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Interview tracksAI Agents / RAG
Interview track

AI Agents / RAG

Planning, tool use, memory and retrieval-augmented generation for autonomous agents. Reason through how agents decompose goals, call tools, and recover when things go wrong.

Curated & expert-sourced - not AI-invented
12
interviews
Easy → Hard
levels covered
~30 min
average length
Voice-led
adaptive · curated

All interviews 12

Grouped by level - start anywhere

Easy

3 interviews
What Is an AI Agent
Curated·Technical·Easy·~30 min
Start
Retrieval-Augmented Generation for Agents
Curated·Technical·Easy·~30 min
Start
Tool Calling and Function Execution
Curated·Technical·Easy·~30 min
Start

Medium

5 interviews
Planning and Task Decomposition
Curated·Technical·Medium·~30 min
Start
Agent Memory: Working, Short-Term, and Long-Term
Curated·Technical·Medium·~30 min
Start
Agent Frameworks and Orchestration Libraries
Curated·Technical·Medium·~30 min
Start
Context Engineering: Managing the Agent's Information Window
Curated·Technical·Medium·~30 min
Start
Agentic RAG: Iterative Retrieval and Self-Correction
Curated·Technical·Medium·~30 min
Start

Hard

4 interviews
MCP, A2A, and Interoperability Standards
Curated·Technical·Hard·~30 min
Start
Metacognition and Self-Correction in Agents
Curated·Technical·Hard·~30 min
Start
Multi-Agent Architecture and Coordination Patterns
Curated·Technical·Hard·~30 min
Start
Trustworthy and Secure AI Agents
Curated·Technical·Hard·~30 min
Start