Start by clarifying requirements and scale, then propose a high-level architecture that separates concerns: orchestration, tool integration, memory, and multi-agent coordination. Walk through each component, discussing trade-offs between latency, cost, reliability, and observability, and justify design choices with concrete examples.
Pro tip: Emphasize how you would instrument the system for observability and set up feedback loops for continuous improvement, as this demonstrates production maturity beyond just building a prototype.
Ask questions to understand the use case, expected load, latency requirements, cost budget, and reliability needs. Define functional and non-functional requirements clearly.
Outline the main modules: task decomposition, tool calling, memory management, and multi-step reasoning. Explain how they interact and the data flow.
Describe how multiple agents communicate, share state, and resolve conflicts. Discuss patterns like centralized orchestration vs. decentralized collaboration.
Discuss strategies to reduce latency (caching, parallel tool calls), control cost (model selection, token limits), ensure reliability (retries, fallbacks), and enable observability (logging, tracing, metrics).
Highlight key trade-offs made and how the design could evolve with scale or changing requirements. Mention potential future improvements.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the agent's scope and risk profile, then propose a layered evaluation framework that combines offline benchmarks, online A/B testing, and continuous monitoring. Emphasize safety mechanisms like guardrails, human-in-the-loop, and red-teaming, and discuss trade-offs between safety and performance.
Pro tip: Frame evaluation and safety as a continuous lifecycle, not a one-time gate, and tie it to business metrics like user trust and retention. Show you understand Meta's scale by mentioning automated regression testing and canary deployments.
Ask about the agent's capabilities, user interactions, and potential harms (e.g., misinformation, bias, security). This ensures evaluation targets the right risks.
Propose both task-specific metrics (e.g., success rate, latency) and safety metrics (e.g., toxicity, bias). Use offline datasets and simulated environments for initial testing.
Describe A/B testing, canary releases, and real-time monitoring for anomalies. Include feedback loops from user reports and automated alerts.
Outline guardrails like input/output filters, fallback to human agents, and red-teaming exercises. Discuss how to handle edge cases and adversarial attacks.
Emphasize continuous improvement through post-mortems, model updates, and cross-functional review boards. Align with Meta's responsible AI principles.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by clarifying the system's current architecture, expected scale, and constraints (e.g., latency, cost, isolation). Then propose a layered scaling strategy: horizontal scaling of stateless components, partitioning of stateful components, and asynchronous processing with backpressure. Finally, discuss trade-offs between consistency, latency, and cost, and how you would validate the design with load testing and monitoring.
Pro tip: Emphasize the importance of defining clear SLOs and using them to drive architectural decisions; this shows you think about scalability in terms of business impact, not just technical metrics.
Ask about expected concurrency, latency targets, cost budget, and isolation requirements. This ensures your scaling approach is grounded in real needs.
Analyze the current system to find components that limit scaling, such as databases, shared caches, or orchestrators. Determine which are stateless vs. stateful.
Propose adding more instances behind a load balancer, using auto-scaling groups, and ensuring idempotency for safe retries.
For databases and queues, suggest sharding, read replicas, and partitioning strategies. Discuss consistency trade-offs (e.g., eventual vs. strong).
Use message queues to decouple producers and consumers, and apply backpressure to handle bursts. Discuss rate limiting and circuit breakers.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.