The Agent Engineer Interview
Agent engineering interviews test your ability to build reliable, production-grade AI systems. Here's what interviewers look for:
Key Competencies
| Area | What They Test | How They Test It |
|---|---|---|
| System Design | Architecture decisions | "Design an agent for X" |
| LLM Knowledge | Understanding of models | "Why would you choose model X?" |
| Production Mindset | Reliability focus | "How do you handle failures?" |
| Safety Awareness | Risk mitigation | "What could go wrong?" |
Common Interview Questions
Q: "What's the difference between an LLM and an agent?"
Strong Answer: "An LLM is a function that transforms text to text. It has no state, no ability to take actions, and no way to verify its outputs. An agent wraps an LLM with tools, memory, and an orchestration loop. The agent can take actions (call APIs, query databases), maintain state across turns, and verify information against real data sources. The key insight is that production AI needs more than text generation—it needs the ability to act and verify."
Strong Answer: "An LLM is a function that transforms text to text. It has no state, no ability to take actions, and no way to verify its outputs. An agent wraps an LLM with tools, memory, and an orchestration loop. The agent can take actions (call APIs, query databases), maintain state across turns, and verify information against real data sources. The key insight is that production AI needs more than text generation—it needs the ability to act and verify."