OpenAI Agentic AI Engineer Interview Questions

AI Agents are the hottest topic in 2025–2026 GenAI interviews, but most candidates confuse agents with chatbots. Interviewers at OpenAI and Anthropic want you to articulate the agent loop — perceive, plan, act, reflect — and explain when tool use justifies agent complexity over a simple chain.

Interviewers want a practical comparison, not marketing.

A production AI coding agent typically includes:

Prompt injection is the #1 security interview topic for agents that read untrusted text.

AGENTS.md is becoming the de facto 'README for agents.'

Treat agent models/prompts/tools like dependencies.

Medium agents platform question on reusable skill packs and governance.

Medium AI Agents interview question on openai swarm / agents sdk patterns — architecture, trade-offs, eval, and production patterns.

Hard AI Agents interview question on browser agents and computer use risks — architecture, trade-offs, eval, and production patterns.

Easy AI Agents interview question on the ReAct pattern — Thought/Action/Observation loops, trade-offs, and production guardrails.

**Assistants API** Managed threads, code interpreter, file search, function tools with run lifecycle (queued/in_progress). Less boilerplate for CRUD-style agent apps. Trade-offs: vendor lock-in, run polling complexity, harder fine-grained graph control.

Claude Code is Anthropic's CLI coding agent designed for repository-level work: reading the codebase, editing multiple files, running tests/builds, and iterating until a task succeeds. Unlike autocomplete-first copilots that live inside the editor, Claude Code operates as an agent loop — observe → plan → act (edit/run tools) → verify.

Codex CLI represents OpenAI's push into agentic coding outside the editor — similar category to Claude Code and other CLI agents. The core loop is: understand task → explore repo → edit → run commands → iterate.

Cost optimization for coding agents is context engineering + runtime policy.

Prompt engineering focuses on instruction wording. **Context engineering** focuses on the full state fed to the model each step: policies, repository maps, retrieved files, prior tool outputs, memories, and task specs.

**Session** Persistent realtime connection with session.update for instructions/tools; handle reconnect.

**Concept** Responses treat interaction as structured items (messages, tool calls, outputs) with native support for reasoning models and built-in tools — reducing glue code.

Rate limiting for agents differs from human API usage — bursts are larger and less predictable.

Compress agent memory into durable distilled state while preserving goals, constraints, and tool outcomes.

Multi-agent prompt patterns where distinct personas argue, review, or refine outputs before final synthesis.

Context budget tactics: summarization, selective retrieval, example pruning, and distillation for long-running agent sessions.

Security-focused prompt engineering on jailbreaks, indirect injection, tool sandboxing, and layered defenses for production chatbots and agents.

Project question on LLM routers — intent classification, cascades, early exit, and quality monitoring.

Hard scenario on agent memory — episodic vs semantic memory, vector stores, summarization, and model parametric limits.