Why MindsDB is the Fastest Way to Build AI Agents Today

Hi, In an era where building AI-powered applications often feels like assembling a spaceship from scratch, I discovered a different path — MindsDB. As a developer working on KbNet, I needed a way to automatically generate summaries of knowledge base articles using AI. Instead of setting up complex machine learning pipelines, I used MindsDB and built a working AI flow in hours — not days.

In this article, I’ll walk you through why MindsDB makes AI development so seamless, how it compares to traditional approaches, and how I used it to power a real-world feature in my app — a smart summary generator based on knowledge graph traversal.

The Problem Statement #

I have structured data in a database that represents a knowledge graph. Each node represents a concept or article, and maps are AI flows — sequences of nodes leading to the current point in the journey.

Now, I wanted to build an AI Agent that, when given a simple prompt and a map reference ID, could:

1. Retrieve all nodes associated with that map.
2. Understand the sequence or path taken to reach the current node.
3. Generate a rich, context-aware summary — in natural language — that sounds more like a personal journal than a SQL dump.

Sounds fun, right? Except…

The Traditional Route — A Long Road #

If I had taken the usual route, here’s what I’d be doing:

1. Backend Setup #

Start with writing code in Python, Node, or Java. Fetch nodes using raw SQL or an ORM. Serialize the data, clean it, make it usable. Basically, your app becomes a data plumber.

2. LLM Integration #

Pick an AI provider (OpenAI, Hugging Face, etc). Write wrapper code to format prompts, manage tokens, and handle the response. Also, hello API key management and rate limits.

3. Agent Logic #

You now write logic to:

• Order the nodes by step_index or timestamp
• Stitch the context manually
• Format a prompt that gives the LLM just enough — but not too much — info
• Clean and parse the LLM’s response
• Retry if it fails, maybe fallback if the summary is junk

4. API Layer #

Wrap all this in a nice endpoint. Add retries, validation, timeouts, and logs for the logs that watch other logs.

🕒 Total Time: Days (even weeks, depending on coffee supply)
⚙️ Complexity: High
🧩 Modularity: Low. Everything’s tightly glued
🚧 Maintenance: Fragile. Tweak one piece, test everything again

I’ve done it this way before. It works, but it’s definitely not something you build in a weekend — unless you skip sleep and friends.

Enter MindsDB — Same Problem, Way Simpler #

Now here’s where MindsDB shines. Instead of juggling a dozen moving parts, I was able to handle everything with a single SQL job and an AI agent declaration.

The Agent in One Command #

Here’s the entire setup to create the AI Agent:

CREATE AGENT IF NOT EXISTS summary_agent
USING
  model = 'gemini-2.0-flash',
  google_api_key = 'your_key_here',
  include_knowledge_bases = ['kbnet_kb'],
  include_tables = [
    'db.maps',
    'db.nodes',
    'db.navigation_steps',
    'db.node_relationships'
  ],
  prompt_template = '...long descriptive prompt...';

Done.

This agent now understands:

• The map structure
• The nodes the user has explored
• The steps they’ve taken
• And it generates a journal-style narrative of their journey

It’s declarative. You don’t write logic — you explain the data and intent, and MindsDB takes over.

The System Prompt (What the Agent “Knows”) #

"You are writing a reflective, journal-style narrative of a user's learning journey through a dynamic map of interconnected topics.

You have access to the sequence of topics, summaries, and actions taken (e.g., deeper, related, similar, backtracking).
(db.maps) all the user maps has [map_id]
(db.nodes) all the nodes has [summary]
(db.navigation_steps) steps taken by user has source and target [node_id]
(db.node_relationships) relationships between nodes

Reconstruct the journey as a first-person story. Make it immersive, curious, and human. Use topic connections and user direction to build a narrative arc."

That’s all. The rest — connecting the dots, generating the response — is handled internally by MindsDB.

The Job That Ties It Together #

I created a scheduled job in SQL that:

1. Picks a pending summary request.
2. Sends it to the agent.
3. Stores the final summary and updates the status.

CREATE JOB IF NOT EXISTS summary_job AS (
  UPDATE map_summaries SET status = 'IN_PROGRESS'
  FROM (SELECT * FROM pending_summary_view LIMIT 1) AS d
  WHERE id = d.id;

  UPDATE map_summaries SET status = 'COMPLETED', summary = d.answer, completed_at = NOW()
  FROM (
      SELECT p.id, r.answer
      FROM summary_agent AS r
      JOIN (
          SELECT id, question FROM pending_summary_view WHERE status = 'IN_PROGRESS'
      ) AS p
      ON r.question = p.question
  ) AS d
  WHERE id = d.id;
)
EVERY 5 MINUTES
IF (SELECT COUNT(*) > 0 FROM pending_summary_view WHERE status IN ('PENDING', 'IN_PROGRESS'));

Now, every 5 minutes, MindsDB checks if there’s work to do. If yes, it runs the agent, updates the database, and done.

🧪 Using the Agent is Just a Query #

No API calls. No webhook hell. Just SQL.

SELECT answer FROM summary_agent WHERE question = 'Summarize the journey of map-id: xyz';

That’s the whole interface. Even the question field acts as the input channel.

💡 Why This Blew My Mind #

• 🔥 Agent logic is declarative — no backend to maintain
• 🧩 It scales naturally — just add jobs or agents
• 🤹 Switching providers is trivial — change model = 'model-name' and <provider>_api_key.
• ⚙️ It runs inside the data layer — no glue code or extra microservices

I didn’t write a backend service, and I didn’t have to babysit an LLM. I wrote SQL. That’s it.

🧵 Final Thoughts #

If you’re someone who’s been through the mess of building LLM agents manually — gluing APIs, syncing data, writing retry logic — you’ll appreciate the simplicity of MindsDB.

It feels like what SQL should’ve always been:
Declarative data + Declarative AI = Just works.

With KbNet, this gave me a fast path to production without needing to build infra from scratch. MindsDB let me focus on what mattered — the experience — instead of the plumbing.

If you’re working on something that could use an AI brain without a DevOps nightmare, give MindsDB a try. I’m not being paid to say this — I just had a good time not writing boilerplate code for once.

And honestly, that’s rare. Usually, the only thing AI helps me generate is more bugs.

Priyanshu Verma

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