How to Implement a Multi-Agent System for a Text-to-SQL Problem | Video

Most organizations store large amounts of structured and unstructured data in PostgreSQL, MongoDB, or other relational and non-relational databases. However, non-technical users, such as HR or operations teams, often lack direct access to these insights.

Our goal was to bridge that gap by developing a Text-to-SQL chatbot that interprets natural language queries and generates executable SQL or MongoDB commands.

The system was implemented using LangChain, LangGraph, and Azure OpenAI, and followed a multi-agent architecture to ensure modularity and traceability.

This project aimed to explore that idea through a practical Text-to-SQL prototype/implementation.

Why build multiple agents instead of one big AI brain?

We learned quickly that good AI doesn’t come from one “genius” model; it comes from a team of specialists.
Instead of a single monolithic agent, we built a Multi-Agent System for Text-to-SQL, where each AI component had a focused, well-defined role.

The system consisted of five specialized agents:

Here’s how our architecture worked:

• Supervisor (Orchestrator): routes user queries through the appropriate agents.
• Query Parser: identifies the relevant database, schema, and tables.
• Query Generator: formulates SQL or MongoDB queries based on parsed parameters.
• Query Executor: runs the query and retrieves results.
• Result Generator: converts database output into a natural language response.

This modularity made the system easy to debug and highly reliable. If a query failed, we had enough information to determine which step the problem originated in. Having a good level of traceability is key for good debugging!

How does a user request actually move through the system?

Let’s walk through a simple example.
Suppose an HR manager asks:

“What is the status of job opening ID 2742803?”

The Supervisor receives this request and triggers the specialized agents one by one. Here is an example:

How do you align an LLM with your Data Structure?

Large language models like GPT or Claude are powerful, but they don’t automatically know your company’s data structure. They need a map.

To enable schema-aware reasoning, we provided the Query Parser with a structured JSON schema describing both PostgreSQL and MongoDB databases. This schema included table names, relationships, and plain-English metadata.

Few-shot examples were added to the prompt to ground the model in realistic question-to-query mappings

• Each table and column name
• Their relationships and meanings
• Simple English descriptions (so the AI knows that “cand_fb_scr” = “candidate feedback score”)
  

What happens when the AI fails, and how can it be prevented?

Here’s the hard truth: every AI system fails sometimes.
The difference between a prototype and a production-ready system is how well it handles failure.

We designed multiple fallback mechanisms across the pipeline to keep the chatbot stable:

To improve fault tolerance, we implemented fallback mechanisms at multiple stages.

For example:

• If a query referenced a missing table, the system retried with corrected syntax.
• If a query used SQL syntax for MongoDB, the format was automatically adjusted.
• In case of API or execution errors, the request was logged and retried with user feedback

These mechanisms increased robustness and made the prototype/implementation/system more reliable in practice.  And since each agent handled one job, debugging was fast and transparent.

How can we make systems like this even smarter?

Building a Multi-Agent Text-to-SQL system required balancing flexibility, modularity, and reliability.

Using a framework-based approach (LangChain + LangGraph) allowed us to experiment quickly and isolate each stage of the reasoning pipeline.

Future work includes integrating RAG for contextual awareness and incorporating automated evaluation frameworks such as DeepEval or Arize Phoenix to monitor performance at scale.

This experience highlights how applied AI engineering is moving from handcrafted prototypes to reproducible, production-grade systems.

This demonstrates how modular AI systems can move from research concepts to practical enterprise tool.

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