I Built Convodb So You Can Talk to Your Database (And It Talks Back With 4 AI Agents)

This is a submission for the TigerData Agentic Postgres Hackathon Challenge

When PostgreSQL Met AI Agents (And I Went Down the Rabbit Hole) 🤯

So there I was, scrolling through dev.to, when I saw the TigerData Agentic Postgres hackathon announcement. My first thought? "Cool, another database." My second thought, after reading for 5 minutes? "WAIT, DATABASES CAN DO THAT?!" 🤩

Let me tell you about the most exciting research rabbit hole I've gone down in months, and why I'm now convinced that Agentic Postgres might just be the missing piece for building truly intelligent AI applications.

What Even IS Agentic Postgres? (The Question That Started Everything)

Before this hackathon, my mental model of PostgreSQL was pretty simple:

• It stores data (tables, rows, columns)
• It's fast and reliable
• Everyone uses it
• That's... pretty much it?

Then TigerData came along and said: "What if your database could work with AI agents instead of just serving them data?"

And suddenly I'm learning about:

• Zero-copy database forks (< 1 second! What kind of sorcery?!)
• Hybrid search combining vector embeddings + BM25 ranking
• MCP (Model Context Protocol) for giving AI agents PostgreSQL expertise
• Fluid Storage that makes all this magic possible
• Multi-agent collaboration using separate database forks

My brain: "Okay, this changes EVERYTHING." 🤯💡

The Aha Moment: When I Realized What This Enables

Here's what got me excited. Imagine you're building an AI coding assistant that needs to:

1. Analyze your codebase (needs database access)
2. Suggest optimizations (needs to test different approaches)
3. Verify changes won't break things (needs safe testing environment)
4. Learn from multiple developers simultaneously (needs isolation)

Traditional approach:

• Set up test database (minutes to hours)
• Hope tests don't interfere with each other
• Manually manage test data
• Pray nothing breaks production
• Cross fingers the AI doesn't suggest something catastrophic

With Agentic Postgres:

User: "Optimize this slow query"
System: Creates 4 database forks in 4 seconds �
Agent A: Tests adding indexes (on Fork 1)
Agent B: Rewrites query with CTEs (on Fork 2)
Agent C: Tries different join order (on Fork 3)
Agent D: Experiments with partitioning (on Fork 4)
System: Compares results, applies best solution to production
Total time: < 10 seconds

THAT'S INSANE. And it's exactly what TigerData enables! 🚀

The Research Phase: Going Deep on Every Feature

When I started researching for the hackathon, I did what any excited developer does: I went DEEP. Like, "stayed up until 3 AM reading documentation" deep. 😅

Let me break down what I learned about each key feature:

1. Zero-Copy Database Forks: The Game Changer 🔥

What I thought forks were:
"Probably like git branches but for databases... maybe it copies all the data? Must be slow."

What they actually are:
Copy-on-write (COW) magic at the block storage layer. You get a complete database copy in sub-second time without actually copying data!

Mind-blowing stats from my research:

• Fork creation: < 1 second (tested, confirmed, not marketing!)
• Cost: Only pay for changes, not the full copy
• Isolation: Completely separate environment
• Includes: Database + storage + embeddings + indexes + everything

Real-world use case I'm implementing:

// User asks: "What's the best way to structure this data?"

async function testMultipleSchemas(userQuery: string) {
  // Create 3 forks simultaneously
  const forks = await Promise.all([
    tigerCLI.createFork(), // Fork 1: Normalized schema
    tigerCLI.createFork(), // Fork 2: Denormalized schema
    tigerCLI.createFork(), // Fork 3: Hybrid approach
  ]);

  // Each agent tests their approach in parallel
  const results = await Promise.all([
    agent1.testNormalizedSchema(forks[0]),
    agent2.testDenormalizedSchema(forks[1]),
    agent3.testHybridSchema(forks[2])
  ]);

  // Compare performance, pick winner
  const bestApproach = selectBestPerformance(results);

  // Cleanup (automatic with TTL, but being explicit)
  await Promise.all(forks.map(f => tigerCLI.deleteFork(f.serviceId)));

  return bestApproach;
}

// Total time: 5-8 seconds for 3 complete database tests
// Traditional approach: 30+ minutes minimum

This isn't science fiction. This is what the Tiger CLI actually enables! 🔥

Resource that helped me understand this:

• Zero-Copy Forks Announcement - The blog post that made me go "wait, WHAT?!"
• Tiger CLI GitHub - Actual code examples

2. Hybrid Search: When Vector Search Met BM25 🎯

This is where things get really interesting. I've used vector search before (OpenAI embeddings + pgvector), and I've used full-text search (PostgreSQL's tsvector). But I never thought about combining them!

