What to take into an interview
- Build foundations before learning framework-specific APIs.
- Practice implementation, trace debugging, and architecture explanations.
- Use a weekly plan that produces small, reviewable artifacts.
Start with the software foundations
Be comfortable with Python functions, dictionaries, exceptions, type hints, JSON, HTTP failures, and unit tests. Agent applications use familiar software engineering mechanisms around a probabilistic component. Weak fundamentals become especially costly when model behavior is already variable.
Review state machines and dependency boundaries. A model call, tool registry, memory store, retriever, and evaluator should have explicit inputs and outputs so each can be tested without running the entire system.
Learn the five core agent skills
Study loops, tool creation, memory, retrieval-augmented generation, and evaluation as separate layers. For each layer, learn one basic implementation, one common failure mode, and one way to observe or test it.
- Loops: action parsing, observations, terminal states, and budgets.
- Tools: schemas, validation, retries, permissions, and normalization.
- Memory: token budgets, summarization, retrieval, pinning, and eviction.
- RAG: chunking, query rewriting, hybrid search, reranking, and citations.
- Evals: rubrics, traces, repeated runs, hidden cases, and safety checks.
Practice explaining tradeoffs
Interviewers may ask whether to use a workflow, an autonomous loop, or a human approval step. Frame the decision around task predictability, consequence of error, available evaluation, latency, cost, and observability. More autonomy is not automatically better.
Prepare concise explanations for model choice, context construction, retry policy, credential scope, and rollout strategy. State what you would measure after launch and which thresholds would trigger a fallback or human review.
Use a four-week preparation loop
In week one, implement deterministic helpers and a minimal loop. In week two, focus on tools and memory. In week three, build retrieval and citation exercises. In week four, evaluate traces, rehearse system design, and revisit failed problems.
Keep a short error journal. Record the symptom, first incorrect state transition, root cause, and regression test. This gives you concrete debugging stories and prevents preparation from becoming passive reading.
Hands-on practice
Related coding problems
Route Tool Calls from Agent Output
Practice parsing and dispatching a model-selected action.
Open problemSliding Window Conversation Memory
Manage context under an explicit token budget.
Open problemAggregate Stochastic Agent Evals Across Seeds
Measure nondeterministic behavior across repeated runs.
Open problemBuild the foundation
Related curriculum tracks
Common questions
Frequently asked questions
How long does AI agent interview preparation take?
It depends on your Python and system design background. A focused four-week plan can cover the main patterns, while deeper production experience develops through repeated implementation and debugging.
Should I build a portfolio project?
A small, well-tested project with traces and evals is more useful than a broad demo with unclear reliability. Be ready to explain failures, tradeoffs, and what you measured.
What should I practice without paid model APIs?
You can implement parsers, state transitions, tool validation, memory policies, retrieval helpers, and evaluation logic with deterministic stubs before connecting a hosted model.
Practice next
Start with a measurable preparation plan
Work through one control-flow problem, one context problem, and one evaluation problem to establish a practical baseline before expanding into full systems.