Dust: Enterprise AI Agent Platform

1. 1
   Define Agent Purpose & Scope

   Clearly articulate the specific business problem the AI agent will solve. Identify target users, key tasks to automate, and desired outcomes through stakeholder interviews and requirements gathering.
2. 2
   Set Up Development Environment

   Install necessary programming languages (e.g., Python), AI/ML frameworks (e.g., LangChain, LlamaIndex), and configure API keys for LLM providers (e.g., OpenAI, Anthropic) and cloud platforms (AWS, Azure, GCP).
3. 3
   Integrate Internal Knowledge Bases (RAG)

   Connect your AI agent to proprietary company data sources (documents, databases, internal wikis). Ingest and process this data, typically into a vector store, to enable Retrieval Augmented Generation (RAG) for context-aware responses.
4. 4
   Connect to Business Tools via APIs

   Develop or configure tools that allow the AI agent to interact with existing enterprise systems (CRM, ERP, HR systems) through their APIs. Define the agent's capabilities to use these tools for specific actions or data retrieval.
5. 5
   Develop and Test the Custom Agent

   Construct the agent's logic using your chosen AI framework, orchestrating its use of LLMs, knowledge retrieval, and tool execution. Rigorously test the agent's performance, accuracy, and adherence to business rules.
6. 6
   Deploy and Secure the Agent

   Containerize your agent (e.g., Docker, Kubernetes) and deploy it on a secure cloud platform. Implement robust security measures, including access control (RBAC), data privacy (GDPR, HIPAA compliance), and secure API management.

import os
from langchain_community.document_loaders import TextLoader
from langchain_community.vectorstores import Chroma
from langchain_openai import OpenAIEmbeddings, ChatOpenAI
from langchain.text_splitter import CharacterTextSplitter
from langchain.chains import RetrievalQA

# --- Prerequisites: Install libraries ---
# pip install langchain-community langchain-openai chromadb openai

# --- 1. Set your OpenAI API key ---
# Replace 'YOUR_OPENAI_API_KEY' or set as an environment variable (e.g., export OPENAI_API_KEY='...')
# os.environ["OPENAI_API_KEY"] = "YOUR_OPENAI_API_KEY"

# --- 2. Create a dummy knowledge base file ---
knowledge_file = "company_knowledge.txt"
with open(knowledge_file, "w") as f:
    f.write("Our company's main product is the 'Quantum Widget 5000'.\n")
    f.write("It was launched in Q3 2023.\n")
    f.write("Key features include AI-powered analytics and a modular design.\n")
    f.write("Our headquarters are in San Francisco.\n")
    f.write("The CEO is Dr. Eleanor Vance.")

# --- 3. Load documents ---
loader = TextLoader(knowledge_file)
documents = loader.load()

# --- 4. Split documents into chunks ---
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
texts = text_splitter.split_documents(documents)

# --- 5. Create embeddings and store in a vector database (Chroma) ---
embeddings = OpenAIEmbeddings()
db = Chroma.from_documents(texts, embeddings)

# --- 6. Initialize a retriever from the vector database ---
retriever = db.as_retriever()

# --- 7. Initialize the LLM ---
llm = ChatOpenAI(temperature=0) # Uses gpt-3.5-turbo by default

# --- 8. Create a RetrievalQA chain ---
qa_chain = RetrievalQA.from_chain_type(llm, chain_type="stuff", retriever=retriever)

# --- 9. Query the agent ---
print("\n--- Querying the Agent ---")
queries = [
    "When was the Quantum Widget 5000 launched?",
    "Where are our headquarters located?",
    "Who is the CEO?",
    "What are the key features of our main product?"
]

for query in queries:
    response = qa_chain.invoke({"query": query})
    print(f"Query: {query}")
    print(f"Response: {response['result']}\n")

# --- Clean up the dummy file ---
os.remove(knowledge_file)
print(f"Cleaned up: {knowledge_file}")