existing AB-100 agentic solution guide<\/a> explains broad design principles. This capstone applies them to a particular measurable service with clear state transitions and a governed completion test.<\/p>\nDraw the ordinary request and the exception path<\/h3>\n Use a concrete customer with two warranty cases for products that share a similar description. The agent must establish which case and product the user means, ask for a safe clarification if needed, and retrieve the correct policy version. The happy path is short because the order record is available and the eligibility rule is clear. The exception path differs: the account reference is ambiguous, the policy does not cover that region or the source service is temporarily unavailable. For each branch, describe the information shown to the customer and the durable case state left for the support team. A system that returns a plausible answer while leaving the wrong case modified has not achieved the business goal. The diagram should distinguish what was observed from what the agent recommended.<\/p>\n
A typical request begins with identity verification and an order reference. The coordinator retrieves approved facts, checks policy eligibility and explains the result. If an allowed non-financial update is requested, the tool validates parameters and returns the committed state. If the request involves a sensitive credit or contradictory evidence, the agent prepares a handoff for an authorized operator.<\/p>\n
Design the exceptional path first in a diagram. What happens when the order system times out, the policy is expired, a connector cannot authenticate or the action’s acknowledgment is lost? The agent should not simply retry indefinitely, create duplicate transactions or tell the customer the work is complete without proof.<\/p>\n
Use a stable case and transaction correlation ID. That makes the flow intelligible across customer chat, Dynamics 365 records, middleware and the approval process.<\/p>\n
Protect business actions at the service boundary<\/h3>\n A model can recommend that a customer qualifies for compensation, but the business service must enforce eligibility, permissions and any required approval. Reading an order and mutating a payment are different privileges. No retrieved document should be able to expand what a tool may do or modify the organization’s refund threshold.<\/p>\n
Test the agent with a fictional user asking for another customer’s records. The data API should reject the request according to real authorization rules. Test a refund above the delegated limit and ensure the outcome remains pending or denied even if the model describes a compelling reason.<\/p>\n
An action result should distinguish requested, accepted, pending review, failed and committed states. Customers deserve truthful transaction status rather than confident prose about what the assistant intended to do.<\/p>\n
Calculate a credible pilot business case<\/h3>\n A pilot’s cost includes licensing, model usage, tools, retrieval, log storage, evaluation, security reviews and the human work that remains. Compare the cost per correctly resolved inquiry with the same measure before automation. A system that deflects calls but increases repeat contacts may not produce the benefit its dashboard suggests.<\/p>\n
Use a representative test population with common, ambiguous and difficult cases. Consider operational changes such as faster new-agent training or reduced searching through separate systems, but avoid claiming savings from hypothetical staff reductions that the business has not approved.<\/p>\n
Document a downside case in which a wrong action incurs a correction cost or regulatory inquiry. The architecture and budget must support controls needed to keep that risk acceptable.<\/p>\n
Evaluate behavior and final business state<\/h3>\n Build acceptance tests for a valid warranty, an expired policy, an extended customer-specific plan, a missing order, a customer trying to access another account, an unavailable API and a requested financial adjustment. Every test must specify both the expected explanation and the allowed final state of the business record.<\/p>\n
Measure retrieval evidence, policy correctness, tool invocation outcome, latency, escalation, total interaction cost and user recontact. Automated model evaluators can provide useful signals but must be calibrated against human-reviewed examples and transactional checks. A pleasant answer cannot pass when it changed the wrong case.<\/p>\n
When a new policy or model is released, rerun the same ledger. A single impressive pilot demonstration is not a substitute for repeatable regression testing of the workflow.<\/p>\n
Make release and rollback part of the architecture<\/h3>\n The deployable solution includes agent topics, prompts, knowledge sources, connector permissions, workflows, model deployments and possibly separate Dynamics 365 changes. Track dependencies across development, test and production environments. If an agent expects a field that the production connector does not yet expose, publishing the chat interface will not repair the integration.<\/p>\n
Stage rollout to a controlled population where supported and monitor actual outcomes against agreed gates. Keep an approved option to disable a dangerous action or restore a prior tested version. A rollback of agent instructions cannot undo customer records already modified, so reconciliation ownership must be documented as well.<\/p>\n
Operations staff need monitoring and an escalation runbook that works without the original builder. Deployment success should be assessed by a tested customer transaction, not only by the platform reporting that an agent was published.<\/p>\n
Present the solution as a decision record<\/h3>\n Give a reviewer two candidate architectures for the same service. One extends an existing Dynamics 365 capability through Copilot Studio and a narrow action connector; the other introduces a custom Foundry agent for the full conversation. Compare the expected accuracy, data integration effort, authorization boundaries, operational skills, latency, business testing and recovery process for both. The recommendation should explain which requirements justify the added complexity rather than assume that the custom option is inherently more advanced or valuable. State which evidence from the pilot would reverse the decision, such as persistent data quality problems or unacceptable escalation costs. That makes the architecture review a living record of tradeoffs and measurable outcomes rather than a presentation built only around vendor product names.<\/p>\n
The final architecture package should explain the business problem, alternatives considered, component ownership, data flow, identity and tool boundaries, cost assumptions, expected benefit, release tests and failure response. It should identify why some work remains deterministic, where generative reasoning is useful and which cases require humans.<\/p>\n
Ask reviewers to challenge one assumption at a time: what if the policy changes overnight, an order system becomes unavailable, the business opens in another region or the model changes response format? A defensible architecture explains the adjustment and its consequences without adding an unrestricted super-agent as the default solution.<\/p>\n
AB-100 architectural readiness is demonstrated when a service can be deployed, measured, governed and safely changed over time. A working chatbot is only one visible part of that enterprise responsibility.<\/p>\n","protected":false},"excerpt":{"rendered":"
A complete AB-100 business solution architecture capstone connecting Copilot Studio, Dynamics 365, governed data, measurable ROI, evaluation and release control.<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[2342,2353],"tags":[],"class_list":["post-12351","post","type-post","status-publish","format-standard","hentry","category-certifications","category-microsoft"],"aioseo_notices":[],"aioseo_head":"\n\t\t\n\t