The problem with vector search alone:
Great for semantic similarity, terrible for exact keyword matches.

Example:

Query: "How to create index on timestamp column?"
Vector search: Returns general indexing docs (semantic match)
But misses: Specific "timestamp" syntax (keyword miss)

The problem with full-text search alone:
Great for keywords, terrible for understanding meaning.

Example:

Query: "How to speed up date queries?"
Full-text search: Looks for exact words "speed" and "date"
Misses: Articles about "optimizing temporal queries" (same meaning, different words)

The magic of hybrid search (vector + BM25):
Combines semantic understanding with keyword precision!

Here's the actual SQL I learned from TigerData's docs (and yes, this blew my mind):

-- The hybrid search query that changed how I think about search

WITH vector_results AS (
  SELECT
    id,
    content,
    embedding <=> query_embedding AS vector_distance,
    ROW_NUMBER() OVER (ORDER BY embedding <=> query_embedding) AS vector_rank
  FROM documents
  ORDER BY embedding <=> query_embedding
  LIMIT 20
),
bm25_results AS (
  SELECT
    id,
    content,
    ts_rank_bm25(content_tsv, plainto_tsquery('user query')) AS bm25_score,
    ROW_NUMBER() OVER (
      ORDER BY ts_rank_bm25(content_tsv, plainto_tsquery('user query')) DESC
    ) AS bm25_rank
  FROM documents
  WHERE content_tsv @@ plainto_tsquery('user query')
  ORDER BY bm25_score DESC
  LIMIT 20
),
combined AS (
  SELECT
    COALESCE(v.id, b.id) AS id,
    COALESCE(v.content, b.content) AS content,
    -- Reciprocal Rank Fusion (RRF) algorithm
    (COALESCE(1.0 / (60 + v.vector_rank), 0.0) * 0.6 +
     COALESCE(1.0 / (60 + b.bm25_rank), 0.0) * 0.4) AS hybrid_score
  FROM vector_results v
  FULL OUTER JOIN bm25_results b ON v.id = b.id
)
SELECT id, content, hybrid_score
FROM combined
ORDER BY hybrid_score DESC
LIMIT 10;

What makes this beautiful:

1. Reciprocal Rank Fusion - Clever algorithm that combines rankings
2. Weighted scores - You control vector vs keyword importance (0.6 vs 0.4 here)
3. FULL OUTER JOIN - Catches results from either method
4. Pure SQL - No external services, all in PostgreSQL!

My testing plan:
I'm building a "Conversational Database Explorer" where users ask questions in natural language. Hybrid search will power:

• Documentation lookup ("How do I create an index?")
• Query history search ("Show me that join query I wrote last week")
• Schema exploration ("Which tables contain customer data?")

Resources that taught me this:

• pg_textsearch Introduction - The announcement that made BM25 on Postgres a reality
• TigerData Docs on pg_textsearch - Technical details and examples

3. Tiger MCP: PostgreSQL Expertise as a Service 🧠

This one took me a while to wrap my head around. MCP (Model Context Protocol) is Anthropic's open standard for giving LLMs access to external tools and data. Tiger MCP is TigerData's implementation that gives AI assistants 10+ years of PostgreSQL expertise.

What this means practically:

Instead of me writing:

const prompt = `You are a PostgreSQL expert. Here's the schema...
Help the user write a query... Remember to use indexes...
Oh and don't forget about...`
// 500 lines of PostgreSQL best practices

I can just:

// Use Tiger MCP server (hosted or self-hosted)
const response = await mcpClient.searchPostgresDocs(userQuery);
// MCP returns relevant PostgreSQL docs, examples, and best practices

The three MCP tools I'm most excited about:

1. semantic_search_postgres_docs - Search official PostgreSQL docs (versions 14-18)

   // User: "How to create a GIN index on JSONB?"
   const docs = await mcp.searchPostgresDocs("GIN index JSONB");
   // Returns: Exact syntax, examples, performance notes

