Building Trustworthy Agentic/RAG Workflows

​It is common for RAG systems to produce inaccurate/unhelpful responses.

Today, let’s look at how we can improve this using ​Cleanlab Codex​, which is commonly used in production systems to automatically detect and resolve RAG inaccuracies.

Tech stack:

• LlamaIndex for orchestration
• ​Milvus​ as the self-hosted vectorDB
• ​Cleanlab Codex​ to validate the response
• OpenRouterAI to access the latest Qwen3 C

Here's the workflow:

• LLM processes the query to select a tool
• Converts the query into the right format (text/SQL)
• Executes the tool and fetch the output
• Generates a response with enriched context
• Validates the response using Cleanlab's Codex

The video below depicts this entire system in action:

Now, let's see the code!

Set up LLM

We'll use the latest Qwen3, served via OpenRouter.

Ensure the LLM supports tool calling for seamless execution.

Set up SQL Query engine

A Natural Language to SQL Engine turns plain queries into SQL commands, enabling easy data interaction.

Set up RAG Query engine

• Convert PDF, DOCX, or any document to Markdown for Vector Storage with Docling.
• Query Engine fetches context from Milvus, combines it with the query, and sends it to LLM for a response.

Set up tools

Now, it's time to set up and use both the query engines we defined above as tools. Our Agent will then smartly route the query to it's right tools.

Cleanlab Codex Validation

Next, we integrate Cleanlab Codex to evaluate and monitor the RAG app in just a few lines of code:

Create an Agentic workflow

With everything set up, let's create our agentic routing workflow.

Kick off the workflow

With everything set, it's time to activate our workflow.

We begin by equipping LLM with two tools: Document & Text-to-SQL Query.

After that, we invoke the workflow:

Streamlit UI

To enhance user-friendliness, we present everything within a clean and interactive Streamlit UI.

Upon prompting, notice that the app displays a Trust Score on the generated response.

This is incredibly important for RAG/Agentic workflows that are quite susceptible to inaccuracies and hallucinations.

Along with this, we also get specific evaluation metrics along with detailed insights and reasoning for each test run:

​Here’s the Codex documentation →​

​And you can find the code for today’s issue in this GitHub repo →

Thanks for reading!