AI Agent Development and Interview Guide: 04-Agent Architecture and LangGraph Orchestration

1. technical system component：technical system component Agent

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2. ReAct、Plan-and-Execute technical system component Router

2.1 Router

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2.3 Plan-and-Execute

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2.4 technical system component

3. technical system component Agent technical system component

technical system component。technical system component Schema technical system component：

from typing import Literal, TypedDict


class AgentState(TypedDict, total=False):
    run_id: str
    user_id: str
    goal: str
    route: Literal["knowledge", "ticket", "human"]
    retrieved_evidence: list[dict]
    tool_calls: list[dict]
    tool_results: list[dict]
    step_count: int
    max_steps: int
    final_answer: str
    error_code: str | None

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4. technical system component

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from typing import Literal, TypedDict

from langgraph.checkpoint.memory import InMemorySaver
from langgraph.graph import END, START, StateGraph


class SupportState(TypedDict, total=False):
    query: str
    route: Literal["knowledge", "ticket", "human"]
    evidence: list[str]
    ticket_id: str
    answer: str


def classify(state: SupportState) -> dict:
    query = state["query"]
    if "technical system component" in query or "technical system component" in query:
        return {"route": "ticket"}
    if "technical system component" in query:
        return {"route": "human"}
    return {"route": "knowledge"}


def route_after_classify(state: SupportState) -> str:
    return state["route"]


def retrieve(state: SupportState) -> dict:
#    # technical system component Retriever，technical system component Evidence。
    return {"evidence": ["[S1] technical system component：technical system componentknowledge bases。"]}


def answer(state: SupportState) -> dict:
    evidence = "\n".join(state.get("evidence", []))
    return {"answer": f"technical system component：\n{evidence}"}


def create_ticket(state: SupportState) -> dict:
#    # technical system component、technical system component。
    return {"ticket_id": "T-001", "answer": "technical system component T-001"}


def handoff(state: SupportState) -> dict:
    return {"answer": "technical system component"}


builder = StateGraph(SupportState)
builder.add_node("classify", classify)
builder.add_node("retrieve", retrieve)
builder.add_node("answer", answer)
builder.add_node("create_ticket", create_ticket)
builder.add_node("handoff", handoff)

builder.add_edge(START, "classify")
builder.add_conditional_edges(
    "classify",
    route_after_classify,
    {
        "knowledge": "retrieve",
        "ticket": "create_ticket",
        "human": "handoff",
    },
)
builder.add_edge("retrieve", "answer")
builder.add_edge("answer", END)
builder.add_edge("create_ticket", END)
builder.add_edge("handoff", END)

graph = builder.compile(checkpointer=InMemorySaver())

config = {"configurable": {"thread_id": "demo-thread-1"}}
result = graph.invoke({"query": "technical system component？"}, config=config)
print(result["answer"])

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5. technical system component

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from typing import Literal
from pydantic import BaseModel, Field


class NextAction(BaseModel):
    action: Literal["search", "create_ticket", "ask_user", "finish"]
    tool_name: str | None = None
    tool_arguments: dict = Field(default_factory=dict)
    user_question: str | None = None
    final_answer: str | None = None

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6. technical system component

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def guard_limits(state: AgentState) -> Literal["continue", "stop"]:
    if state["step_count"] >= state["max_steps"]:
        return "stop"

    calls = state.get("tool_calls", [])
    if len(calls) >= 2 and calls[-1] == calls[-2]:
        return "stop"

    return "continue"

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7. technical system component

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8. Checkpoint technical system component

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9. Human-in-the-Loop

from langgraph.types import Command, interrupt


def approve_ticket(state: SupportState) -> dict:
    decision = interrupt({
        "question": "technical system component？",
        "proposed_title": state["query"][:80],
    })
    return {"approved": bool(decision)}


## technical system component interrupt technical system component；technical system component thread_id。
## graph.invoke(Command(resume=True), config=config)

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10. technical system component

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12. technical system component

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13. technical system component

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14. technical system component

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