1. semantic_search_tiger_docs - Search TimescaleDB/TigerData docs

   // User: "How do hypertables work?"
   const docs = await mcp.searchTigerDocs("hypertables");
   // Returns: Concepts, setup, best practices

1. get_prompt_template - Get task-specific PostgreSQL templates

   // User needs help with query optimization
   const template = await mcp.getPromptTemplate("optimize_query");
   // Returns: Structured prompt with PostgreSQL optimization expertise

How I'm using it in my project:

// Multi-agent system with MCP integration

class QueryAgent {
  async generateSQL(userQuery: string, schema: Schema) {
    // 1. Get relevant PostgreSQL docs via MCP
    const docs = await mcpClient.searchPostgresDocs(userQuery);

    // 2. Get optimization template via MCP
    const template = await mcpClient.getPromptTemplate("write_optimized_query");

    // 3. Combine with GPT-4
    const prompt = `
      User Query: ${userQuery}
      Database Schema: ${JSON.stringify(schema)}

      PostgreSQL Documentation:
      ${docs}

      Optimization Guidelines:
      ${template}

      Generate optimized SQL query.
    `;

    const sql = await openai.chat.completions.create({
      model: "gpt-4-turbo-preview",
      messages: [{ role: "user", content: prompt }]
    });

    return sql;
  }
}

The setup (surprisingly easy):

# Option 1: Use hosted MCP server (easiest)
# Just configure in your IDE:
# .claude/mcp.json
{
  "mcpServers": {
    "tiger-docs": {
      "url": "https://mcp.tigerdata.com/docs"
    }
  }
}

# Option 2: Self-host (more control)
git clone https://github.com/timescale/tiger-docs-mcp-server.git
cd tiger-docs-mcp-server
npm install
npm run build
npm start

Resource that made MCP click for me:

• Tiger MCP Server GitHub - Source code and setup instructions
• TigerData MCP Docs - Integration guide

4. Tiger CLI: Your Database's Remote Control <�

The Tiger CLI is how you actually USE all these features. And it's surprisingly delightful to use!

Commands I use constantly:

# Authentication (one-time setup)
tiger auth login
# Opens browser, authenticate, done! ✅

# Create a free database (no credit card required!)
tiger service create --name my-db --free --with-password -o json

# Fork a database (THE MONEY SHOT)
tiger service fork prod-db-xyz --now
# Output: Fork created in 0.8 seconds! 🚀

# Get connection string
tiger db connection-string my-db-abc
# Returns: postgresql://user:pass@host:port/db

# Connect directly (uses psql)
tiger db connect my-db-abc
# You're in! Run queries, inspect schema, whatever you need

# Cleanup
tiger service delete fork-xyz
# Gone! (But production is safe)

My favorite workflow:

# Morning: Create test environment
tiger service fork prod-db --now > test-fork.json
TEST_DB=$(jq -r '.service_id' test-fork.json)

# Test migrations all day
tiger db connect $TEST_DB -f migration-v1.sql
tiger db connect $TEST_DB -f migration-v2.sql
# ... test, break things, no stress

# Evening: Cleanup
tiger service delete $TEST_DB
# Tomorrow: Fork again fresh!

Resource:

• Tiger CLI Docs - Comprehensive command reference

5. Multi-Agent Collaboration: The Architecture I'm Building 🤖

This is where everything comes together. The hackathon challenge asks for "Multi-agent collaboration using separate database forks," and I have a PLAN.

The Conversational Database Explorer Architecture:

User: "Analyze my database and suggest optimizations"
        |
        v
+-----------------------------+
|      Agent Coordinator      |
| (Orchestrates all agents    |
|  in parallel)               |
+-----------------------------+
        |          |          |
        v          v          v
   +---------+ +---------+ +---------+
   | Fork 1  | | Fork 2  | | Fork 3  |
   +---------+ +---------+ +---------+
        |          |          |
        v          v          v
   +---------+ +---------+ +---------+
   | Schema  | | Query   | | Insight |
   | Agent   | | Agent   | | Agent   |
   +---------+ +---------+ +---------+
        |          |          |
        | Analyzes | Generates| Discovers
        | structure| SQL      | patterns
        |          |          |
        +----------+----------+
                   |
                   v
         Synthesized Response
   "Found 3 slow queries, 2 missing indexes,
    and 1 optimization opportunity..."

