LeetCode-style practice for agentic AI interviews
Solve Python agent-loop, tool-calling, memory, RAG, and eval problems used in real AI engineering interviews.
Interview Workspace
Practice the agent tasks hiring teams care about.
A focused split workspace for agentic AI interview problems, Python code, console output, and trace-based scoring. Run solutions, study feedback, and build proof that you can ship reliable agent systems.
import re
MAX_STEPS = 6
def run_agent(question: str, llm, tools: dict) -> str:
scratchpad = f"Question: {question}\n"
for _ in range(MAX_STEPS):
output = llm(scratchpad)
scratchpad += output + "\n"
# TODO: parse for Final Answer or Action and act accordingly
pass
return "I could not find an answer."
Interview Curriculum
Five agentic AI focus areas that compound your hiring signal.
Agent Loop
Bounded ReAct and reflection loops that stop cleanly.
Tool Creation
Typed schemas, resilient wrappers, and safe tool calls.
Memory
Context budgets, durable state, and retrieval handoffs.
RAG
Retrieval, reranking, grounding, and evidence shaping.
Evals
Deterministic checks before judgment-layer evaluation.
Interview Shift
AI interviews are moving from answers to agent systems.
Top AI companies and AI-native startups increasingly care whether engineers can build reliable agents, design tools, manage context, retrieve knowledge, and evaluate outputs. If you know agentic development, you can stand out as stronger evidence than generic coding practice alone. AgenticPrep.io focuses the practice loop on those interview skills.
Traditional
Pass hidden cases
Agentic interview
Build, trace, and improve agent systems
How it works
Interactive walkthroughSplit workspace
Build the agent beside the problem
Write Python, inspect tests, and keep the interview prompt visible in one focused split workspace.
Problem
Build a bounded ReAct loop
Stop when the agent returns a final answer or reaches the iteration limit.
def run_agent(task, tools):
# reason, act, observe
for step in range(6):
action = agent.next(task)
if action.is_final:
return action.answer