A multi-agent batch analytics job can spawn dozens of short-lived worker agents overnight, then drop to near-zero load by day. Requirements emphasize elastic scale, reliability, operational efficiency, security isolation, and cost control. Which compute specification approach is most appropriate?
Select an answer to reveal the explanation.
Short Explanation
You want compute that scales with agent concurrency, isolates workloads, and idles cheaply when load drops. Elastic container or serverless Azure compute meets the scalability, reliability, efficiency, security, and cost goals. Pair it with concurrency controls and quotas.
Full Explanation
Technology component selection for multi-agent solutions includes compute balancing scale, reliability, efficiency, security, and cost. Nightly fan-out of worker agents is an elastic workload: Azure Container Apps, AKS with autoscaling, or Functions/durable orchestration can scale out and in. Security isolation may use separate identities and network profiles per workload class. The elastic container/serverless Azure compute approach meets these goals. The always-on closet server fails elasticity and cloud ops. Laptop-only workers are unreliable. Permanently over-provisioning premium VMs wastes budget. Also design queueing/batching so model rate limits and tool backends are not overwhelmed when many agents start together, and pair compute choice with concurrency controls and quotas.