The code structure:

// Agent Coordinator (the brain)
class AgentCoordinator {
  private schemaAgent: SchemaAgent;
  private queryAgent: QueryAgent;
  private insightAgent: InsightAgent;
  private memoryAgent: MemoryAgent;

  async processUserQuery(query: string, sessionId: string) {
    // 1. Create forks for each agent (parallel!)
    const forks = await Promise.all([
      tigerCLI.createFork(), // Schema agent's sandbox
      tigerCLI.createFork(), // Query agent's sandbox
      tigerCLI.createFork(), // Insight agent's sandbox
    ]);

    // 2. Run agents in parallel (they can't interfere!)
    const [schemaAnalysis, generatedSQL, insights, context] =
      await Promise.all([
        this.schemaAgent.analyze(query, forks[0]),
        this.queryAgent.generateSQL(query, forks[1]),
        this.insightAgent.discover(query, forks[2]),
        this.memoryAgent.getContext(sessionId), // Uses main DB
      ]);

    // 3. Synthesize results
    const response = this.synthesize({
      schema: schemaAnalysis,
      sql: generatedSQL,
      insights: insights,
      context: context,
    });

    // 4. Cleanup forks
    await Promise.all(forks.map(f => tigerCLI.deleteFork(f.serviceId)));

    return response;
  }
}

// Schema Agent (understands database structure)
class SchemaAgent {
  async analyze(query: string, forkId: string) {
    const conn = await connectToFork(forkId);

    // Introspect schema safely on fork
    const tables = await conn.query(`
      SELECT table_name, column_name, data_type
      FROM information_schema.columns
      WHERE table_schema = 'public'
    `);

    // Use MCP for PostgreSQL best practices
    const recommendations = await mcpClient.searchPostgresDocs(
      "schema design best practices"
    );

    return {
      tables,
      recommendations,
      relevantTables: this.identifyRelevantTables(query, tables),
    };
  }
}

// Query Agent (generates SQL)
class QueryAgent {
  async generateSQL(query: string, forkId: string) {
    const conn = await connectToFork(forkId);

    // Get schema context
    const schema = await this.getSchemaContext(conn);

    // Use MCP + GPT-4 for SQL generation
    const mcpDocs = await mcpClient.searchPostgresDocs(query);
    const sql = await this.generateWithGPT4(query, schema, mcpDocs);

    // Test on fork (safe!)
    const isValid = await this.validateSQL(conn, sql);

    return { sql, isValid, executionPlan: await this.getExplain(conn, sql) };
  }
}

// Insight Agent (discovers patterns)
class InsightAgent {
  async discover(query: string, forkId: string) {
    const conn = await connectToFork(forkId);

    // Analyze query patterns
    const slowQueries = await conn.query(`
      SELECT query, mean_exec_time, calls
      FROM pg_stat_statements
      ORDER BY mean_exec_time DESC
      LIMIT 10
    `);

    // Check for missing indexes
    const missingIndexes = await this.analyzeMissingIndexes(conn);

    return {
      slowQueries,
      missingIndexes,
      optimizationOpportunities: this.findOptimizations(slowQueries),
    };
  }
}

// Memory Agent (conversation context)
class MemoryAgent {
  async getContext(sessionId: string) {
    // Uses main database (not fork)
    const history = await db.query(`
      SELECT user_message, assistant_message, created_at
      FROM conversations
      WHERE session_id = $1
      ORDER BY created_at DESC
      LIMIT 10
    `, [sessionId]);

    return this.buildContext(history);
  }

  async store(sessionId: string, userMsg: string, assistantMsg: string) {
    // Store in TimescaleDB hypertable for time-series optimization
    await db.query(`
      INSERT INTO conversations (session_id, user_message, assistant_message)
      VALUES ($1, $2, $3)
    `, [sessionId, userMsg, assistantMsg]);
  }
}

Why this architecture is powerful:

1. Parallel Execution - All agents run simultaneously (3-5 seconds total)
2. Isolated Testing - Each agent has its own fork (no interference)
3. Safe Exploration - Nothing touches production database
4. Automatic Cleanup - Forks deleted after use (or TTL expires)
5. Context Aware - Memory agent maintains conversation history
6. PostgreSQL Expertise - MCP provides deep database knowledge

Performance estimates:

• Fork creation (3x): ~3 seconds
• Agent execution (parallel): ~2-4 seconds
• Response synthesis: ~1 second
• Total: 6-8 seconds for comprehensive database analysis 🚀

Compare to traditional approach:

• Set up test databases: 30+ minutes
• Run tests sequentially: 10+ minutes
• Manual analysis: 20+ minutes
• Total: 60+ minutes 😱

That's a 10x speed improvement! And the agents can't possibly break production because they're working on isolated forks.

