 years of hands-on experience spanning Power Platform, SharePoint, Azure, and Microsoft Fabric, I focus on platform governance, enterprise automation, and AI enablement using Power Apps, Power Automate, Copilot Studio, and Azure AI Foundry. My work centers on building architectures that balance innovation with control ensuring security, compliance, and long-term sustainability at scale.
I’m passionate about simplifying complex administrative, integration, and ALM challenges, enabling both makers and IT teams to deliver solutions confidently and responsibly in the Microsoft cloud.
As the creator of the Power Platform Engineer community, I actively share real-world insights, architecture patterns, and governance best practices to foster collaboration, elevate platform maturity, and support continuous learning.
Area of Expertise
Topics
From Complexity to Clarity: Powering Intelligent Data Understanding with Microsoft Fabric Data Agent
Unlock real-time, intelligent data access through natural language and automation. How Microsoft Fabric Data Agents enable more intelligent and controlled data experiences.
Empowering secure, intelligent interactions with your enterprise data without writing a single line of code.
From Raw Data to Business Answers Without Writing SQL
Most business users sit one question away from a decision, but they depend on a data analyst to get there. That bottleneck costs time, slows decisions, and frustrates both sides.
This session shows how Microsoft Fabric Data Agent changes that equation. You will see a live demo where procurement managers, operations leads, and finance teams query enterprise data in plain English and get accurate, context-aware answers in seconds. No SQL. No waiting.
You will learn how to configure a Fabric Data Agent against real enterprise datasets, train it with natural language to SQL examples, and deploy it inside Microsoft Teams through Copilot Studio. The session covers what makes agents fail in production, how schema design and column descriptions carry more weight than most teams expect, and how to govern agent behavior at scale.
This is not a product overview. It is a production-tested playbook built from real enterprise deployments.
Why Solution Deployments Fail and Where Copilot Helps You Catch Problems Earlier
Most deployment failures trace back to missing dependencies, environment variable mismatches, or connection reference gaps. These are detectable before deployment if you know where to look. This session covers the most common solution management failure points, how Copilot assists in reviewing component configurations, generating pre-deployment checklists, and interpreting error output. You leave with a repeatable review process you can apply to your next release.
MCP + Copilot Studio: Building Enterprise AI Agents That Connect to Everything
Model Context Protocol (MCP) is fast becoming the standard way to connect AI agents to enterprise systems, tools, and data. But what does it mean for Microsoft Copilot Studio developers and architects?
This demo-driven session shows how MCP gives agents a standard way to discover and work with business systems. We'll cover why Microsoft adopted MCP, how it works with Copilot Studio, Azure AI Foundry, Dataverse, and SharePoint, and what architects need to know before going to production.
In live demos, we'll build a Copilot Studio agent that talks to multiple MCP servers to pull business data, run actions, and orchestrate workflows. Along the way we'll cover authentication, governance, security, and deployment.
Whether you're building your first enterprise agent or modernizing existing Copilot solutions, you'll leave with practical guidance for designing AI architectures that are scalable, secure, and ready for what's next.
What Happens When Your Team Starts Using Copilot Without a Plan
Turning on Copilot without a clear approach creates more work, not less. Inconsistent apps, unreviewed flows, and support issues follow. This session covers what goes wrong in the first 30 days, what actions separate productive teams from ones that stall, and how to build a repeatable pattern for Copilot-assisted delivery in Power Apps and Power Automate.
How Copilot Cuts App and Flow Build Time in Half
Building from scratch takes time most projects do not have. This session shows how Copilot generates a working canvas app from a plain-language prompt, creates Power Automate flows from a description, and writes expressions that used to take 20 minutes to debug. You will see the full cycle from prompt to tested output, with clear markers on where you still need to review before deploying.
From SharePoint Documents to Enterprise AI: Building a Secure Knowledge Agent with Microsoft Copilot
Many organizations already have years of valuable knowledge stored in SharePoint, but turning that content into a trusted AI experience requires more than simply connecting a document library.
In this session, we'll walk through how to build an enterprise knowledge agent using Microsoft Copilot Studio and SharePoint while maintaining security, governance, and content quality.
We'll cover:
Choosing the right SharePoint content for AI grounding
Structuring knowledge libraries for better AI responses
Security trimming and permission-aware access
Common retrieval and citation challenges
Managing document freshness and knowledge lifecycle
Deploying the agent using Power Platform ALM
Monitoring usage and continuously improving responses
You'll see practical demonstrations and real-world lessons learned from implementing enterprise knowledge assistants that employees can trust.
