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:
Google ADK (Agent Developer Kit) for modular tool-based agent logic
LangGraph for stateful, branching agent orchestration
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:
→ “Network Issue” with 0.85 confidence
→ “Medium”
→ confidence > 0.75 → no escalation
→ 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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