The Implementation Plan: 4 Phases to Hackathon Glory 🎯

After all this research, I created a detailed implementation plan. Here's the roadmap:

Phase 1: Foundation (Days 1-3)

Goal: Get basic infrastructure working

// Week 1 checklist:
const phase1Tasks = [
  " Set up TigerData account (free tier)",
  " Install Tiger CLI and authenticate",
  " Create base database with extensions",
  "� Implement Tiger CLI service wrapper",
  "� Build fork manager with lifecycle tracking",
  "� Create database models (sessions, forks, conversations)",
  "� Test fork creation/deletion",
];

Key milestone: Successfully create and delete forks programmatically.

Phase 2: AI Integration (Days 4-6)

Goal: Get agents talking to the database

const phase2Tasks = [
  "🧠 Set up pgvector extension",
  "🧠 Install pg_textsearch for BM25",
  "🧠 Implement embedding generation (OpenAI)",
  "🧠 Build hybrid search service",
  "🧠 Create basic query agent (GPT-4)",
  "🧠 Test natural language to SQL",
];

Key milestone: "Show me all tables" � Returns actual table list.

Phase 3: Multi-Agent System (Days 7-9)

Goal: Agents working in parallel

const phase3Tasks = [
  "🤖 Implement agent coordinator",
  "🤖 Build schema agent",
  "🤖 Build query agent",
  "🤖 Build insight agent",
  "🤖 Build memory agent",
  "🤖 Test parallel execution on forks",
];

Key milestone: All 4 agents analyzing database simultaneously.

Phase 4: Polish & Deploy (Days 10-14)

Goal: Production-ready demo

const phase4Tasks = [
  "🎨 Build Next.js chat interface",
  "🎨 Create schema browser component",
  "🎨 Implement WebSocket for real-time chat",
  "🎨 Deploy backend to Render/Railway",
  "🎨 Deploy frontend to Vercel",
  "🎨 Record demo video",
  "🎨 Write submission post",
];

Key milestone: Live demo anyone can try!

What Makes This Hackathon Project Special 💎

There are a few things I'm doing that I haven't seen elsewhere:

1. Parallel Universe Testing

Instead of testing ONE solution, test MULTIPLE simultaneously:

User: "How should I optimize this query?"

Traditional: Try approach 1 � Measure � Try approach 2 � Measure � Compare
Time: 10+ minutes

Agentic Postgres: Fork 4x � Test all approaches in parallel � Compare
Time: < 10 seconds

2. Conversational Branches

Each "what-if" scenario creates a new fork:

User: "What if I denormalize this table?"
System: *Creates fork*
Agent: *Tests denormalized schema*
Agent: "Here's the performance impact..."

User: "Actually, what if I partition it instead?"
System: *Creates another fork*
Agent: *Tests partitioning*
Agent: "Partitioning gives better results!"

You can explore multiple possibilities without commitment!

3. Time-Travel Debugging

Fork from specific timestamps to analyze issues:

// Production issue at 2:30 PM
// Fork from 2:25 PM (before the issue)
const beforeFork = await tigerCLI.createFork(prodDB, {
  recoveryTime: "2025-01-10 14:25:00"
});

// Fork from 2:30 PM (during the issue)
const duringFork = await tigerCLI.createFork(prodDB, {
  recoveryTime: "2025-01-10 14:30:00"
});

// Compare database state
const changes = await compareDBs(beforeFork, duringFork);
console.log("What changed:", changes);

// Test fix on the "during" fork
await testFix(duringFork, proposedFix);

4. Self-Healing Queries

AI detects errors, generates fixes, tests on forks, applies best solution:

async function selfHealQuery(brokenSQL: string) {
  // Detect issue
  const error = await validateSQL(brokenSQL);

  // Generate 3 potential fixes
  const fixes = await generateFixes(brokenSQL, error);

  // Create fork for each fix
  const forks = await Promise.all(fixes.map(() => tigerCLI.createFork()));

  // Test all fixes in parallel
  const results = await Promise.all(
    fixes.map((fix, i) => testSQL(forks[i], fix))
  );

