LLM Agent Architecture: Interview Answer Framework

Agent architecture questions have replaced generic “how does an LLM work” questions as the centerpiece of 2026 AI engineering interviews, because most production AI systems now involve some form of agentic loop rather than a single prompt-response call. Interviewers use this question to separate candidates who have only called a chat API from candidates who have built systems that plan, act, observe, and remember across multiple steps. This article gives you a framework for structuring that answer.

Why This Question Replaced Basic LLM Questions

A single LLM call is a solved interview topic — everyone can explain tokens, context windows, and temperature. Agent architecture is where real engineering judgment shows up: how do you decide when the agent should stop reasoning and take an action, how do you recover when a tool call fails, and how do you give the agent memory without letting the context window balloon. Interviewers ask this question specifically because the answer space is wide enough to reveal seniority.

Framework: The Four-Component Agent Model

Structure every agent architecture answer around four components, and explicitly name the interactions between them, not just the components in isolation.

1. Reasoning loop — how the agent decides what to do next (ReAct and its variants).
2. Tool interface — how the agent invokes external capabilities (function/tool calling).
3. Planning layer — how the agent sequences multi-step tasks (task decomposition, plan-and-execute).
4. Memory system — how the agent retains context across steps and sessions.

The ReAct Pattern: What to Say

ReAct (Reason + Act) interleaves reasoning traces with actions: the model produces a thought, takes an action (typically a tool call), observes the result, and produces another thought incorporating that observation, looping until it decides it has enough information to answer. The key thing to articulate is why this beats a plain chain-of-thought-then-answer approach: it lets the agent course-correct based on real information instead of committing to a full plan upfront based on assumptions that might be wrong. Give a concrete example: an agent researching a company’s stock price does not need to plan every step in advance — it reasons “I need the current price,” calls a price API, observes the result, then reasons “I now need the P/E ratio to answer the valuation question,” and calls the next tool.

Name the known limitation: ReAct loops can run long and expensive if the stopping criterion is weak, so production systems add a maximum iteration count and a “reflect on whether you have enough information” checkpoint every few steps.

Tool Calling: What to Say

Explain tool calling as a three-part contract: a schema (name, description, and typed parameters) the model uses to decide which tool to call and with what arguments, a dispatcher that validates arguments and executes the actual function, and a result-formatting step that turns the tool’s raw output into something the model can reason over in its next step. The interview-differentiating detail is naming failure handling explicitly: what happens when the tool call has malformed arguments (validate and return a structured error back to the model rather than crashing), when the tool itself errors (retry with backoff for transient failures, surface a clear error message to the model for the agent to reason about otherwise), and when the model calls a tool that doesn’t exist (a guardrail that catches hallucinated tool names before they reach the dispatcher).

Planning Algorithms: What to Say

Distinguish two dominant approaches. Plan-and-execute: the agent produces a full multi-step plan upfront, then executes each step, optionally replanning if a step fails or produces unexpected results — better for tasks with well-understood structure where upfront planning saves redundant reasoning calls. Reactive/ReAct-style planning: the agent decides one step at a time with no fixed upfront plan — better for tasks where the right next step genuinely depends on what was just observed, such as open-ended research or debugging. Mention hierarchical decomposition as a third pattern for complex tasks: a top-level planner breaks a goal into sub-goals, each potentially handled by a specialized sub-agent, useful when a single flat reasoning loop would need too many steps to stay coherent.

Memory Systems: What to Say

Separate memory into three types by scope and lifetime, because conflating them is the most common candidate mistake. Working memory is the current context window — what the agent can see right now, including recent reasoning steps and tool observations, bounded by token limits and requiring summarization or truncation strategies once it fills up. Episodic memory persists across a single session or task, such as a running scratchpad of facts gathered so far — often implemented as a structured state object rather than raw text, so it can be selectively re-injected into the prompt. Long-term/semantic memory persists across sessions, typically backed by a vector store or database, retrieved via similarity search when relevant to the current task, similar in mechanism to RAG but storing the agent’s own past experiences and learned facts rather than a static knowledge base.

Comparison Table: Agent Architecture Components

Sample Interview Answer Structure

When asked “design an agent that can research a topic and write a report,” walk through the components in order: “I’d use a ReAct-style reasoning loop for the research phase since the right next search query depends on what earlier searches returned. Tools would include a web search function and a document-fetch function, both returning structured results with explicit error states. For planning, I’d use a lightweight plan-and-execute structure at the top level — outline sections first, then research each section reactively — since the overall report structure is known upfront even if the research within each section is not. For memory, working memory holds the current section’s research; episodic memory holds a running outline and key facts gathered so far, re-injected each turn; I would not need long-term memory unless this agent needs to remember prior reports across sessions.”

Common Mistakes Candidates Make

The most common mistake is describing an agent as “it uses ReAct” without explaining what that buys over simpler approaches — interviewers want the tradeoff reasoning, not the buzzword. The second is treating tool calling as just an API detail rather than discussing failure modes, which signals you have never debugged a flaky tool integration in production. The third is describing memory as a single undifferentiated concept (“it remembers stuff”) instead of separating working, episodic, and long-term scopes, which is the fastest way to signal you have not built a multi-turn agent that survives more than a few steps.

How to Practice This

Pick three agent use cases — a coding assistant, a customer support agent, a data analysis agent — and for each, name the specific reasoning loop, tool set, planning style, and memory scope you would use, and justify why that use case demands that specific combination rather than a different one. The justification is what interviewers are actually grading.

For a complete walkthrough of agent architecture interview questions alongside tool calling, RAG, and evaluation frameworks, with model answers scored against what hiring committees actually reward, see The 0-to-1 AI Engineer Interview Playbook (Amazon: https://www.amazon.com/dp/B0H2CML9XD?tag=sirjohnnymai-20).