How Copilot Changes the Way You Work With SharePoint Every Day
Most SharePoint users search, scroll, and dig through folders to find what they need. Copilot changes that pattern. This session shows how Copilot surfaces content from SharePoint sites, summarizes documents without opening them, and answers questions grounded in your actual organizational data. You will see live demos across document libraries, lists, and site pages, with a clear picture of what Copilot needs to work well and where it still falls short.
From Proof of Concept to Production: Lessons Learned Building Enterprise AI Agents
Building an AI proof of concept is easy.
Operating AI agents in production is where the real engineering begins.
This session shares practical lessons learned from designing, deploying, and supporting enterprise AI agents using Microsoft Copilot Studio, Power Platform, Microsoft 365, and Azure AI.
Rather than focusing on product features, we'll examine the real-world challenges encountered during production deployments, including:
Data quality and retrieval challenges
AI hallucination mitigation
Environment strategy and deployment pipelines
Security and governance guardrails
Monitoring usage and measuring business value
Operational ownership and continuous improvement
Attendees will see architecture diagrams, deployment patterns, governance approaches, and production lessons that can immediately improve their own AI initiatives.
Data Ingestion Does Not Have to Be Hard: Three Approaches Inside Microsoft Fabric
Microsoft Fabric gives you three ways to ingest data: Dataflow Gen2, Python notebooks, and the Copy Data activity. All three work. Choosing the wrong one creates rework.
This session applies all three to the same scenario, pulling data from a SQL database, a REST API, and ADLS-hosted files, then landing it in a single Fabric destination. You will see live configuration, real code, and pipeline orchestration, not diagrams.
You will leave with a decision framework that tells you when each tool earns its place. Dataflow Gen2 for low-code analyst scenarios. Python notebooks when you need full control. Copy Data when speed and simplicity matter more than flexibility. A Fabric Data Pipeline ties all three into one orchestrated flow.
Beyond Dashboards: Building AI Data Analysts with Microsoft Fabric, Power BI, and Copilot Studio
Power BI dashboards answer questions about yesterday. Enterprise AI agents help answer questions about right now.
In this session, we'll explore how organizations can extend Microsoft Fabric and Power BI with Microsoft Copilot Studio to create AI-powered data analysts that understand business context, answer natural language questions, retrieve supporting documentation, and automate follow-up actions.
Using a real-world architecture, we'll demonstrate how structured data, semantic models, and enterprise knowledge can work together to provide trustworthy AI experiences.
Topics include:
Connecting Copilot Studio with Microsoft Fabric and Power BI
Using semantic models to improve AI responses
Combining structured data with enterprise documentation
Triggering Power Automate workflows from AI conversations
Security trimming and governance considerations
Lessons learned from production implementations
Attendees will leave with a practical architecture they can adapt to their own organizations and a clear understanding of where AI agents complement, not replace, traditional dashboards.
Building Enterprise AI Employees with Microsoft Copilot Studio
AI has evolved beyond answering questions. The next generation of enterprise AI is built around intelligent business agents that can reason, retrieve enterprise knowledge, execute tasks, and collaborate across Microsoft 365.
Through a live end-to-end demonstration, you'll see an AI employee retrieve information from Outlook, SharePoint, Planner, Dataverse, and Azure AI knowledge sources to prepare executive project status reports, identify risks, create follow-up tasks, and assist knowledge workers.
Beyond Dashboards: Building AI Data Analysts with Copilot Studio and Microsoft Fabric
ower BI dashboards help users understand what happened.
But what if users could simply ask questions in natural language, receive contextual answers, trigger business actions, and explore enterprise data through AI agents?
In this session, we'll build an enterprise AI Data Analyst using Microsoft Copilot Studio and Microsoft Fabric.
We'll explore how AI agents can:
Answer business questions using enterprise data
Retrieve insights from Microsoft Fabric and Power BI semantic models
Combine structured and unstructured knowledge
Trigger Power Automate workflows from conversations
Provide citations and trusted responses
Apply governance and security for enterprise deployments
You'll also learn where Copilot Studio fits alongside Power BI, Fabric, Azure AI, and Microsoft 365 Copilot, along with practical architecture patterns and lessons learned from enterprise implementations.