  // Apply best fix to production
  const bestFix = selectBest(results);
  return bestFix;
}

The Tech Stack (Because Details Matter) 🛠️

Here's what I'm using and why:

Frontend

const frontend = {
  framework: "Next.js 14",              // App router, RSC, fast
  ui: "React 18 + Tailwind CSS",        // Beautiful, responsive
  components: "shadcn/ui + Radix UI",   // Accessible, polished
  state: "Zustand",                     // Simple, no boilerplate
  realtime: "WebSocket (Socket.io)",    // Chat interface
  charts: "Recharts",                   // Query visualization
};

Backend

const backend = {
  framework: "FastAPI (Python 3.11+)", // Async, fast, typed
  database: "TigerData Agentic Postgres", // The star! ⭐
  orm: "SQLAlchemy 2.0",                 // Async support
  ai: "OpenAI GPT-4 + text-embedding-3-small",
  websocket: "python-socketio",          // Real-time chat
  testing: "pytest + httpx",             // Comprehensive tests
};

Database Extensions

-- The power trio
CREATE EXTENSION IF NOT EXISTS timescaledb;   -- Time-series optimization
CREATE EXTENSION IF NOT EXISTS vector;        -- Semantic search (pgvector)
CREATE EXTENSION IF NOT EXISTS pg_textsearch; -- BM25 ranking
CREATE EXTENSION IF NOT EXISTS pg_stat_statements; -- Query monitoring

Infrastructure

const infrastructure = {
  frontend: "Vercel",           // Instant deploys, edge functions
  backend: "Render / Railway",  // Easy Docker deploys
  database: "TigerData Cloud",  // Already deployed!
  monitoring: "Sentry + Axiom", // Errors + logs
  cicd: "GitHub Actions",       // Auto-deploy on push
};

The Learning Curve: What Surprised Me 📚

Easy Things (That I Expected to Be Hard)

1. Tiger CLI Installation

   curl -fsSL https://cli.tigerdata.com | sh
   # Done! 30 seconds.

Expected: Complex setup. Reality: One command. 🔥

1. Creating Forks

   tiger service fork my-db --now
   # Returns in < 1 second

Expected: Minutes of waiting. Reality: Faster than git checkout! ⚡

1. Hybrid Search Implementation Expected: Custom code, complex algorithms. Reality: One SQL query (well, a complex one, but still!). The pg_textsearch extension does all the heavy lifting.

Hard Things (That I Underestimated)

1. Understanding Copy-on-Write
   The concept is simple. The implications are profound. It took me a while to really grasp how this changes everything about database testing.

2. Reciprocal Rank Fusion
   The hybrid search algorithm is elegant but not obvious. I had to read the formula like 10 times before it clicked:
   

   RRF_score = � (1 / (k + rank_i))
   where k = 60 (constant), rank_i = rank in list i

1. Multi-Agent Coordination Making sure agents don't step on each other's toes, even with separate forks, requires careful orchestration. The coordinator pattern helps but needs thought.

Resources That Saved Me (Links You'll Want to Bookmark) 🔖

Here's every resource that helped me understand Agentic Postgres:

Official Documentation

• TigerData Main Docs - Start here!
• Getting Started Guide - First steps with TigerData
• AI Integration Docs - AI-specific features
• REST API Reference - Complete API docs

Key Blog Posts

• Postgres for Agents - The vision explained
• Zero-Copy Forks Announcement - Mind-blowing tech details
• Fluid Storage Deep Dive - How the magic works
• pg_textsearch Introduction - BM25 for Postgres
• Agentic Postgres Free Plan - No credit card needed!

GitHub Repositories

• Tiger CLI - Command-line tool source
• Tiger Docs MCP Server - MCP server implementation
• TimescaleDB - The foundation

Example Projects

• AgenticForkSquad - Multi-agent query optimizer
• Self-Healing Framework - Automatic issue resolution
• Agent Auth - Security with agents

Community

• DEV Community Tag - Latest posts and discussions
• Discord - Active community support
• Product Page - Features overview

The Meta Moment: Research Planning WITH AI 🤖

Here's the fun part: I used AI to help me research Agentic Postgres. Not just casual ChatGPT queries, but a systematic research session with Claude Code that:

1. Searched the web for TigerData blog posts, docs, and examples
2. Fetched Context7 docs for library integrations
3. Analyzed architecture patterns from existing hackathon projects
4. Generated implementation plans with code examples
5. Created comprehensive documentation (9 markdown files!)

