Agentic AI for Developers

Build Real-World Autonomous Systems Faster With LangChain, CrewAI & NVIDIA Enterprise Tools

Agentic AI is the next frontier of development — intelligent systems that can reason, plan, take actions, and work across tools and environments without constant human intervention.
For developers, this means building autonomous workflows, domain-aware agents, and production-grade AI applications with the same ease as building a traditional web app.

DoggyDish.com is your launchpad into this new world.

Whether you’re using LangChain, orchestrating multi-agent systems with CrewAI, or deploying high-performance pipelines on NVIDIA Enterprise platforms, this guide will help you get started—fast.

Where to Start: The Three Pillars of Agentic AI Development

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

1. 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

2. 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

3. 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.

Quick Start With an NVIDIA NIM Endpoint

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

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

 

Putting It All Together: A Modern Agentic AI Architecture

LangChain → creates the logic
CrewAI → handles multi-agent coordination
NVIDIA Enterprise → runs everything at scale on real GPU hardware

This combination gives you:

  • Fast development

  • Reliable orchestration

  • Ultra-fast inference

  • Production-ready deployment

It’s the perfect stack for any developer building real agentic systems in 2025.

Starter Project: Your First End-to-End Agentic Workflow

Here’s a minimal blueprint you can build today:

Goal:

Create an agent that researches a topic, summarizes it, writes a report, and stores results.

Stack:

  • LangChain (RAG + tools)

  • CrewAI (writer + researcher + reviewer agents)

  • NVIDIA NIM (fast inference backend)

High-Level Flow:

  1. CrewAI assigns roles

  2. LangChain tools fetch online data

  3. Agents collaborate using CrewAI

  4. LLM inference runs through NVIDIA microservice

  5. Data saved to local DB or vector store

This is the fastest way to build something real.