AB-100 Agentic AI Business Solutions Architect Roadmap

Begin with the process, data, risk, and measurable outcome. Select agents only where interpretation, planning, dynamic tool choice, or uncertain context creates value.

• Map current processes, decisions, delays, errors, controls, human roles, and non-AI baselines
• Assess agents for task automation, analytics, and decision support without forcing agentic design
• Review grounding data for accuracy, relevance, timeliness, cleanliness, availability, ownership, and permissions
• Organize reusable business data with authoritative sources, sensitivity, residency, and lifecycle metadata
• Apply the Cloud Adoption Framework for AI and create strategy, plan, readiness, adoption, governance, and management views
• Define an agentic-first roadmap, prompt guidance, prompt library controls, and solution constraints
• Compare prebuilt agents, Microsoft 365 extension, Copilot Studio, Power Platform automation, and custom Foundry builds
• Determine when custom models or customized small language models are justified by evaluation
• Create an AI Center of Excellence with reusable standards and federated product accountability
• Build TCO and ROI ranges and assess build, buy, extend, model routing, risk, and opportunity cost

Design the experience, responsibility boundaries, data flow, tools, handoffs, and deterministic controls across low-code and code-first platforms.

• Design task, autonomous, prompt-response, and other agent patterns by outcome and risk
• Design Copilot Studio topics, fallback, generative orchestration, prompt actions, agent flows, reasoning, and voice
• Choose standard NLP, conversational language understanding, or generative orchestration appropriately
• Design grounding and data processing, including permission-aware retrieval and human-review needs
• Design Power Apps and Power Platform AI components using the Well-Architected Framework
• Design Dynamics 365 customer experience, service, sales, contact center, finance, and supply-chain AI use cases
• Design Microsoft 365 Copilot and agent experiences in Teams and SharePoint
• Design custom Foundry models and agents when deeper code, orchestration, evaluation, or deployment control is required
• Design MCP, Computer Use, connectors, APIs, and custom actions as explicit trust boundaries
• Define multi-agent roles, tools, identities, handoffs, state, terminal conditions, cycle prevention, and escalation

Make every behavior-changing artifact versioned, promotable, observable, reversible, and governed.

• Define development, test, UAT, production, evaluation, region, tenant, subscription, and isolation strategy
• Use Power Platform solutions, pipelines, connection references, environment variables, managed solutions, and rollback
• Include Copilot Studio agents, connectors, actions, prompts, flows, and knowledge in ALM
• Include Foundry agents, prompts, models, tools, code, deployments, data contracts, and evaluations in ALM
• Design ALM for custom models, Dynamics 365 AI features, grounding data, and dependencies
• Apply Microsoft Entra identity, least privilege, separation of duties, DLP, secret protection, and audit
• Validate data residency and movement and apply access controls to grounding and model-tuning data
• Threat-model prompt manipulation, poisoned knowledge, oversharing, vulnerable models, MCP, Computer Use, and tool abuse
• Review fairness, reliability and safety, privacy and security, inclusiveness, transparency, and accountability
• Require deterministic human approval, authorization, schema validation, idempotency, and kill switches for consequential actions

Test the business workflow and its probabilistic and deterministic parts. Monitor semantic quality beside availability and infrastructure health.

• Define validation criteria for custom models and expected behavior for prompts and agents
• Create unit, component, conversation, tool, handoff, security, performance, and end-to-end business tests
• Use Copilot to help generate test cases while preserving independent requirements and human review
• Evaluate grounding, relevance, task adherence, completion, tool choice, tool inputs, handoffs, safety, and fairness
• Test prompt attacks, wrong-user data, residency, bypass attempts, loops, tool failures, and human rejection
• Monitor agent performance, telemetry, traces, failures, latency, quality, adoption, feedback, and backlog
• Correlate observations with agent, prompt, model, tool, connector, solution, environment, and data versions
• Use evidence to tune prompts, retrieval, models, routing, tools, workflows, and escalation
• Measure tokens, messages or credits, model calls, tools, data, telemetry, review, and support cost
• Use progressive release, kill switches, incident response, rollback targets, and retirement exercises

An agentic solution succeeds only when people, process, governance, operations, and value evolve with the technology.

• Segment users, roles, changed work, decisions, approvals, skills, accessibility needs, and support demands
• Create communications, role-based training, champions, manager guidance, feedback, and escalation channels
• Measure active and repeat use, task success, human override, satisfaction, confidence, quality, and unintended workload
• Distinguish adoption from effectiveness and report uncertainty, attribution limits, and adverse effects
• Compare measured benefits and lifecycle TCO with the baseline and conservative ROI assumptions
• Use gated decisions to scale, revise, pause, stop, or retire each solution
• Complete the governed customer-operations multi-agent project
• Complete the enterprise Copilot and Foundry adoption blueprint
• Practice original scenarios balanced 28% plan, 28% design, and 44% deploy
• Recheck current official docs, explain distractors architecturally, and avoid dumps or pass guarantees