The irony? I'm researching how to build AI agents that use databases... by using an AI agent! 😄

What I learned from this meta experience:

AI is REALLY good at:

• Gathering scattered information from multiple sources
• Synthesizing concepts from docs into actionable plans
• Generating code examples based on API documentation
• Creating structured documentation with consistent formatting

AI still needs humans for:

• L Understanding "why" (business value, user needs)
• L Making architectural decisions (trade-offs, priorities)
• L Creative problem-solving (novel use cases)
• L Debugging edge cases in production

This is exactly why Agentic Postgres is exciting! It gives AI agents:

• Safe sandboxes (forks) to explore ideas
• Fast iteration (< 1 second to try something new)
• Real data (not synthetic test data)
• Isolated environments (can't break production)

What's Next: Building the Vision 🚀

Now that research is done, it's time to BUILD! The Conversational Database Explorer brings together all the TigerData Agentic Postgres features into a production-ready application that anyone can use to interact with their databases through natural language.

The Big Picture: Why This Matters 🌟

Here's why I'm genuinely excited about Agentic Postgres (beyond winning a hackathon):

1. It Changes How We Test

No more "hope this doesn't break production." Just fork, test, verify, apply. Safe experimentation becomes the default.

2. It Enables True AI Collaboration

AI agents can work in parallel without stepping on each other's toes. Each gets a complete, isolated database sandbox.

3. It Makes AI More Trustworthy

When AI can test its own suggestions on real data (via forks) before recommending them, it becomes more reliable.

4. It Democratizes Complex Operations

Query optimization, schema design, performance tuning these used to require expert DBAs. Now AI agents can help, safely.

5. It's Fast Enough to Be Interactive

Sub-second forks mean you can have a conversation with your database:

User: "What if I add an index here?"
System: *Creates fork, tests index*
System: "16% faster, uses 2MB disk. Want to apply it?"
User: "Yes!"
System: *Applies to production*

That's not batch processing. That's a dialogue! 💬

Final Thoughts: The Journey vs The Destination 🎯

Right now, as I write this, the Conversational Database Explorer is still mostly in my head and in documentation. The code is mostly placeholders. The demo doesn't exist yet.

But you know what? The research phase has been incredible!

I learned about:

• Copy-on-write storage systems
• Reciprocal rank fusion algorithms
• Multi-agent orchestration patterns
• PostgreSQL internals (way more than I expected!)
• Production-grade authentication (from my previous Auth0 integration)

And I discovered something that might change how I build every database-backed application going forward: the ability to test safely and quickly on real data.

That's not just a feature. That's a paradigm shift. 🔥

For Fellow Builders

If you're reading this and thinking "Should I try TigerData?", here's my honest take:

Try it if you:

• Build AI applications that need database access
• Want safe testing environments (forks are a game-changer)
• Need hybrid search (semantic + keyword)
• Like clean CLIs and good documentation
• Want to learn PostgreSQL deeply (MCP is a great teacher!)

Maybe skip it if:

• L You just need a basic CRUD app (regular PostgreSQL is fine)
• L You don't work with AI/ML (you won't use the cool features)
• L You're not comfortable with SQL (there's a learning curve)

For me? This is exactly what I needed for building trustworthy AI agents. The fact that there's a hackathon is just the excuse I needed to finally build it! =

What I Hope to Prove

By the end of this hackathon, I want to demonstrate that:

1. AI agents can be trusted when they have safe sandboxes (forks)
2. Database optimization can be conversational (not just for DBAs)
3. Multi-agent collaboration works when agents have isolation
4. Hybrid search is superior to vector or keyword alone
5. Development velocity increases when testing is instant

Ambitious? Yes. Possible? Absolutely. Worth the late nights? We'll see! =

The Commitment: Shipping This Thing =�

Here's my public commitment:

I, Anshu Mandal, will build the Conversational Database Explorer using TigerData Agentic Postgres. I will implement all 5 key features (MCP, CLI, pg_textsearch, forks, Fluid Storage) in a production-ready demo. I will document the journey, share the code, and submit before the deadline.

Why public commitment?

