Building a Semantic Layer for Agentic AI with EDB Postgres and Amazon Bedrock

As modern enterprises transition from basic chatbot interactions to autonomous agentic AI workflows, the integrity, security, and context of underlying data have never been more critical. Autonomous agents require real-time access to operational data, but exposing raw database schemas directly to large language models often leads to hallucinated queries, broken join paths, and severe security violations. To solve this architectural challenge, developers are increasingly turning to a governed semantic layer embedded directly within the database engine.

In this deep-dive review and implementation guide, we explore how to construct a semantic layer inside EDB Postgres AI and seamlessly query it using a Strands Agents agent on Amazon Bedrock AgentCore. By leveraging native database features such as schema definitions, views, and row-level security (RLS), we ensure that business logic and access control live where the data lives. Furthermore, routing these high-throughput model requests through a reliable LLM API aggregator like n1n.ai guarantees enterprise-grade stability and ultra-low latency.

The Architecture of Agentic Semantic Layers

Traditional Retrieval-Augmented Generation (RAG) architectures often rely on external vector stores that are disconnected from the primary transactional database. While this works for simple document retrieval, complex multi-step agentic workflows demand structured business logic. An agent interacting with a database needs to understand foreign keys, calculated metrics, temporal filters, and strict tenant boundaries without requiring custom procedural code in every application layer.

By centralizing the semantic definitions within EDB Postgres AI, the database itself becomes the source of truth for both human applications and AI agents.

-- Example: Creating a governed semantic view for agentic consumption
CREATE VIEW semantic_customer_lifetime_value AS
SELECT 
    c.customer_id,
    c.company_name,
    c.region,
    SUM(o.total_amount) AS total_spent,
    COUNT(o.order_id) AS total_orders,
    MAX(o.order_date) AS last_order_date
FROM 
    core_customers c
JOIN 
    core_orders o ON c.customer_id = o.customer_id
WHERE 
    o.status = 'COMPLETED'
GROUP BY 
    c.customer_id, c.company_name, c.region;

This view encapsulates complex join paths and business metrics. When an agent queries this view, it receives pre-calculated, validated business logic rather than parsing raw, unnormalized tables.

Enforcing Access Control with Row-Level Security (RLS)

Security is the primary bottleneck preventing enterprises from deploying autonomous agents into production environments. If an agent has access to a database connection string, it can potentially read, update, or delete data outside its authorized scope.

EDB Postgres AI solves this by enforcing native Postgres GRANT privileges and Row-Level Security (RLS) policies. When an agent connects to the database via Amazon Bedrock AgentCore, the session can be configured to execute under a specific database role with strict RLS predicates.

-- Enable Row-Level Security on the semantic view's underlying tables
ALTER TABLE core_orders ENABLE ROW LEVEL SECURITY;

-- Create a security policy restricting agents to their assigned region
CREATE POLICY agent_region_isolation_policy ON core_orders
    FOR SELECT
    USING (region = current_setting('app.current_agent_region', true));

This guarantees that even if an autonomous agent attempts to generate a query that bypasses logical filters, the storage engine itself blocks unauthorized data access at the kernel level.

Integrating with Amazon Bedrock AgentCore and Strands Agents

Amazon Bedrock AgentCore provides the orchestration runtime necessary for building sophisticated multi-agent systems. By combining Bedrock models with Strands Agents, developers can define tools that execute SQL queries against our EDB Postgres semantic layer.

Below is a Python implementation snippet showing how a Strands Agent tool connects to EDB Postgres AI, executes a query safely, and processes the output before feeding it back to the language model. When managing external model endpoints or switching between various foundation models during agent execution, developers can route their calls through n1n.ai to optimize cost and performance metrics.

import psycopg2
from strands_agents import Agent, tool

@tool(description="Query customer semantic metrics from EDB Postgres")
def get_customer_ltv(region_name: str) -> str:
    connection = psycopg2.connect(
        dbname="enterprise_db