Building Enterprise-Grade Agent Workflows with Google ADK + LangGraph

🗓️ By Lekha Priya · Sunday, July 21, 2025


As AI agents become more capable, the real challenge isn’t just intelligence — it’s control, reliability, and safe integration into enterprise workflows.

Over the last few weeks, I’ve been experimenting with combining three powerful layers:

  1. Google ADK (Agent Developer Kit) for modular tool-based agent logic

  2. LangGraph for stateful, branching agent orchestration

  3. A real-world ITSM use case around incident classification and effort estimation

In this newsletter, I’ll break down how I merged these technologies to create a fully interruptible, stateful, and modular AI agent system — the kind you can actually trust in a production setting.


The Problem: Traditional Workflows Fall Short

My target use case was simple on the surface: 📝 Take incoming IT incidents → classify the type of issue → estimate the resolution effort → escalate if confidence is low.

But in real life?

  • Some tickets are ambiguous.

  • Some models are uncertain.

  • Human override is a must.

  • Reusability and observability are non-negotiable.

A linear LLM chain wouldn’t cut it. I needed a system of intelligent parts, all coordinated — and accountable.


Part 1: Google ADK – Modular Agents with Tools

I started by building agent tools using Google’s ADK, which makes it easy to define:

  • : determines category using rules/LLMs

  • : maps category to effort heuristically

  • : infers root cause from

  • : optional human review logic

Each agent was encapsulated and testable on its own. The ADK pattern of tool-chains and memory handling gave me a great starting point. But orchestration across these tools needed more structure…


Part 2: LangGraph – Controlling the Flow with Memory & Branching

Enter LangGraph — the perfect fit for:

  • Handling shared state

  • Creating graph-based execution paths

  • Supporting conditional routing and interruptibility

I defined a like this:

This state becomes the single source of truth across all agents.

Then, I added LangGraph nodes:

And defined conditional edges:

Suddenly, my agent system could think, branch, pause, and resume — safely.


End-to-End Example: VPN Ticket

Input:

LangGraph execution path:

  1. → “Network Issue” with 0.85 confidence

  2. → “Medium”

  3. → confidence > 0.75 → no escalation

  4. → END

Output:

The same flow would have escalated to a node if confidence had dipped.


Why This Stack Works

By combining both, I was able to layer orchestration on top of modular tools — exactly how modern enterprise AI systems should be built.


In IT, HR, finance, or customer service — agents need more than “smartness.” They need:

  • Audit trails 🧾

  • Decision safety nets 🧍

  • Scalable, reusable components 🔁

  • Observability 📊

This system ticks every box.


Read the Full Article

I’ve broken this down in full detail (with diagrams, Mermaid flows, and code) in my Medium piece:

"5 Reasons Why LangGraph is a Breakthrough for Building Stateful, Controllable AI Agents "👉 https://lekha-bhan88.medium.com/5-reasons-why-langgraph-is-a-breakthrough-for-building-stateful-controllable-ai-agents-8f8de4afcbf8

If you're building agentic systems or deploying GenAI into enterprise workflows:

  • Feel free to connect!

  • Drop your questions in the comments

  • Or message me — always happy to share and learn

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#LangGraph #GoogleADK #LLM #AIagents #AgenticAI #EnterpriseAI #ITSM #LangChain #GenAI #WorkflowAutomation #AIOrchestration #Newsletter #LekhaWrites

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