What are Agents in LangChain?
Agents in LangChain are autonomous AI systems that use a Large Language Model (LLM) as a reasoning engine to decide which actions to take and in what order. Unlike simple chains with fixed steps, agents dynamically choose tools, APIs, or functions based on user input and intermediate results — repeating the think → act → observe loop until a final answer is reached.
Agents in LangChain are autonomous AI systems that use a Large Language Model (LLM) as a reasoning engine to decide which actions to take and in what order. Unlike simple chains with fixed steps, agents dynamically choose tools, APIs, or functions based on user input and intermediate results — repeating the think → act → observe loop until a final answer is reached.
An Agent combines an LLM with a set of Tools and a reasoning strategy (called an AgentExecutor). The LLM acts as the 'brain' — it reads the input, decides which tool to call, observes the result, and either calls another tool or returns a final response. This loop is often referred to as the ReAct (Reasoning + Acting) pattern.
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LLM / Chat Model — The reasoning engine that decides what to do next
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Tools — Functions the agent can call (e.g., web search, calculator, SQL query)
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AgentExecutor — The runtime loop that executes the think → act → observe cycle
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Prompt Template — Instructs the LLM on how to reason and format its actions
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Memory (optional) — Allows the agent to remember past interactions
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ReAct Agent — Reasons step-by-step using Thought → Action → Observation
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OpenAI Tools Agent — Uses OpenAI's native function/tool calling capability
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Structured Chat Agent — Handles multi-input tools with structured arguments
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Self-Ask with Search — Breaks complex questions into sub-questions iteratively
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Plan-and-Execute Agent — Plans all steps first, then executes them sequentially
In LangGraph (the newer LangChain framework for agents), agents are modeled as stateful graphs where each node represents an action or decision point. This gives developers fine-grained control over the agent's flow, making it ideal for complex multi-step and multi-agent workflows.
Scenario Questions
0-2 years experience
- 1How would you set up a simple ReAct agent in LangChain to answer a user’s question using a search tool?
- 2If you forget to add a tool to an agent’s tool list, what will happen when the agent tries to use it?
- 3Can you walk me through the steps the agent takes from receiving input to returning a response?
2-5 years experience
- 1We have a chatbot that sometimes repeats the same answer. How would you modify the agent’s memory or prompt to reduce duplication?
- 2During a rollout, the agent started failing when the external API returned a 500 error. How would you handle this failure within the agent’s tool execution flow?
- 3Explain why adding a new tool caused the agent to exceed the token limit and how you would address it.
5-8 years experience
- 1Design an architecture where multiple specialized agents coordinate to handle a complex user request that involves data retrieval, summarization, and action execution. What trade‑offs do you consider for routing and state sharing?
- 2At scale, agents can cause high latency due to repeated LLM calls. How would you redesign the agent pipeline to improve throughput while preserving flexibility?
- 3What are the risks of using recursive agents for multi‑step reasoning, and how would you mitigate runaway loops or token blow‑up?
8+ years experience
- 1Our platform plans to migrate from LangChain agents to a custom orchestration layer. What architectural concerns would you raise, and how would you ensure backward compatibility for existing agent‑based features?
- 2When multiple teams build agents that share tools and memory stores, how would you establish governance and versioning to avoid conflicts and ensure observability?
- 3Discuss the long‑term maintenance implications of embedding business logic inside agent prompts versus external services, and recommend a strategy for a large enterprise.
Follow-up Questions
- What metrics would you instrument to detect an agent getting stuck in a loop?
- How do you choose between a built‑in tool and a custom LLM prompt for a given sub‑task?
- Can you describe a situation where you’d move business logic out of the agent prompt into a service?