Meet Xata Agent: An Open Source Agent for Proactive PostgreSQL Monitoring, Automated Troubleshooting, and Seamless DevOps Integration

XATA Agent is an open source AI assistant designed to serve as a reliable engineer for postgresql databases. Record records and performance standards are constantly monitored, capturing signals such as slow Information, CPU spaces and memory nails, and the number of abnormal communication, to detect the emerging problems before they escalate to power outages. Depending on a group of diagnostic play books, safe SQL and reading procedures only, the agent makes concrete recommendations and can even automate routine tasks, such as emptiness and indexing. By packaging years of operational experience and associating with the capabilities of the big language model (LLM), the XATA agent reduces the burden on databases officials and enables development teams to maintain high performance and availability without asking for a deep specialization yet.

Under the cap, XATA agent is performed as the Next.js application using Vercel AI SDK and is mainly written in Typescript. The warehouse is organized as MonorePo, with evidence for the front end of the database (Apps/Dbagent ‘), joint libraries (‘ Al -Hazm ‘), training files, and Docker assets. This planning simplifies the contribution process: after installing the knot via the .NVMRC file, a developer runs the “PNPM” to withdraw the dependencies, prepare the local postgresql using Docker Compose, and determines the LLM accreditation data in. Both the user interface and the logic of diagnostic for the agent.

The publication of XATA agent in production follows similar and direct steps. The team publishes Docker photos of the agent service and the postgresql database, and offers an example “Docker-Corm.yml”. The operators form a small set of environmental variables, such as the General URL and API keys for the chosen LLM provider, in the “.env.prduction” file. Then, one order creates the entire stack:

After a short starting starting stage, the agent’s interface appears on the specified address, directing users through the database on the plane, forming accreditation data, and initial health checks. This self -host model achieves a balance between self -rule and control, allowing the teams to check all components, and merge the agent with internal monitoring pipelines, and still benefits from the improvements that depend on society.

Below is an illustrative excerpt of “Docker-CORM.YML” for self-hosting:

version: '3.8'
services:
  xata-agent:
    image: xataio/agent:latest
    environment:
      PUBLIC_URL: http://localhost:8080
      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, it is similar to workflow:

# 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

At.

OPENAI_API_KEY=sk-your-openai-key
ANTHROPIC_API_KEY=ak-your-anthropic-key
PUBLIC_URL=http://localhost:3000

The principle of the basic design of the XATA factor is expansion. The hallucinative agent avoids adhering to a fixed group of playing books written in humans and unproductive tools. PlayBooks is a regular English file that determines step-by-step guidelines, while the tools are Typescript functions that envelop database or CLOUD-PROVIDER Applications. Integration operations – such as Slack and AWS RDS – turn in the system by training and user interface elements, allowing adding new data sources and notification channels with minimal effort.

The main functions of the XATA factor include:

• Controlly monitoring: Watching records and standards constantly, including the use of the CPU, memory pressure, and query specifications, for an abnormal mark early.
• Configure control: suggested modifications to postgres settings such as “Shared_Buffers” and “Work_MEM” based on the properties of the work burden.
• Explining and repairing errors in performance: investigating slow information, identifying lost indexes, and recommending indexing strategies.
• Safe Diagnosis: SQL implementation only against system display methods (‘pg_stat_statements’, ‘PG_Locks’) to collect context without risking data integration.
• Cloud integration: withdrawing records and standards directly from managed services such as RDS and Aurora via Cloudwatch.
• Alerts and notifications: Send in actual time to the stagnation channels when crossing the critical thresholds.
• LLM Flexibility: Supporting multiple inference engines, including Openai, anthropic, and Deepseek, so that institutions can improve security and cost.
• PlayBook customization: Determining and repairing errors in the normal English to capture best backup practices.
• MCP Server’s ability: It acts as a model of the context of the context of the model, allowing other agents to call its tools over the network.
• The progress of approval and evaluation test: plans to introduce governance controls for sensitive operations and automated verification of the agent’s recommendations.

Developers can compose new tools by exporting simple Typescript functions. For example, a tool may seem to bring the slower five heroes:

// 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 agent can contact him:

// 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);
  },
});

Playbooks lists link the tools together in a coherent diagnostic flow. Below is a YAML playing book to investigate the 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, one can take advantage of the integrated stagnation 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 standard structure, where the tools, playing and complementarity book are associated with a loose manner, ensuring that the agent’s extension to support the workflow or new platforms requires the minimum boiler. For example, the Google Cloud SQL support only includes implementing a new integration that brings measures via Google monitoring interface facades and transfers it to the user interface as a configuration step.

The XATA agent’s road map reflects its commitment to developing the possibility of its observation of institutions. Short -term plans include allocated playing books, which enable the teams to encrypt the field recovery procedures, and support the context of the context of the context of the model (MCP), allowing other agents to connect XATA tools over the network. Medium -term improvements include assessment and testing, harnessing to measure the accuracy of the agent’s advice against historical incidents and the progress of approval of operations that are likely to be sensitive. The cloud version is also developed, as it is integrated with one click with common monitoring chimneys and simplifying it on the difference without hosting infrastructure.

The carefully designed claim pays the design layer design that connects the language models to this book and tools device. As explained in a recent comment on AI-Agent design, the agent is directed to “providing clear, bright and accurate responses to the questions. Use the tools provided to obtain a context of the postgresql database to answer questions. Getpostgreestens tools.

By writing down best practices in repetitive play books, XATA agent unifies the response to accidents and reduces the novice engineer barrier to explore complex database problems. The teams that benefit from the agent gain a single source of the truth of the operational procedures, which reduces human error and enabling rotation when communicating, as less experienced employees can deal with alerts with confidence. Whether it is self -hosted or submitted as a managed service, the XATA agent calls on community contributions, a review of the peer and cooperative governance, ensuring that the collective experience of the open source community continuously enhances the agent’s capabilities.

In conclusion, the XATA agent represents a great progress in the ability to note the database and explore errors and their independent repair. Its mixture of expandable Monorepo Typexcript, Human Written Play Books, Safe SQL tools, and its flexible LLM placed as a practical solution for modern Devops teams. While institutions are increasingly seeking to automate complex infrastructure tasks, the XATA agent highlights by increasing human experience instead of trying to replace them, and providing clear and executable visions and automation that helps to maintain the performance and reliability of posgresql.

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