Beyond Chatbots: Building AI Employees with Copilot Studio
Organizations everywhere are deploying chatbots, but the real opportunity is building AI employees that can perform work, not just answer questions.
In this session, you'll learn how to build a Copilot Studio agent that acts like a digital team member by connecting to Microsoft 365 data and business systems. We'll go beyond traditional Q&A scenarios and demonstrate how an AI employee can search emails, review Planner tasks, gather project updates from SharePoint, and generate executive-ready status reports in minutes.
Through a live end-to-end demonstration, you'll see how a manager can ask a simple question such as, "Prepare my weekly project status report," and have the agent collect information from multiple sources, summarize risks, identify action items, and provide a polished response.
Whether you're a business analyst, citizen developer, or solution architect, you'll leave with practical patterns you can use immediately to create AI-powered assistants that help teams work faster and make better decisions.
This session focuses on real-world business scenarios, live demonstrations, and actionable techniques you can implement as soon as you return to the office.
How We Built and Governed a Production AI Agent with Copilot Studio
Moving AI agents from proof-of-concept to production requires much more than simply publishing a bot.
In this session, we’ll walk through a real-world enterprise approach to building, deploying, governing, and supporting production AI agents using Microsoft Copilot Studio and Power Platform.
Topics include:
Environment strategy (DEV / QA / PROD)
Solution-based ALM and deployment pipelines
Governance and DLP policies
Security and sharing controls
Service account and connection strategies
Production readiness validation
Monitoring and operational support considerations
Attendees will see practical implementation patterns, deployment strategies, and lessons learned from enterprise AI adoption initiatives.
Whether you’re a Power Platform administrator, architect, or developer, this session will provide actionable guidance to help you move from AI experimentation into scalable enterprise deployment.
Your AI Agent Worked in Dev. Then Production Happened.
Everyone has an AI agent demo. Very few teams successfully operationalize them.
This session explores the operational, architectural, and governance failures that emerge when enterprise AI agents move from proof-of-concept into real-world production environments.
We’ll walk through what actually breaks at scale:
RAG quality degradation
Deployment and environment promotion failures
Agent publishing inconsistencies
Identity and connection management issues
Hallucinated links and retrieval edge cases
Governance friction vs developer velocity
Security and sharing risks
Why successful demos often fail operationally
Attendees will also learn practical mitigation strategies, architecture patterns, and operational guardrails for sustainable enterprise AI systems.
5 Mistakes Organizations Make When Deploying Copilot Studio Agents
Organizations are rapidly adopting Microsoft Copilot Studio to build AI-powered agents, but many deployments struggle once they move beyond proof-of-concept into enterprise environments.
In this session, we’ll explore five of the most common mistakes organizations make when deploying Copilot Studio agents and how to avoid them.
Topics include:
Poor environment and ALM strategy
Weak governance and DLP controls
Overexposed sharing and security risks
Production deployment challenges
Missing operational ownership and support planning
Attendees will also learn practical approaches for:
DEV / QA / PROD environment management
Secure deployment pipelines
Agent lifecycle management
Production readiness validation
Governance guardrails that enable innovation without slowing developers down
This session is based on real-world enterprise implementation experience and lessons learned deploying AI agents with Microsoft Power Platform and Copilot Studio.
How We Built and Governed a Production AI Agent with Copilot Studio
Organizations everywhere are building AI agents with Microsoft Copilot Studio but moving from proof-of-concept to a secure, production-ready deployment is where most teams struggle.
In this session, I’ll walk through a real-world enterprise approach to building, deploying, governing, and supporting AI agents using Copilot Studio and Power Platform.
We’ll cover:
Environment strategy (DEV / QA / PROD)
Solution-based ALM and deployment pipelines
Governance guardrails and DLP policies
Security and access management
Service account and connection strategies
Production readiness validation
Monitoring, support, and operational lessons learned
You’ll also see practical demonstrations and architecture patterns that can help your organization move beyond AI experimentation into scalable enterprise adoption.
Attendees will leave with actionable guidance, governance ideas, and deployment strategies they can apply immediately in their own environments.
What Actually Breaks When Enterprise AI Agents Reach Production
Everyone has an AI agent demo.
Very few teams survive production.
This talk explores the operational, architectural, and governance failures that emerge when enterprise AI agents move from proof-of-concept into real-world deployment environments.
