Agentic AI in Practice: Building Real-World Automations with LangGraph and n8n
By Pavan Sharma — AI Agent Developer & Full Stack Engineer
What Makes an AI "Agentic"?
A regular LLM call is stateless: you send a prompt, you get a response. An agent is different — it has a goal, tools it can call, and a loop that runs until the goal is achieved or it runs out of steps.
At micro1, I've been building AI agents that handle real-world workflows. The architecture has evolved significantly from "just call GPT-4" to production systems that are reliable, auditable, and efficient.
The Agent Loop
Every agent follows a basic loop:
Observe → Think → Act → Observe → Think → Act → ... → Done
In LangGraph, this is modeled as a directed graph:
from langgraph.graph import StateGraph, END
workflow = StateGraph(AgentState)
workflow.add_node("think", reasoning_node)
workflow.add_node("act", tool_execution_node)
workflow.add_node("observe", result_processing_node)
workflow.add_conditional_edges(
"think",
should_continue,
{"continue": "act", "done": END}
)
Tool Design Matters More Than Model Choice
The most common mistake in agentic AI: spending too much time picking the LLM and too little time designing the tools.
A good agent tool has:
- ▸A single, clear responsibility — tools that do too much lead to incorrect tool selection
- ▸Predictable input/output schemas — use Pydantic models, not loose dicts
- ▸Explicit error returns — the agent needs to know when a tool fails and why
- ▸Idempotency where possible — agents may call the same tool multiple times
from pydantic import BaseModel
class SearchInput(BaseModel):
query: str
max_results: int = 5
date_filter: str | None = None
class SearchOutput(BaseModel):
results: list[dict]
total_found: int
error: str | None = None
Orchestrating with n8n
For workflows that mix AI steps with external services (APIs, databases, webhooks), n8n is excellent. It provides visual workflow orchestration and handles:
- ▸Retry logic and error routing
- ▸Webhook triggers and scheduled runs
- ▸HTTP requests to external APIs
- ▸Database read/write nodes
I use a pattern where LangGraph handles the AI reasoning loop and n8n handles the surrounding orchestration — triggering the agent, routing its output to the right destination, and sending notifications.
The Planning Problem
Simple agents work on single-step tasks. Real tasks require multi-step planning:
- ▸"Research competitor pricing" requires: identify competitors → scrape pricing pages → extract data → compare → summarize
- ▸"Schedule a meeting" requires: check availability → find slot → send invites → add to calendar
For complex plans, I use a two-level architecture:
- ▸Planner agent: breaks the goal into subtasks
- ▸Executor agent: executes each subtask, reporting results back to the planner
This is more expensive but significantly more reliable than a single agent trying to do everything.
What Breaks in Production
1. Context window overflow: agents accumulate tool results and the context grows until it hits the limit. Solution: summarize older results instead of retaining raw output.
2. Tool selection errors: the agent calls the wrong tool or with wrong parameters. Solution: add a validation step before execution, and use structured output forcing for tool calls.
3. Infinite loops: the agent keeps trying failed approaches. Solution: add a step counter and a "reflect on failure" node that forces a different strategy after N failed attempts.
4. Hallucinated tool outputs: the model sometimes generates what it "thinks" a tool would return instead of actually calling it. Solution: enforce tool calling via the model's function-calling API, never via free-text generation.
The Future of Agentic AI
The shift from single-call AI to multi-step agents is the biggest change in practical AI engineering right now. The tooling is maturing fast — LangGraph's persistence layer, n8n's growing integration library, and better LLM function-calling support make production agents increasingly viable. The hard problems are no longer AI problems — they're software engineering problems.