Video course · 10 chapters · 49 min · certificate
Enterprise AI Strategy: Design Agent Solutions That Scale
Design an enterprise AI agent strategy that scales: the Cloud Adoption Framework process, high-value agent use cases, single versus multi-agent design, guardrails, Copilot Studio knowledge, and when to build custom agents, models or small language models.
What you'll learn
- Apply the Cloud Adoption Framework AI adoption process
- Identify high-value business use cases for agents
- Decide when a multi-agent design is justified
- Define guardrails with the solution constraint pyramid
- Choose between extending Copilot and building custom agents
- Decide when custom models or SLMs are worth it
Chapters
10 chapters · 48:55-
1:22
01Intro Members
Scaling AI without chaos
The goal: an AI strategy that scales without disconnected agents, duplicated effort or governance gaps.
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8:35
02Strategy Members
The Cloud Adoption Framework for AI
The CAF as a GPS with seven stops, and how AI strategy maps to planning.
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5:48
03Use cases Members
Strategy for agents in business
Agents are more than chatbots: finding high-value use cases and spending resources wisely.
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6:33
04Architecture Members
Designing multi-agent solutions
Start with one agent and scale to multi-agent only when evidence says you should.
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3:38
05Prebuilt Members
Use cases for prebuilt agents
Prebuilt Copilot agents deliver quick value where information is scattered and tasks are repetitive.
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3:03
06Guardrails Members
Rules and constraints
The solution constraint pyramid: behavioral rules, human oversight for high impact, and data governance.
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6:51
07Copilot Studio Members
Knowledge and generative answers
Grounding Copilot Studio agents with knowledge sources and generative answers instead of hand-built topics.
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4:10
08Decision Members
Extend Copilot or build custom
Extend Microsoft 365 Copilot when core capabilities fit; build custom agents when they don't.
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3:16
09Models Members
When to build custom models
Custom models are a costly, high-stakes decision: try prebuilt models, prompting, fine-tuning and retrieval first.
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5:39
10SLMs Members
Small language models
SLMs as precision tools: domain, behavioral and task tuning for efficiency, specificity and control.
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