Because when I told the dev.to community about StudyMate, and then people actually used it, that accountability pushed me to make it better. Let's do it again! 💪

Progress updates:

• 📅 Week 1: Backend foundation
• 📅 Week 2: AI integration
• 📅 Week 3: Multi-agent system
• 📅 Week 4: Polish & ship

I'll share updates, code snippets, and learnings along the way. Follow along if you're interested!

Resources One More Time (Because You'll Need These) 📚

Organized by what you want to do:

Getting Started:

• TigerData Free Tier Signup - No credit card!
• Getting Started Guide
• Tiger CLI Installation

Learning the Tech:

• Agentic Postgres Overview
• Zero-Copy Forks Explained
• Hybrid Search Tutorial

Building AI Agents:

• AI Integration Docs
• MCP Server Guide
• Tiger Docs MCP GitHub

Inspiration:

• Hackathon Examples - See what others built
• AgenticForkSquad
• Self-Healing Framework

Community:

• Discord - Get help, share ideas
• GitHub Organization - Explore the source

Let's Build This! 🚀

That's my research journey with TigerData Agentic Postgres. From "what even is this?" to "I need to build with this NOW!"

The best part? This is just the beginning. Once the hackathon project is done, I can use this pattern for:

• Code review agents (test suggestions on forks)
• Migration testing agents (verify before production)
• Performance optimization agents (compare approaches)
• Data exploration agents (safe discovery)
• Schema evolution agents (test design changes)

The possibilities are endless when you have:

• Safe sandboxes (zero-copy forks)
• Fast iteration (< 1 second creation)
• PostgreSQL expertise (MCP)
• Hybrid intelligence (vector + BM25 search)
• Multi-agent collaboration (parallel execution)

If you're building with TigerData too, let's connect! Drop a comment, share your project, or hit me up on:

• GitHub: @prime399
• DEV.to: Right here! 👋

Let's make databases intelligent together! 🤖🚀

---

P.S. - If you read this whole thing, you're either:

1. Really interested in Agentic Postgres (awesome! =L)
2. Procrastinating on something (I feel you =)
3. My mom (hi Mom! Thanks for pretending to understand! d)

Either way, thanks for reading! Now go build something cool! =�

Acknowledgments:

• TigerData team for building something genuinely innovative
• The hackathon organizers for the motivation
• The dev.to community for being awesome
• All the developers who wrote the example projects I learned from
• Future me, for the late nights ahead 😴

Let's ship this thing! 🚢

Try It Out! Live Demo & Open Source 🌐

UPDATE: The Conversational Database Explorer is now live!

Live Demo

🔗 Website: convodb.tech

What you can do:

1. Chat with your database in natural language
2. Watch 4 AI agents collaborate in real-time
3. Explore your schema interactively
4. Get query optimization suggestions
5. Test changes on instant database forks (< 1 second!)

Try it now:

Visit: https://convodb.tech
Sign up (GitHub/Google OAuth)
Connect your PostgreSQL database
Ask: "Show me all my tables"
Ask: "Find slow queries and optimize them"
Ask: "What if I add an index to this column?"

Open Source Code

💻 GitHub: github.com/prime399/convodb

Repository highlights:

• Complete FastAPI backend with Tiger CLI integration
• Next.js frontend with real-time WebSocket chat
• Multi-agent system (Schema, Query, Insight, Memory agents)
• Hybrid search implementation (pgvector + pg_textsearch)
• Fork manager with automatic lifecycle management
• Comprehensive documentation (9 detailed guides!)
• Production-ready deployment configs

Quick clone and run:

git clone https://github.com/prime399/convodb.git
cd convodb
# Follow setup instructions in README.md

What's included:

• ✅ Tiger CLI Service Wrapper
• ✅ Fork Manager with TTL
• ✅ Hybrid Search Service
• ✅ Multi-Agent Architecture
• ✅ WebSocket Chat Interface
• ✅ Next.js + FastAPI Boilerplate
• ✅ Complete Documentation

Star the repo if it helps you! ⭐

Connect & Collaborate

Building with TigerData Agentic Postgres? Let's connect!

• 💻 GitHub: @prime399
• 🌐 Project: convodb.tech
• 📝 DEV.to: Right here in the comments!
• 💬 Discord: TimescaleDB Community

Want to contribute?

• Open issues for bugs or feature requests
• Submit PRs for improvements
• Share your own TigerData projects
• Help improve the documentation

Let's build intelligent databases together! 🐯🚀