Drawing from hands-on experience deploying enterprise agents with Microsoft Copilot Studio, SharePoint retrieval systems, Dataverse integrations, deployment pipelines, and multi-environment governance models, we’ll walk through what actually breaks at scale.
Topics include:
Why RAG quality silently degrades in enterprise content systems
Environment promotion and deployment failures
Agent publishing inconsistencies across channels
Identity and connection management problems
Hallucinated links and retrieval edge cases
Governance friction vs developer velocity
Security and sharing failures in enterprise environments
Human escalation gaps
Why “successful demos” fail operationally
We’ll also cover mitigation strategies, architecture patterns, and practical operational guardrails for enterprise AI systems.
This session is intended for AI engineers, architects, platform owners, and technical leaders building production-grade AI agent systems.
5 Mistakes Organizations Make When Deploying Copilot Studio Agents
Organizations are rapidly adopting Microsoft Copilot Studio to build AI-powered agents, but many deployments struggle with governance, security, scalability, and operational readiness once they move beyond proof-of-concept.
In this session, we’ll explore five of the most common mistakes organizations make when deploying Copilot Studio agents in enterprise environments and how to avoid them.
Topics include:
Poor environment and ALM strategy
Weak governance and DLP controls
Overexposed sharing and security risks
Production deployment challenges
Missing operational ownership and support planning
Attendees will also learn practical approaches for:
DEV / QA / PROD environment management
Secure deployment pipelines
Agent lifecycle management
Production readiness validation
Governance guardrails that enable innovation without blocking makers
This session is based on real-world enterprise implementation experiences and lessons learned from deploying and governing AI agents using Microsoft Power Platform and Copilot Studio.
Attendees will leave with actionable guidance they can immediately apply to their own AI and automation initiatives.
How We Built and Governed a Production AI Agent with Copilot Studio
Organizations are rapidly adopting Microsoft Copilot Studio to build AI-powered agents, but moving from proof-of-concept to a secure, production-ready deployment is where many teams struggle.
In this session, I’ll walk through a real-world enterprise approach to building, deploying, governing, and supporting AI agents using Microsoft Copilot Studio and Power Platform.
We’ll cover:
Environment strategy (DEV / QA / PROD)
Solution-based ALM and deployment pipelines
Governance guardrails and DLP policies
Security and access management
Service account and connection strategies
Production readiness validation
Monitoring, operational support, and lessons learned
Attendees will also see practical architecture patterns and deployment approaches that help organizations move beyond AI experimentation into scalable enterprise adoption.
Whether you’re a Power Platform administrator, architect, developer, or business technology leader, this session will provide actionable guidance you can immediately apply within your own Copilot Studio and Power Platform environments.
Model-Driven Apps Reimagined: Generative Pages and Agents in Real Enterprise Workflows
Model-driven apps stay central for enterprise scenarios due to security, data integrity, and lifecycle control. Adoption slows when experiences feel rigid and task driven. This session shows how generative pages and Copilot agents change that equation. You see how to layer AI-driven experiences on top of Dataverse without breaking governance. The session walks through a real implementation where agents assist users with record creation, summarization, decision support, and guided actions inside a model-driven app. Generative pages reduce form complexity while agents handle intent, context, and next steps. Outcomes include faster task completion, higher user adoption, and reduced customization debt. You leave with an architecture pattern, design rules, and a checklist to decide where generative UI and agents add value versus where classic forms still win.
Building Production-Ready AI Agents with Copilot Studio: From Idea to Governed Deployment
AI agents are quickly becoming a core part of how organizations automate work, assist users, and surface knowledge but building an agent that actually works in production requires more than just prompts.
In this hands-on workshop, attendees will learn how to design, build, secure, and deploy AI agents using Copilot Studio and the Power Platform, with a strong focus on real-world enterprise patterns. We’ll start by breaking down what makes an AI agent effective intent design, grounding with trusted data, orchestration with Power Automate, and responsible AI controls. Participants will then build a functional agent step-by-step, integrating Microsoft 365 data, business workflows, and guardrails such as environment strategy, DLP, and governance.
We’ll also cover how to move beyond a “demo agent” by applying ALM best practices, environment promotion, and monitoring so agents can safely scale across teams and departments. This workshop is designed for makers, admins, and architects who want practical skills they can apply immediately, not theory.
