During month-end, a multi-agent finance close system must process thousands of entity reconciliations. Naively spawning unlimited agent instances exhausts model rate limits. What should you implement?
Select an answer to reveal the explanation.
Short Explanation
Scale with a throttle, not a stampede. Queue reconciliations, batch when safe, cap parallel workers to model/API rate limits, and back off on 429s. Option C. Unlimited spawn (A) melts quotas. Canceling automation (B) fails the business. One mega-context agent for the world (D) is slow and brittle.
Full Explanation
Skills include scaling and control of multi-agent spawning, batching, and concurrent execution. Option C balances throughput with platform limits using queues, worker pools, batching, and backpressure.
Option A causes thundering herds and cascading failures.
Option B rejects the automation goal.
Option D underutilizes parallelism and risks context/quality issues.
On Azure, pair Service Bus/Storage queues with worker containers and Foundry/Azure OpenAI rate-limit aware clients.
Exam tip: Concurrency limits are part of orchestration design, not an afterthought.