AI Agents: The Complete Guide
Agent Architecture Overview
ReAct Pattern: Reasoning and Acting
Tool Use Architecture
Memory Systems
Multi-Agent Systems
Interview Q&A
Q1: What is the difference between an AI agent and a simple LLM call?
Answer: A simple LLM call takes input and returns output in one step. An AI agent operates in a loop: it perceives the environment, reasons about what to do, takes actions using tools, observes results, and iterates until the task is complete. Agents have memory, can use external tools, and make autonomous decisions about how to accomplish goals.
Q2: Explain the ReAct pattern and why it improves agent performance.
Answer: ReAct (Reasoning + Acting) alternates between thinking (Thought) and doing (Action). The agent first reasons about what information it needs, then uses a tool to get that information, observes the result, and repeats. This grounds the LLM in real-world data, reducing hallucinations and improving accuracy compared to pure chain-of-thought reasoning.
Q3: How do you handle the context window limitation in AI agents?
Answer: Use a tiered memory system: short-term memory (current context window), long-term memory (vector store for semantic search), and episodic memory (experience logs). Implement summarization to compress old messages, use retrieval-augmented generation to fetch relevant context, and implement sliding window strategies to manage token usage.
Q4: What are the key considerations when designing tool use for agents?
Answer: Tool definitions should have clear descriptions and parameter schemas. Implement input validation before calling tools. Handle errors gracefully with retries and fallbacks. Use structured outputs (JSON) for reliable parsing. Consider rate limiting and caching. Provide tools incrementally rather than all at once to avoid overwhelming the agent.
Q5: Compare supervisor vs pipeline multi-agent patterns.
Answer: Supervisor pattern uses a coordinator that routes tasks to specialist agents and aggregates results. Best for dynamic task decomposition. Pipeline pattern chains agents sequentially where each processes the output of the previous. Best for linear workflows like ETL. Supervisor is more flexible; pipeline is simpler to implement and debug.
Q6: How do you evaluate AI agent performance?
Answer: Measure task completion rate, number of steps taken, tool call accuracy, error recovery rate, and time to completion. Use human evaluation for quality assessment. Track hallucination rates and factual accuracy. Benchmark against established datasets (HotpotQA, WebArena). Monitor cost per task and latency metrics.
Q7: What is reflection in AI agents and why is it important?
Answer: Reflection is the agent evaluating its own output quality, detecting errors or hallucinations, and iterating to improve. It's important because LLMs can generate plausible but incorrect responses. Reflection catches these errors before presenting results to users, improving reliability and trustworthiness.
Q8: Explain the trade-offs between different memory systems.
Answer: Short-term memory is fast but limited by context window. Long-term memory (vector store) provides unlimited storage but requires retrieval overhead. Episodic memory learns from experience but needs careful curation. The optimal system combines all three: use short-term for current task, long-term for knowledge, episodic for learning from past successes/failures.