Autonomous Multi-Agent Systems

Agentic AI & Deployment

Deploy agents to serverless cloud platforms like Modal for scalable, pay-per-use inference.

pip install modal

import modal

app = modal.App("agent-app")

@app.function(gpu="A100")
def run_agent(prompt: str):
    # Load model and generate
    return response

@app.local_entrypoint()
def main():
    print(run_agent.remote("Analyze this data"))

Advanced RAG with ChromaDB

Use ensemble retrieval with multiple embedding models for better coverage.

from langchain.retrievers import EnsembleRetriever

retriever = EnsembleRetriever(
    retrievers=[chroma_retriever, bm25_retriever],
    weights=[0.7, 0.3]
)

Structured Outputs with Pydantic

from pydantic import BaseModel
from typing import List

class Deal(BaseModel):
    title: str
    price: float
    discount: float
    url: str
    category: str

class DealScanResult(BaseModel):
    deals: List[Deal]
    total_savings: float

Planning Agents

Build agents that plan, execute, and evaluate — creating a Plan → Execute → Review loop.

class PlanningAgent:
    def plan(self, task):
        return self.llm(f"Create a step-by-step plan for: {task}")

    def execute(self, plan):
        for step in plan:
            result = self.execute_step(step)
            yield result

    def review(self, results):
        return self.llm(f"Evaluate these results: {results}")

Final Project: DealAgentFramework

Build a price-is-right deal scanner agent with:

• Web scraping MCP server for deal collection
• RAG agent for deal analysis and comparison
• Planning agent for deal monitoring schedule
• Gradio dashboard for visualization
• Modal deployment for 24/7 operation