AB-620 expects you to understand how Copilot Studio combines generative reasoning with authored process logic. Agent flows and topics are central to that design. They let you define inputs, outputs, required steps, formatting, actions, variables, and recovery paths around an agent that would otherwise rely only on runtime reasoning.<\/p>\n
For the AB-620 exam<\/a>, do not study flows and topics as independent UI features. Study the decisions behind them: when a process should become deterministic, when an agent should ask a user for missing information, when a tool belongs inside a topic, and how a workflow should behave when a dependency fails.<\/p>\n An agent flow is appropriate when a business operation has known inputs, outputs, side effects, or required sequencing. Examples include creating a case, requesting approval, updating a record, sending a notification, or invoking a service that must return a structured result.<\/p>\n The agent may decide that the flow is needed, but the flow should enforce the steps that must not be skipped. That distinction reduces ambiguity and makes behavior easier to test. It also aligns with the wider Copilot Studio architecture<\/a> principle of putting deterministic controls below the generative layer.<\/p>\n Flows become more reliable when their interface is small and clear. Define required values, optional values, types, and expected outputs. Avoid passing one unstructured block of text when the workflow needs a customer ID, a category, and an amount as separate fields.<\/p>\n Typed inputs reduce the model\u2019s opportunity to invent structure at execution time. Typed outputs make it easier for the agent to explain a result, branch on status, or escalate an error. Treat the flow interface as an API contract even when everything lives inside the same platform.<\/p>\n Human approval is most useful when the action is consequential, difficult to reverse, or sensitive to context the system cannot reliably infer. Approval should not be sprinkled over every step simply to feel safe; that creates friction without improving control.<\/p>\n A strong human-in-the-loop design tells the reviewer exactly what is about to happen, what evidence supports the action, and which fields matter to the decision. The workflow should preserve the approval outcome as part of the execution record and should have a clear path for rejection or timeout.<\/p>\n If a downstream action can fail, define what failure looks like. Distinguish validation errors from permission failures, missing records, timeouts, and service outages. Decide what can be retried and what requires a different path.<\/p>\n The same boundary appears in tool-use engineering<\/a>: the model should not be forced to guess what happened. Structured failures let the agent communicate accurately and reduce the risk that it continues a workflow from a false assumption.<\/p>\n Topics remain useful even when generative orchestration is enabled. They can guide a known interaction, collect required values, enforce a specific sequence, or provide a controlled response for a sensitive business process.<\/p>\n The important question is not whether topics are \u201cold\u201d and generative orchestration is \u201cnew.\u201d The question is which parts of the interaction benefit from flexibility and which require a predictable structure. An identity-verification step, for example, may need a stricter authored path before the agent resumes open-ended assistance.<\/p>\n A topic can call a flow, connector, API, or another tool. Add a tool when the user\u2019s intent requires data or an action the model cannot safely perform through language alone. Define what information must be collected before the call and what the topic should do with each possible result.<\/p>\n Avoid using a tool merely because it exists. The tool should solve a concrete problem in the topic. Clear tool descriptions and narrow capabilities also help generative orchestration choose correctly when several options are available.<\/p>\n Custom prompts can classify, summarize, extract, transform, or generate text within a topic. Treat them as maintained application components. Define the task, inputs, output contract, representative examples, and evaluation cases.<\/p>\n The broader prompt engineering fundamentals<\/a> are directly relevant: clarity, context, constraints, examples, and explicit output structure improve reliability. AB-620 adds the enterprise question of what happens after the prompt result is produced. If that output controls a business action, validate it before execution.<\/p>\nUse an agent flow when the process has a defined operational contract<\/h2>\n
Make input and output parameters explicit<\/h2>\n
Human-in-the-loop steps belong at consequence boundaries<\/h2>\n
Build error handling into the flow, not into wishful prompt text<\/h2>\n
Use topics when authored conversational control adds value<\/h2>\n
Tools inside topics should have a reason to be there<\/h2>\n
Custom prompts need the same discipline as reusable code<\/h2>\n
Knowledge sources and APIs solve different kinds of questions<\/h2>\n