Meet the XATA Agent: Active PostgreSQL monitoring, automatic troubleshooting and seamless DEVOPS integration open source agent

XATA Agent is an open source AI assistant designed to serve as a site reliability engineer for PostgreSQL databases. It constantly monitors logs and performance metrics, capturing signals such as slow queries, CPU and memory spikes, and exceptional connection counts to detect problems that occur before upgrading to interrupts. The agent will utilize well-planned diagnostic scripts and secure, read-only SQL routines to provide specific suggestions and even automate daily tasks such as vacuums and indexes. By encapsulating years of operational expertise and pairing it with modern large language model (LLM) features, XATA agents reduce the burden on database administrators and make it affordable for development teams to maintain high performance and availability without the need for in-depth Postgerges specialization.

Under the hood, the XATA proxy is implemented as a next.js application using the Vercel AI SDK, written mainly in typescript. The repository is a MonorePo organization that contains a dedicated directory for database proxy Frontend (‘apps/dbagent’), shared libraries (‘katchages’), configuration files, and docker assets. This layout simplifies the contribution process: After installing the node through the accompanying “.nvmrc” file, the developer runs ‘pnpm install’ to extract dependencies to set up a local postgresql instance composed of Docker to define the ‘.env.local’Files’Files’.env.local” file, apply database migration and development server. This turnkey developer experience enables direct iteration in both the user interface and the proxy diagnostic logic.

Deploying XATA agents in production follows similar direct steps. The team published Docker images of the proxy service and its companion PostgreSQL database and provided an example of “Docker-Compose.yml”. The operator configures a few sets of environment variables in the “.env.production” file, such as the public URL and API keys of its selected LLM provider. Then, a command starts the entire stack:

After a brief startup phase, the proxy’s web interface appears at the specified address, guiding the user through database introduction, credential configuration and initial health checks. This self-committed model strikes a balance between autonomy and control, allowing teams to audit each component, integrate agents with internal monitoring pipelines, and still benefit from community-driven enhancements.

Here is an explanatory snippet of the “docker-compose.yml” configuration for self-hosting:

version: '3.8'
services:
  xata-agent:
    image: xataio/agent:latest
    environment:
      PUBLIC_URL: 
      OPENAI_API_KEY: your_openai_api_key_here
# Optional additional providers:
#      ANTHROPIC_API_KEY: your_anthropic_api_key_here
#      DEEPSEEK_API_KEY: your_deepseek_api_key_here
    ports:
      - "8080:8080"
  postgres:
    image: postgres:14
    environment:
      POSTGRES_USER: agent_user
      POSTGRES_PASSWORD: secure_password
      POSTGRES_DB: agent_db
    volumes:
      - db_data:/var/lib/postgresql/data

volumes:
  db_data:

For local development, the workflow looks like:

# Switch Node version
cd apps/dbagent
nvm use

# Install dependencies
pnpm install

# Copy example environment
cp .env.local.example .env.local

# Start development server
pnpm dev

In “.env.local”, the developer provides credentials for their LLM and defines where the front-end should connect:

OPENAI_API_KEY=sk-your-openai-key
ANTHROPIC_API_KEY=ak-your-anthropic-key
PUBLIC_URL=

The core design principles of XATA agents are scalable. Agents adhere to fixed manual scripts and non-destructive tools to avoid hallucinations. Scripts are ordinary English files that can specify instructions step by step, while tools encapsulate typescript functions that encapsulate database queries or cloud-provided API calls. Integrations (such as Slack and AWS RD) plug it into the system through configuration and UI widgets, allowing new data sources and notification channels to be added with minimal effort.

Key features of XATA proxy include:

• Active monitoring: Continuously observe logs and metrics, including CPU usage, memory pressure and query delays to mark exceptions early.
• Configuration tuning: Based on workload characteristics, it is recommended to adjust Postgres settings, such as “Share_Buffers” and “Work_mem”.
• Performance Troubleshooting: Investigate slow queries, identify missing indexes and recommend indexing strategies.
• Security Diagnosis: Perform read only SQL on system views (‘pg_stat_statements’, ‘pg_locks’) to collect context without risking data integrity.
• Cloud Integration: Pull logs and metrics directly from hosting services such as RDS and Aurora through CloudWatch.
• Alerts and Notifications: Send real-time alerts to the Slack channel when critical thresholds are crossed.
• LLM Flexibility: Supports multiple inference engines, including OpenAI, Humans, and DeepSeek, so organizations can optimize security and cost.
• Script Customization: Define new troubleshooting streams to capture proprietary best practices in simple English.
• MCP server function: acts as a model context protocol server, allowing other agents to call their tools over the network.
• Approval workflow and evaluation tests: Plans to introduce governance controls for sensitive operations and automatic verification of agent recommendations.

