Question 1

Why use state graphs in LangGraph instead of linear prompt chains?

Accepted Answer

Linear prompt chains break the moment an LLM encounters unexpected ambiguity, format errors, or multi-step decision paths. LangGraph models processes as cyclical state machines with strongly typed state, allowing agents to branch, retry, self-correct, and pause for human approval with 100% durability.

Question 2

How do Human-in-the-Loop (HITL) interrupt gates work in production?

Accepted Answer

LangGraph checkpointers freeze the complete state graph in PostgreSQL. The system generates an approval token and alerts managers via a Next.js dashboard or webhook. When the manager clicks Approve or edits a section, the graph resumes from the exact checkpoint without re-running earlier steps.

Question 3

Which LLMs do you use for agentic systems?

Accepted Answer

Primarily Claude 3.5 Sonnet for deep reasoning, coding, and structured extraction, along with GPT-4o for analytical synthesis and Grok / Gemini for rapid multimodal tasks. All model calls use strict temperature settings and schema validation.

Question 4

How do you prevent agents from hallucinating or running in infinite loops?

Accepted Answer

Every loop is governed by hard iteration limits (typically 3–4 passes max), Pydantic validation boundaries, and deterministic fallback branches. If an agent fails to converge, the thread safely escalates to human staff rather than burning tokens.

LangGraph & Multi-Agent System Developer