Agentic AI for Developers

Turnkey Precision vs. Open-Source Sovereignty

As a developer building autonomous workflows, domain-aware agents, and production-grade AI applications means planning ahead. This guide is not extensive, and with the advent of new applications EVERY WEEK, there is no one-size-fits all. The goal here is to make sure you understand core-concepts that will be useful towards your future planning.

DoggyDish.com is your launchpad into this new world.

As you enter the realm of Agentic AI for Developers, you can expect traditional concepts around application development planning, testing, scaling to be familiar. Do not be distracted yourself with the hype, shiny objects, that will take you off course and waste unnecessary time and resources.

Let’s break down the Developer Agentic AI Stack

Layer	Component Stack	What it does (The Analogy)
User Interface	Slack, Web App, Custom UI	The Dashboard: Where agent interacts.
Agent Orchestration	CrewAI / LangGraph	The Manager: Assigns roles to different agents.
Application Logic	LangChain / LlamaIndex	The Brain: Handles RAG, memory, and API calls.
Inference Interface	NVIDIA: NIM AMD: HUGS	The Engine: Provides the model via a standard API.
Model / Safety	NVIDIA: NeMo (Models + Guardrails) AMD: Llama 3 + HF Safety	The Guard: Filters toxic or off-topic responses.
Training / Optimization	NVIDIA: NeMo Training / Megatron AMD: PyTorch + DeepSpeed	The Factory: builds the models
Driver / Rundtime	NVIDIA: CUDA AMD: ROCm	The Fuel: How the software talks to the silicon.
Infrastructure/Hardware	NVIDIA: H100/H200/B200/V200 AMD: MI300X/350/355/400	The Physical Machine: The actual GPU hardware.

Below is the simplified developer menu to launch your first agentic system.
Pick one—or combine all three for maximum capability.

Choose Your Infrastructure

When deploying agentic AI applications built on LangChain, your infrastructure choice matters as much as your model choice. The goal isn’t “most powerful GPU,” it’s right GPU in the right place for the workload. Below is a fast, decision-oriented guide to help you choose between Cloud, NeoCloud, and On-Prem using the RTX PRO 6000 as the reference point.

Option 1: Cloud (Hyperscalers)

Examples: AWS, Google Cloud, Microsoft Azure

Typical RTX-class cost: ~$1.00–$1.50 per GPU/hour (nearest equivalents)

Option 2: NeoCloud (GPU-First Providers)

Examples: CoreWeave, Vultr, Lambda-style GPU clouds

Typical RTX-class cost: ~$0.50–$1.00 per GPU/hour

Option 3: On-Prem (Owned Infrastructure)

Examples: Supermicro, enterprise GPU servers, colocated racks

Effective RTX PRO 6000 cost: ~$0.35–$0.50 per GPU/hour (amortized)

Full Article on Comparison

Quick Decision Guide

Your Situation	Best Fit
Prototyping / experimentation	Cloud
Production inference, cost-sensitive	NeoCloud
24×7 steady workloads	On-Prem
Training bursts	Cloud or NeoCloud
Regulated or private data	On-Prem

Key Takeaway

Cloud optimizes for convenience.
NeoCloud optimizes for GPUs.
On-Prem optimizes for ownership.

Using the RTX PRO 6000 as a baseline makes the tradeoffs clear — and the same logic applies to AMD MI355X. Whether NVIDIA or AMD, the real decision isn’t the silicon, it’s how long, how often, and how predictably you plan to use it.

NVIDIA NeMo is built for teams that need full control over AI models powering agentic systems—from training and fine-tuning to inference at GPU scale.
It’s not about quick demos; it’s about production-grade agents running reliably, securely, and efficiently.

It gives you building blocks for:

Custom LLM training & fine-tuning
Large-scale inference pipelines
RAG with enterprise data sources
Speech, vision, and multimodal models
GPU-optimized deployment (TensorRT, Triton)

It’s ideal for teams who want something that works for years in production, not just a weekend prototype.

