How to Build Postgres AI Agent

Company databases are rich with data and insights that are valuable to a wide variety of business teams. However, business users lack the technical skill set to leverage company databases.

But with the advent of AI agents, business users can now retrieve insights from databases using natural language prompts. This means that business users no longer need to know SQL to generate insights from databases.

In the following blog, we’ll show you how to build a Postgres AI agent in StackAI, so your business teams can harness PostgreSQL without coding.

Company Databases: Challenges for Business Teams

Business users frequently struggle to fully leverage company databases, largely due to the technical complexity involved. Most enterprise databases are designed with efficiency, scalability, and data integrity in mind, often leading to intricate structures that require specialized knowledge to navigate. 

This can be intimidating for non-technical users who lack the database design expertise needed to understand table relationships, joins, and normalization concepts. As a result, accessing even seemingly straightforward insights can become a daunting task, creating a dependency on IT or data specialists.

Another significant barrier is the disconnect between raw data and business context. Databases typically store data in a highly structured, fragmented form that mirrors the underlying technical architecture rather than the way business users think about their workflows. 

For example, customer data might be spread across multiple tables and systems, making it difficult to assemble a complete, accurate picture without deep technical skills. This gap between technical data representation and business-friendly insights can lead to misinterpretations and missed opportunities.

Finally, data accessibility and governance issues further complicate matters. Business users often encounter strict access controls, complex query requirements, and outdated interfaces, which can stifle agility and innovation. 

Additionally, without proper data governance in place, there's a risk of relying on outdated, inconsistent, or incomplete data, which can lead to poor decision-making. Overcoming these hurdles requires not just technical tools but also a cultural shift towards greater data literacy and empowerment across the organization.

AI Agents: Access Company Databases without Coding

Fortunately, AI agents are beginning to reverse this trend by acting as intelligent intermediaries between business users and their data. These agents can interpret natural language queries, allowing users to ask questions in plain English without worrying about the underlying database schema or complex SQL commands. This approach dramatically reduces the learning curve and empowers a broader range of employees to access and leverage critical data independently.

Moreover, AI agents excel at integrating data from multiple sources, breaking down silos that traditionally complicate data retrieval. By stitching together data from different tables, systems, and even external sources, they can present a unified, context-rich view of business information. This not only improves data accessibility but also ensures that decision-makers have the full context needed to drive impactful strategies.

Finally, these agents are also transforming data governance and quality. Advanced AI can automatically detect anomalies, flag outdated records, and enforce data consistency rules, significantly reducing the risk of using flawed or incomplete data. This proactive approach to data management not only boosts user confidence but also accelerates the pace of data-driven decision-making, making businesses more agile and responsive to market changes.

PostgreSQL AI Agent: How to Build

Here’s how to build the PostgreSQL AI Agent. First, make sure to sign up for a free StackAI account. 

Navigate to the account dashboard. Click ‘New Project’.

Click the ‘Workflow Builder’ option.

From here, choose the PostgreSQL AI Agent template. 

The template will launch a pre-built workflow for the AI agent.

Let’s take a look at each component of the workflow. First, we have the Input box (“Question”). This allows a user to type in a natural language query, which will then be converted into a SQL query.

Next, the Text-to-SQL node converts the plain language query into a SQL query. You can choose the SQL language from the drop down. 

The Postgres node allows you to connect to a Postgres database. 

Go to the settings gear to configure your Postgres database.

Click “New connection”. Enter your Postgres credentials into the appropriate fields.

Click “Create connection”. The connection will now be live.

Next, the LLM will run the SQL query against the database to return a natural language answer. The LLM is Anthropic — Claude 3.5 Sonnet. 

Finally, the result of the query is outputted to the user.

No go to the Export tab. 

Give the AI agent a name and a description.

Save the interface. Click the link to the web app.

This will launch the AI agent in your browser. You can ask questions of the PostgreSQL database.

The AI agent will return an answer from PostgreSQL. In this case, there are 231 products in stock.

Launch Postgres AI Agent Now!

It’s difficult for non-technical teams to access operational databases such as PostgreSQL. The data is very valuable, but business users do not know SQL, and they cannot extract the information they need. 

But now, AI agents are allowing business users to retrieve critical insights from databases such as PostgreSQL with natural language queries. This means non-coders can leverage vital database data on their own.

Sign up for a free account with StackAI to launch the PostgreSQL AI assistant now! Enable your business teams to harness the critical data in your company’s databases.