DIY AI Agents: Build, Assemble, Deploy

[Ratnakar7]

In the world of technology, we often face a familiar dilemma: should we buy a ready-made solution or build one ourselves? I was reminded of this during a recent AI proof of concept (POC). We were working on a voice-based AI agent, and just as we were making progress, a polished commercial solution appeared in the market. The catch? It was more expensive than what we were building.

There is a specific kind of satisfaction known only to those who have "built their own." I remember my college days, staring at the price tag of high-end branded PCs and realizing my budget wasn't even close. Instead of giving up, I bought individual parts such as motherboard, CPU, RAM, HDD, and other required items and assembled my own system at half the price. It was a massive win-win.

The same principle applies today with AI agents: you can build your own agent using open-source and affordable tools instead of subscribing to expensive solutions.

-> The DIY Stack: Assembling the Agent

Example: Voice-AI-Agent POC:

Just like a PC needs a CPU, RAM, and a Case, a Voice AI Agent needs core "components":

• The Brain (Orchestration): Use LangGraph, a low-level framework for stateful, long-running agentic workflows. Perfect for complex tasks like "check a ticket status, then decide whether to escalate."

• The Voice Box (Real-time Framework): Pipecat is an open-source Python framework for real-time voice and multimodal AI. It handles the pipeline (Audio → STT → LLM → TTS → Audio) with low latency.

• The Senses (STT/TTS): Use OpenAI Whisper for Speech-to-Text and ElevenLabs for human-like voice synthesis.

• The Phone Line (Telephony): Twilio acts as the bridge, handling phone calls and streaming audio via WebSockets.

-> The ServiceNow Integration

How It Works

1. Caller speaks → Twilio captures audio.

2. Speech-to-Text → Whisper transcribes the caller's request.

3. Agent Orchestration → LangGraph/Pipecat manage the conversation flow.

4. ServiceNow Integration → Virtual Agent topics and flows create or update tickets.

5. Text-to-Speech → ElevenLabs converts responses back to voice.

6. Caller hears response → Ticket ID, status, or update delivered in real time.
   
   

-> Benefits of DIY AI Agents

• Cost efficiency: Assemble with open-source tools instead of paying for expensive licenses.

• Customization: Tailor workflows to your exact business needs.

• Skill growth: Your team learns cutting-edge AI frameworks.

• Future-proofing: Swap components as better models or APIs emerge.

-> The Verdict: Build vs. Buy

The ready-made market is booming, but the DIY approach offers:

• Cost Efficiency: You only pay for the tokens and minutes you actually use. No "enterprise seat" fluff.

• Zero Vendor Lock-in: Want to swap ElevenLabs for a cheaper open-source TTS tomorrow? You can.

• Deep Customization: Tailor the agent to your specific business logic, not just what a SaaS provider allows.

Sometimes, the best way to move forward is to go back to your roots: buy the parts, learn the framework, and build it yourself.

Thanks,

Ratnakar

[PaulSylo]

HI @Ratnakar7 

Thanks for the wonderful detail out ! this is very nice... One question, instead of lang graph why can't we try still with ServiceNow Ai agent orchestrator itself? or Is this art of possibility with external AI agents, you are suggesting ?

[Ratnakar7]

Hi @PaulSylo ,

Good point! 
The ServiceNow AI Agent Orchestrator is great if your agents live fully inside the platform - it gives you native orchestration, governance, and security.
In the blog I used LangGraph more as a way to show what's possible when you want to experiment with external AI agents or advanced reasoning flows outside ServiceNow.
So if your use case is contained within ServiceNow, the orchestrator works fine; LangGraph was just to illustrate the "art of the possible" beyond the platform.

Thanks,
Ratnakar

[PaulSylo]

@Ratnakar7  Thanks a ton but i like this perspective, Will try out !