Quick Start Code (Python)

				
					from nemo.collections.nlp.models import GPTModel
from nemo.core.config import hydra_runner

@hydra_runner(config_path="conf", config_name="gpt_config")
def main(cfg):
    model = GPTModel(cfg)
    model.train()

if __name__ == "__main__":
    main()

This example shows the foundation: defining, training, and scaling a custom model that can later power agentic workflows.

When to Use NVIDIA NeMo

✔ Enterprise or regulated environments
✔ Custom model ownership required
✔ GPU-dense infrastructure available
✔ Long-term agent scalability needs

NVIDIA AI Enterprise — Best for High-Performance, GPU-Optimized Deployment

Once your pipeline works, you need it to run fast, secure, and scalable.
This is where NVIDIA Enterprise tools come in:

You Get:

NIM Microservices (NVIDIA AI Inference Microservices)
Pre-built optimized endpoints for:
LLMs, RAG, vision models, speech, embeddings.
TensorRT-LLM & Triton
Production-class inference acceleration.
NGC Containers
Fully optimized containers for training, fine-tuning, and deployment.
NeMo
For training & customizing high-performance LLMs.

Then call it from Python:

				
					import requests

response = requests.post(
"http://localhost:8000/v1/chat/completions",
json={"model": "meta-llama3-70b", "messages": [{"role": "user", "content": "Hello!"}]}
)

print(response.json())

When to Use NVIDIA Enterprise

✔ You need real production throughput
✔ You need predictable cost-per-token
✔ You’re deploying on-prem or onto Supermicro GPU racks
✔ You require enterprise-grade security

				
					docker run --gpus all -p 8000:8000 \
nvcr.io/nvidia/nim/text-generation:latest

Quick Start With an NVIDIA NIM Endpoint

Hugs

HF + PyTorch + DeepSpeed

LangChain — Best for Rapid Prototyping & Tool-Based Agents

LangChain is the fastest way to get from idea → prototype → working agent.
It gives you building blocks for:

Tool use (APIs, databases, browsers, functions)
RAG (Retrieval-Augmented Generation)
Agent executors
Memory management
Multi-step reasoning

It’s ideal for devs who want to build something that works in 30 minutes, not 30 days.

Quick Start Code (Python)

				
					


from langchain_openai import ChatOpenAI
from langchain.agents import initialize_agent, load_tools

llm = ChatOpenAI(model="gpt-4o-mini")
tools = load_tools(["serpapi", "llm-math"], llm=llm)

agent = initialize_agent(
tools, llm, agent="zero-shot-react-description", verbose=True
)

agent.run("Find the distance between San Jose and Tokyo and convert it to miles.")

When to Use LangChain

✔ Fast prototyping
✔ Single-agent workflows
✔ Applications needing external tool use
✔ RAG-enabled business logic

CrewAI — Best for Multi-Agent Teams & Autonomous Workflows

CrewAI focuses on collaborative, team-based agents with defined roles:
Researchers, planners, analysts, engineers — each with their own strengths.

CrewAI shines when tasks require coordination, such as:

Content pipelines
Data analysis
Report generation
Coding assistants
Marketing or SEO workflows
Multi-step enterprise tasks

Quick Start Code (Python)

				
					from crewai import Agent, Task, Crew

researcher = Agent(name="Researcher", goal="Find accurate data.")
writer = Agent(name="Writer", goal="Produce clear explanations.")

task = Task(description="Explain agentic AI for beginners.", agents=[researcher, writer])

crew = Crew(agents=[researcher, writer], tasks=[task])
output = crew.run()

print(output)

When to Use CrewAI

✔ Multi-agent collaboration
✔ Pipelines & long-lived workflows
✔ Tasks requiring validation or cross-checking
✔ Enterprise operations automation

Putting It All Together: Why This Stack

When building agentic AI, the biggest mistake beginners make is starting at the top — picking tools before thinking about where and how everything will run.

This stack is chosen in reverse order on purpose.

