Microsoft Agentic AI Business Solutions Architect

Agents are only as good as the data they ground on. You gather requirements, audit the data an agent would use for quality, access and freshness, and decide what must be fixed before any agent is built.

• Capturing requirements an agent can be tested against
• Grounding data: sources, quality and freshness
• Access rights and data that must stay out
• Dataverse, SharePoint and line-of-business data as sources
• Closing data gaps before design starts

You build A data readiness report for one solution, with the gaps and the fixes.

Strategy sets which problems to solve with AI and how. You compare prebuilt agents, configuration, low-code builds and custom Foundry work, and write down why each choice fits the organization’s skills and constraints.

• Setting an AI strategy tied to business goals
• Prebuilt, configured, low-code or custom
• Fit with Dynamics 365 and Microsoft 365 investments
• Skills, capacity and ownership of an agent estate
• Writing a decision record others can challenge

You build A build, buy or extend decision record for three candidate solutions.

A solution needs numbers that survive scrutiny. You model benefits and total cost, including licensing, consumption, model usage and support, and see how choices such as a model router change the cost curve.

• Benefits you can measure and benefits you cannot
• Licensing, consumption and model usage costs
• Model router and choosing model size per task
• Total cost of ownership across an agent’s life
• Sensitivity analysis and break-even
• Presenting a case to a finance reader

You build An ROI and total cost model for one agent, with its assumptions stated.

Task agents, autonomous agents and prompt-and-response agents differ in who starts them and who is accountable. You learn the shape of each and match it to the job, the data and the risk.

• Prompt-and-response agents and where they stop
• Task agents that perform a defined job
• Autonomous agents triggered by events
• Triggers, inputs and expected outputs
• Where accountability sits for each type

You build A catalog that assigns an agent type to each use case, with reasons.

Many agents live where people already work. You design agents in Copilot Studio and extend Microsoft 365 Copilot, choosing knowledge, actions, channels and identity so the agent fits the user’s day.

• Agent design in Copilot Studio: topics, knowledge, actions
• Extending Microsoft 365 Copilot with agents
• Choosing channels and surfaces
• Identity and user context in agent calls
• Designing conversations that fail gracefully
• Limits and licensing that shape the design

You build A design specification for an agent in Copilot Studio, from persona to actions.

Dynamics 365 applications ship with agents already built. You learn to assess what they do, configure them, and decide when to extend or replace them, so you do not rebuild what the platform already provides.

• Surveying prebuilt agents across the Dynamics 365 apps
• Configuring a prebuilt agent to a business process
• Reading what a prebuilt agent did and why
• Extending versus replacing a prebuilt agent
• Keeping configuration consistent across environments

You build A configuration plan for the prebuilt agents in one Dynamics 365 solution.

Real processes cross products. You design several agents that hand work to one another across Dynamics 365, Copilot Studio, Microsoft 365 and Foundry, and decide who orchestrates, who owns state and where people approve.

• When one process needs several agents
• Orchestrator and specialist designs
• Passing context and state between agents
• Agent-to-agent calls across products
• Approval points and human takeover
• Avoiding duplicated or conflicting actions

You build A multi-agent design for one end-to-end process, with its handoffs and approvals.

When built-in options fall short, you extend. You connect agents to systems through MCP, decide when a custom Foundry model is justified, and weigh the trade-offs in quality, cost and upkeep.

• How MCP exposes tools and data to agents
• Designing and governing MCP connections
• The Foundry model catalog and model selection
• When to fine-tune or bring a custom model
• The running cost of a custom model
• Choosing between connectors, APIs and MCP

You build An extension design that connects an agent to a line-of-business system through MCP.

Agents need evidence of quality before and after release. You design test sets, acceptance criteria and monitoring, and tune agents using what the monitoring shows about accuracy, usage and cost.

• Acceptance criteria for non-deterministic behavior
• Test sets built from real conversations
• Monitoring accuracy, usage and failures
• Tuning instructions, knowledge and actions
• User feedback as a signal
• Reporting results to the business owner

You build A test and monitoring plan for an agent, with the measures its owner reviews.

Agents change, and changes need a path to production. You plan environments, solutions, versioning and retirement, so that updates are repeatable and an agent can be withdrawn cleanly.

• Environment strategy for development, test and production
• Packaging agents in solutions
• Versioning, promotion and rollback
• Ownership and change control
• Retiring or replacing an agent

You build A lifecycle plan covering environments, promotion steps and retirement for an agent.

Agents act with real access, so they need real controls. You design identity, data boundaries, audit trails and compliance measures for agents, and align them with the controls the organization already runs.

• Identity and permissions for agents
• Data loss prevention and data boundaries
• Audit logs and traceability of agent actions
• Meeting regulatory and internal policy requirements
• Threats to agents: injection and over-permissioning

You build A security and audit design for an agent solution, mapped to the controls it relies on.

Responsible AI is a design input, not a review at the end. You assess harms, set transparency and oversight, and design the controls and user messaging that keep an agent fair, explainable and accountable.

• Identifying harms and affected groups
• Transparency: telling users what the agent is
• Human oversight and escalation
• Fairness, grounding and content safety controls
• Recording decisions in an impact assessment

You build A responsible AI assessment for one agent, with its controls and owners.

Pull the work into one architecture a sponsor could approve. You plan, design and govern a solution for a realistic scenario, and defend the business case, the design, the controls and the rollout.

• Framing the scenario and its success measures
• Assembling the plan: data, strategy and cost
• Assembling the design: agents, extensions and models
• Assembling the deployment: testing, lifecycle and controls
• Presenting the architecture to a review panel

You build An architecture proposal covering business case, agent design, deployment and governance.