CCAR-F Agentic Architecture and Orchestration Practice Question
An architect is designing a Claude agent that must autonomously handle customer disputes end to end. The agent may issue refunds up to $200, but any refund above that amount must be approved by a human reviewer. The system must not block the entire workflow while waiting for approval. Which orchestration pattern best satisfies these constraints?
⚠ Common exam trap
The trap here is assuming the agent can wait or poll in the background, when in reality the API is stateless and the pause must be implemented by the orchestrator, not the model.
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
Model the refund as a tool that returns a pending status plus a case identifier, persist the agent state, and resume the conversation when the human decision arrives.
Human-in-the-loop patterns work best when the agent yields rather than blocks. A refund tool that returns a pending status with a case identifier lets the orchestrator persist the conversation and resume it when the reviewer decides. This keeps the workflow responsive, avoids tying up connections, and cleanly injects the approval or denial back into the agentic loop as a new tool_result.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Have the agent call a refund tool that internally pauses the HTTP request until a human approves, then returns the result in the same tool_result.
Why it's wrong here
Blocking the HTTP request while awaiting human approval ties up worker threads and risks timeouts, violating the requirement that the workflow not stall. Long-lived synchronous waits are fragile under load and can exhaust connection pools. The agent should yield control and resume later, not hold a request open indefinitely for a human decision.
- ✗
Route all disputes to a queue processed only by humans, removing the agent from the approval path entirely.
Why it's wrong here
This abandons the autonomous handling the scenario requires and forfeits the agent's ability to resolve low-value disputes. The requirement is selective human approval only above a threshold, so removing the agent from the path is overkill. It also eliminates the agent's contextual reasoning over dispute details, which is the value the architecture was meant to provide.
- ✗
Instruct the agent in the system prompt to always ask the customer to wait while it internally polls the approval service every few seconds.
Why it's wrong here
Polling inside the model loop burns tokens and turns, and the agent cannot actually sleep or poll between API calls; it only generates text. The system prompt cannot create a background wait mechanism. This wastes context and still does not deliver a durable pause, so the workflow either spins or stalls without a reliable resume path.
- ✓
Model the refund as a tool that returns a pending status plus a case identifier, persist the agent state, and resume the conversation when the human decision arrives.
Why this is correct
Returning a pending status with a case identifier lets the agent stop cleanly without blocking, and persisting state allows the conversation to resume when approval arrives. This is the standard durable-execution pattern for human-in-the-loop agents: the tool reports an intermediate outcome, the orchestrator stores the messages array, and a later event injects the decision as a new tool_result to continue reasoning.
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JA
Written and reviewed by Johnson Ajibi, MSc IT Security
Senior Network & Security Engineer · founder of Courseiva
Last reviewed September 2026 · checked against the official Anthropic exam blueprint
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