We start with real infrastructure, then layer upward so each decision stays simple, affordable, and easy to change later.

Real GPU first → so performance is never a mystery
Standard inference layer → so models can be swapped without rewrites
Simple logic layer → so behavior stays understandable
Agent roles last → so complexity only appears when needed

This approach avoids lock-in, avoids over-engineering, and keeps costs predictable.

What This Stack Optimizes For

Learning by building real systems
Paying only for what you use
Scaling without redesigning
Keeping mental models simple

Why This Matters

You’re not choosing tools — you’re choosing a path.

This stack works on one GPU today, scales to NeoCloud tomorrow, and supports On-Prem later, making it a practical foundation for building real agentic AI systems.

Starter Project: Your First Agentic AI System

This is the easiest way to build a real agentic AI project without overthinking it.

Step 1: Infrastructure (Where It Runs)

Start with DigitalOcean.

Rent one GPU server
No contracts
Shut it off when you’re done

You get real AI hardware without buying anything.

Step 2: Model Access (The AI Brain)

Use NVIDIA NIM.

Your app sends questions
NIM sends back answers
No model training required

You focus on building, not managing AI models.

Step 3: Application Logic (How It Thinks)

Use LangChain.

Connects AI to tools
Lets it search, read, and decide
Controls what happens next

This is what turns AI into an assistant.

Step 4: Agents (Who Does the Work)

Use CrewAI.

Researcher agent
Writer agent
Reviewer agent

Each agent has one job.

Step 5: Memory (What It Saves)

Save summaries
Store results for later use

The AI doesn’t start from zero each time.

What This Project Can Do

Research a topic
Write a report
Save the result
Run again with new topics

All on one GPU.

What This Might Cost (Simple Estimate)

If you’re just learning or testing

GPU on DigitalOcean: ~$1.50 per hour
Use it 2–3 hours a day
~$90–$135 per month

Cheapest way to learn real agentic AI.

If you’re building something more serious

GPU running most of the day
~$700–$1,000 per month

Still cheaper than hiring help or buying hardware.

If you stop the server when not using it

Cost drops fast
Pay only when it’s on

You control the bill.

Why This Is a Smart Starting Point

No big upfront cost
Real production-style setup
Easy to shut down
Easy to scale later

DoggyDish Takeaway

Start small.
Pay by the hour.
Learn on real hardware.

Once this makes sense, moving to NeoCloud or On-Prem is just a cost decision — not a technical one.

Agentic AI for Developers

Let’s break down the Developer Agentic AI Stack

Layer

Component Stack

What it does (The Analogy)

Choose Your Infrastructure

Option 1: Cloud (Hyperscalers)

Option 2: NeoCloud (GPU-First Providers)

Option 3: On-Prem (Owned Infrastructure)

Quick Decision Guide

Key Takeaway

Quick Start Code (Python)

When to Use NVIDIA NeMo

NVIDIA AI Enterprise — Best for High-Performance, GPU-Optimized Deployment

You Get:

When to Use NVIDIA Enterprise

Quick Start With an NVIDIA NIM Endpoint

Hugs

HF + PyTorch + DeepSpeed

LangChain — Best for Rapid Prototyping & Tool-Based Agents

Quick Start Code (Python)

When to Use LangChain

CrewAI — Best for Multi-Agent Teams & Autonomous Workflows

Quick Start Code (Python)

When to Use CrewAI

Putting It All Together: Why This Stack

What This Stack Optimizes For

Why This Matters

Starter Project: Your First Agentic AI System

Step 1: Infrastructure (Where It Runs)

Step 2: Model Access (The AI Brain)

Step 3: Application Logic (How It Thinks)

Step 4: Agents (Who Does the Work)

Step 5: Memory (What It Saves)

What This Project Can Do

What This Might Cost (Simple Estimate)

If you’re just learning or testing

If you’re building something more serious

If you stop the server when not using it

Why This Is a Smart Starting Point

DoggyDish Takeaway