Attendees will leave with a clear blueprint for building AI agents that are useful, secure, and ready for real users.
Governance for MCP Action Builders: How Admins Can Monitor, Audit, and Control Copilot Extensions
As organizations extend Copilot Studio with MCP Actions, admins need clear guardrails to prevent data leakage, ensure least‑privileged access, and keep extensions observable and auditable. This session gives administrators and platform owners a practical governance playbook: how to inventory MCP actions, enforce approvals, monitor usage, audit conversations and action calls, and quickly respond to incidents.
We’ll map the lifecycle of an MCP Action from dev registration to production rollout and show where to place controls: environment strategy, solution ownership, DLP boundaries, API Management policies, authentication and scopes with Entra ID, and logging/auditing patterns. You’ll see exactly which logs to collect, how to trace an action call from Copilot to the backing API, and how to detect risky prompts or over‑permissive actions. We’ll also cover change control, versioning, and safe deprecation.
Live demos showcase an admin discovering a risky action, reviewing evidence (runtime traces + API logs), enforcing a fix (policy/role update), and validating remediation with test prompts. You’ll leave with a checklist, dashboards, and approval workflow templates you can apply immediately to keep Copilot extensions compliant and under control.
From Zero to Hero: Hands‑On Workshop to Build Your First MCP Action for Copilot Studio
Microsoft Copilot Studio now lets you extend copilots with MCP Actions secure, schema‑driven operations that call your APIs and business systems. In this hands‑on workshop, you’ll build your first working MCP Action end‑to‑end, register it in Copilot Studio, and invoke it from a real conversation.
We’ll start with a quick tour of the MCP Actions model (schemas, parameters, authentication, error handling) and then go straight into guided builds. You’ll create an Azure Function that wraps a simple business capability (e.g., “Customer Order Lookup”), expose it via Azure API Management, and register it as an MCP Action in Copilot Studio with descriptive inputs/outputs that LLMs can reliably use. Finally, you’ll test the action in the Actions playground and within a Copilot, add basic governance controls (DLP, environment strategy), and review runtime logs and auditing so admins can approve and monitor extensions safely.
By the end, you’ll leave with a reusable template action, repeatable steps, and practical patterns for secure enterprise integrations—ready to adapt to your own APIs on day one back at work.
DSPM for AI: Discover, Classify, and Control Sensitive Data Used by Copilots
Modern Copilot solutions introduce new risks: AI agents can inadvertently access, amplify, or leak sensitive information hidden across SharePoint, Dataverse, Teams, and unstructured content. Traditional governance is no longer enough. This session provides a practical, hands-on walkthrough of applying Data Security Posture Management (DSPM) principles to the Power Platform and Copilot Studio.
We’ll explore how AI interacts with enterprise data, how to classify and label sensitive information automatically, and how to govern what copilots are allowed to see or generate. You’ll see real demonstrations using Microsoft Purview’s sensitivity labeling, AI hub, data classification, audit trails, and Copilot Studio’s runtime logs to detect and prevent risky behavior.
We will simulate a real incident where a Copilot attempts to access a hidden “Contracts” library containing financial and PII data. Step by step, we'll discover the exposure, analyze the AI activity, classify the data, apply access controls, and validate the fix using content-aware policies.
If you want to govern Copilots responsibly in an AI-first world, this session gives you the exact process and tools to do it.
DSPM for AI: Detect, Explain, and Remediate Risky AI Usage Across Copilots & Agents
In this session, we’ll dive into how Microsoft Purview’s DSPM for AI surfaces risky prompts, oversharing behaviors, unsafe grounding data, and sensitive information flow, and how it provides one‑click remediations to enforce responsible AI usage. Attendees will see how DSPM correlates signals across Copilot for Microsoft 365, custom agents, third‑party AI tools, and browser‑based AI interactions giving security teams a unified view of AI risk for the first time.
Through demos, you’ll learn how to:
Detect risky AI usage patterns across copilots and agents
Identify sensitive data exposure using unified audit logs
Apply automated recommendations to mitigate oversharing
Enforce consistent AI safety baselines across the organization
If you’re responsible for AI governance, Purview, M365 security, or Copilot deployment, this session gives you a practical roadmap to operationalize safe and compliant AI at scale.
