Agent in a Box

Enterprise RAG-Driven Compliance & Policy Agent (Oracle AI Studio)

operations

Enterprise RAG-Driven Compliance & Policy Agent (Oracle AI Agent Studio)

Problem Statement

Large enterprises operating within Oracle Cloud Infrastructure (OCI) often struggle with "Internal Documentation Fragmentation." Critical operational data—ranging from security policies and compliance mandates to internal SOPs—is scattered across OCI Object Storage, decentralized databases, and PDF repositories. Employees and developers spend an average of 3-5 hours per week searching for specific internal protocols (e.g., "What is our specific disaster recovery RTO for Tier-1 databases in the London region?").

Traditional keyword search fails because it cannot synthesize context across multiple documents. Furthermore, generic LLM solutions pose a data sovereignty risk; enterprises cannot send proprietary internal mandates to public third-party APIs without violating compliance. Startups and mid-market firms using Oracle AI implementation strategies need a way to leverage their private data securely. The challenge lies in the complex orchestration of OCI Generative AI services, Vector Databases (OCI Search with OpenSearch), and the newly released Oracle AI Agent Studio. Without a structured implementation blueprint, firms face high latency, "hallucinations" of internal policy, and high compute costs due to inefficient retrieval-augmented generation (RAG) pipelines.

What the Agent Does/Doesn't Do

  • Does: Ingests unstructured OCI Object Storage data, indexes it into OCI OpenSearch, and provides a natural language interface for policy queries with direct source citations.
  • Does: Enforces data privacy by keeping all inference within the OCI tenancy, similar to our Autonomous Vendor Risk Assessment & Security Questionnaire Agent.
  • Does: Routes complex queries to human experts if the confidence score in the retrieved context is below 0.7.
  • Doesn't: Execute cloud infrastructure changes (e.g., it won't resize a bucket, only tell you the policy for doing so). For infrastructure management, see the Autonomous Cloud FinOps & Infrastructure Optimization Agent.
  • Doesn't: Provide legal advice; it strictly summarizes existing internal documentation.

Workflow

  1. Ingestion: OCI Data Integration pulls PDFs/Docs from OCI Object Storage.
    • Input: Raw files. Output: Cleaned text chunks.
  2. Embedding: Text chunks are sent to OCI Generative AI (using Cohere Embed model).
    • Input: Text. Output: Vector embeddings.
  3. Indexing: Embeddings are stored in OCI Search with OpenSearch (Vector Store).
    • Input: Vectors. Output: Searchable index.
  4. Retrieval & Synthesis: Oracle AI Agent Studio intercepts user queries, performs a vector search, and feeds context to the LLM (Llama 3 or Command R).
    • Input: User query. Output: Context-aware response + Source links.
  5. Validation: Agent checks the response against a "Safety Filter" and "Groundedness" check, a process also utilized in our Document Q&A Agent.
    • Input: Generated text. Output: Verified response or "Escalation" trigger.

Tool Stack

  • Oracle AI Agent Studio - Core orchestration for RAG-based agents.
  • OCI Generative AI Service - Managed LLM service (Cohere/Llama models).
  • OCI Search with OpenSearch - Managed vector database for high-performance retrieval.
  • OCI Object Storage - Highly durable data lake for raw policy documents.
  • Oracle Integration Cloud (OIC) - Enterprise connectivity for Slack/Teams.
  • Slack - Collaboration interface for agent interaction.
  • Microsoft Teams - Enterprise communication hub.

Quick Integration

Querying the AI Agent via OCI Python SDK

import oci

# Configuration for OCI authentication
config = oci.config.from_file()

# Initialize the Generative AI Agent Runtime client
agent_runtime_client = oci.generative_ai_agent_runtime.GenerativeAiAgentRuntimeClient(config)

# Replace with your specific Agent Endpoint OCID from AI Agent Studio
agent_endpoint_id = "ocid1.genaiagentendpoint.oc1.iad.example_ocid"

def ask_compliance_agent(query):
    chat_details = oci.generative_ai_agent_runtime.models.ChatDetails(
        user_message=query,
        should_stream=False
    )
    
    try:
        response = agent_runtime_client.chat(
            agent_endpoint_id=agent_endpoint_id,
            chat_details=chat_details
        )
        return response.data.message.content.text
    except Exception as e:
        return f"Error: {str(e)}"

if __name__ == "__main__":
    user_query = "What is our specific disaster recovery RTO for Tier-1 databases in the London region?"
    answer = ask_compliance_agent(user_query)
    print(f"Agent Response: {answer}")

Source: OCI API Reference

Vector Search via OCI OpenSearch

from opensearchpy import OpenSearch, RequestsHttpConnection
from requests_aws4auth import AWS4Auth

endpoint = 'your-opensearch-endpoint.region.oci.oraclecloud.com'
region = 'us-ashburn-1'
awsauth = AWS4Auth('YOUR_OCI_ACCESS_KEY', 'YOUR_OCI_SECRET_KEY', region, 'opensearchservice')

client = OpenSearch(
    hosts=[{'host': endpoint, 'port': 443}],
    http_auth=awsauth,
    use_ssl=True,
    verify_certs=True,
    connection_class=RequestsHttpConnection
)

query = {
  'size': 5,
  'query': {
    'multi_match': {
      'query': 'disaster recovery RTO',
      'fields': ['title', 'content']
    }
  }
}

response = client.search(body=query, index='compliance-policies')

Source: OCI OpenSearch Ingestion Docs

Prompt Skeletons

(Existing prompt skeletons would be listed here)