Developers can create new tools by exporting simple typescript features. For example, a tool that fetches the five slowest queries might look like:

// packages/db-tools/src/tools/checkSlowQueries.ts
import { Pool } from 'pg';
import { ToolResult } from 'xata-agent';

export async function checkSlowQueries(pool: Pool): Promise {
  const result = await pool.query('
    SELECT query, total_time, calls
    FROM pg_stat_statements
    ORDER BY total_time DESC
    LIMIT 5;
  ');
  return { rows: result.rows };
}

Then register it so that the proxy can call:

// apps/dbagent/src/server/tools.ts
import { defineTool } from 'xata-agent';
import { checkSlowQueries } from 'db-tools';

defineTool('checkSlowQueries', {
  description: 'Retrieve the top five slowest queries from pg_stat_statements',
  execute: async ({ dbPool }) => {
    return await checkSlowQueries(dbPool);
  },
});

The script incorporates tools into a coherent diagnostic process. Here are excerpts from the YAML-style script used to investigate slow queries:

# configs/playbooks/investigate_slow_queries.playbook.yaml
name: Investigate Slow Queries
description: Steps to identify and resolve performance bottlenecks caused by slow queries.
steps:
  - tool: getTablesAndInstanceInfo
    description: "Gather table sizes and database instance details."
  - tool: checkSlowQueries
    description: "List the top slow queries to pinpoint hotspots."
  - tool: suggestIndexes
    description: "Generate index recommendations for queries exceeding thresholds."
  - tool: evaluateVacuumStats
    description: "Check vacuum statistics to determine if table bloat is impacting performance."
  - tool: notifySlack
    description: "Alert the team in Slack if queries exceed critical latency."

To integrate with Slack, you can take advantage of the built-in slack adapter:

// packages/integrations/src/slackAdapter.ts
import { SlackAdapter } from 'xata-agent/integrations';

const slack = new SlackAdapter({ webhookUrl: process.env.SLACK_WEBHOOK_URL });

export async function notifySlack({ message }: { message: string }) {
  await slack.send({
    channel: process.env.SLACK_CHANNEL,
    text: '🚨 Xata Agent Alert: ${message}',
  });
}

This modular architecture (tools, scripts, and integrations) is loosely coupled, ensuring that the agent is extended to support new workflows or platforms that require minimal boilerplate. For example, adding Google Cloud SQL support only involves implementing a new integration that takes metrics through Google’s monitoring API and wires it into the UI as a configuration step.

XATA Agent’s roadmap reflects its commitment to growing corporate observability. Short-term plans include custom scripts that enable teams to encode domain-specific recovery programs as well as Model Context Protocol (MCP) support, allowing other agents to call XATA’s tools over the network. Mid-term enhancements include evaluating and testing the wiring harness to benchmark the accuracy of approval workflows for historical events and potentially sensitive operations. Hosted cloud versions are also under development, promising one-click integration through the popular monitoring stack and simplifying boarding for teams without self-hosting infrastructure.

A well-designed system prompts an orchestration layer that connects language models to these scripts and tools. As highlighted in recent comments on AI-Ane Andess design, the agent is instructed to “provide clear, concise, clear, accurate answers to questions. Use the provided tool to get context from the PostgreSQL database to answer questions. When asking for queries, visit the summaryQuery tool, dial the table, and use the table in the initial evaluation. GetPostgresextensions tool.

By coding best practices into repeatable playbooks, XATA agents standardize incident responses and reduce the barriers for junior engineers to troubleshoot complex database issues. The team leverages the agency to provide a real source for the operational process, reducing human errors and enabling single-line rotations for inexperienced employees to ensure that alarms are handled. Whether self-hosting or as a hosting service, XATA Agent invites community contributions, peer reviews, and collaborative governance to ensure that the collective expertise of the open source community continues to enhance the agency’s capabilities.

In summary, XATA proxy represents a significant advance in database observability and automatic troubleshooting. It combines scalable typescript Monorepo, human-written scripts, secure SQL tools and flexible LLM integration to make it a practical solution for modern DevOps teams. As organizations increasingly seek to automate complex infrastructure tasks, XATA agents stand out by enhancing human expertise rather than trying to replace it, providing clear, actionable insights and automation that helps maintain post-QL performance and reliability at scale.

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