ALM for Copilot Studio: Pipelines, Versioning, and Safe Promotion
As Copilot Studio agents move from experiments to mission critical automations, organizations need a disciplined and secure ALM process. This session teaches you how to structure Dev → Test → Prod environments, implement least privilege development setups, package agents into solutions, validate generative behavior safely, and deploy via automated pipelines without breaking production. We’ll walk through versioning strategies, rollback approaches, DLP‑aware promotion, and real-world governance patterns that ensure every agent shipped is secure, stable, and compliant.
Agent 365: Your Registry, Quarantine, and Oversight for AI Agents
As organizations adopt AI agents at scale, governance gaps quickly appear shadow agents, unknown data access paths, inconsistent permissions, and limited audit visibility. Agent 365 solves this by introducing a unified control plane for AI agents across Microsoft 365 and Copilot Studio. In this session, you’ll learn how Agent 365 provides a central registry, identity backed access controls, quarantine workflows, telemetry, and security policies to help IT govern agents the same way they govern users today. We’ll walk through end‑to‑end demos showing how to inventory agents, audit their behavior, apply least‑privilege rules with Entra Agent ID, and take corrective actions on unsanctioned or risky agents. If you're responsible for AI governance, security, Power Platform, or Copilot rollout, this talk will give you a practical blueprint to ship AI safely and confidently.
Scaling Power Apps Beyond MVP with Governance, ALM, and Fabric Analytics
Many organizations succeed with initial Power Apps, then struggle as usage grows. Questions shift toward ownership, deployment risk, and visibility into business impact. This session explains how to scale Power Apps using clear environment strategy, solution-based ALM, and analytics through Microsoft Fabric. You see how Dataverse operational data feeds adoption and performance insights without disrupting app teams.
Designing Enterprise-Ready AI Agents with Agent 365 and Dataverse
AI agents often look impressive in demos but struggle in real environments. Teams ask how to ground agents in trusted data, restrict actions, and manage changes over time. This session focuses on practical design choices for enterprise-grade agents using Agent 365. You walk through how Dataverse acts as the knowledge layer, Copilot Studio orchestrates conversations, and Power Automate executes controlled actions. The session emphasizes security boundaries, role-based access, and lifecycle management rather than prompt tuning.
Designing a Unified Data Model: Dataverse and Fabric for Operational and Analytical Workloads
Teams often duplicate data models between apps and analytics, leading to drift and inconsistent reporting. This session shows how to design a single, intentional data model where Dataverse supports operational workloads and Microsoft Fabric supports analytics without breaking app performance. You walk through table design, relationships, and ownership boundaries. Fabric pipelines are used to extract, transform, and publish analytical models while Power Apps continues to operate on transactional data.
Dataverse-Centric Architecture for Analytics with Fabric and Power Apps
Power Apps generate valuable operational data, but many teams struggle to convert it into trusted insights. This session presents an end-to-end reference architecture. Dataverse captures transactional data, Fabric pipelines move and shape data for analytics, and Power Apps continue serving users without disruption. You focus on pipeline design, incremental loads, schema evolution, and ownership between app and data teams.
What Attendees Will Learn
• How to design Fabric pipelines sourced from Dataverse
• How to handle schema changes safely
• How to separate operational and analytical responsibilities
• How to avoid common integration and performance pitfalls
Copilot Studio in the Enterprise: Building Agents the Right Way
Microsoft Copilot Studio enables teams to rapidly build AI powered agents but in the enterprise, speed alone isn’t enough. Agents must be secure, deployable, auditable, and production ready.
In this demo-driven session, we’ll walk through real world Copilot Studio development scenarios and show how governance naturally fits into the development lifecycle without slowing teams down. Attendees will see how Copilot agents are built as solution based assets, how they execute real actions using Power Automate, and how they move safely from DEV to PROD using environment strategy and ALM best practices.
The session intentionally bridges Copilot Studio development and enterprise governance, helping developers, architects, and administrators understand how identity, environments, pipelines, and monitoring all contribute to trusted AI at scale.
What you’ll learn:
How Copilot Studio agents behave as enterprise application assets
How to design agents that safely execute actions and workflows
How environment strategy and ALM enable controlled DEV → PROD promotion
How governance improves reliability, security, and developer confidence
This session is ideal for organizations adopting Copilot Studio who want to move beyond experimentation and into production grade AI agents.
Summit NA Roadshow - Pittsburgh, PA Sessionize Event
Manoj Annavajjala
Enterprise AI Architect
Detroit, Michigan, United States
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