AI Agents Architecture Interview Guide: Planning, Tools, and Reliability Patterns

AI agent architecture interviews focus on reliability under partial observability. Interviewers present agents that loop, call wrong tools, or exfiltrate data through overly permissive APIs. Strong candidates decompose agents into planning, execution, memory, and policy layers with explicit contracts between them.

Tool design is half the battle. Tools should be idempotent where possible, return structured errors, and expose schemas that models can reliably populate. Discuss validation before execution, sandboxing, rate limits, and human-in-the-loop gates for irreversible actions like refunds or database writes.

Memory architectures—short-term conversation, episodic summaries, long-term vector stores—each introduce stale context and injection risks. Interviewers ask how you prune memory, attribute sources, and prevent retrieved memories from overriding system policies.

Planning strategies range from ReAct loops to explicit DAG planners. Compare latency, debuggability, and failure recovery. When an agent fails mid-plan, do you resume, replan, or escalate? Production answers include checkpointing and deterministic replay for incident analysis.

Observability for agents requires step-level tracing: thought, tool call, observation, and final answer with token and cost attribution per step. Without traces, debugging a wayward agent in production is guesswork.

Evaluation for agents is scenario-based and adversarial. Discuss tool-selection accuracy, task completion rate, safety violations, and cost per successful task. Benchmarks that only test final answer correctness miss the operational failures that burn on-call engineers.