Introducing Autonomous AI Agent

Memory Engine — Powered by ThinkingMemory

Autonomous AI Agent's memory is powered by ThinkingMemory, our layered memory architecture that gives the agent persistent context across sessions. It learns and remembers your schemas, query patterns, team preferences, and operational history. No repeated explanations, no cold starts. The agent gets smarter over time.

Agent Brain — Agentic Mode

The brain is the reasoning core. It plans multi-step workflows, selects the right tools for each step, and executes them autonomously. Describe what you need in natural language, the agent figures out how.

It draws on ThinkingMemory to recall your past pipeline designs, database schemas, and team conventions, so it doesn't just solve problems, it solves them your way.

Tool Execution Layer

Autonomous AI Agent doesn't just reason, it acts. The tool layer gives it direct access to your infrastructure:

• SQL — Run queries across PostgreSQL, MySQL, Snowflake, BigQuery, Redshift, and more
• SSH — Execute commands on remote servers, inspect logs, manage deployments
• APIs — Call external REST APIs, webhooks, and third-party services
• MCP — Integrate with any Model Context Protocol server for extensible tool access

Always-On Infrastructure Monitoring

Autonomous AI Agent runs background heartbeat checks around the clock. Pipeline failures, connection health degradation, schema drift, detected before you notice. It doesn't wait for you to ask, it proactively surfaces issues the moment they appear.

Custom Script Checks

Write a TL or PySpark script that validates a business rule, checks row counts, compares snapshots, or runs any custom logic, then schedule it as a heartbeat check. If the script fails or returns an alert condition, Autonomous AI Agent automatically routes the notification through your configured channels.

• TL Scripts — Use ThinkingLanguage to query databases, check schema drift, validate row counts, and compare table snapshots, all in a few lines
• PySpark Scripts — Run full PySpark jobs as checks, data quality validation, ML model drift detection, cross-system reconciliation at scale
• Scheduled Execution — Cron-like scheduling, run checks every minute, hourly, daily, or on custom intervals
• Auto-Alerting — Failed checks trigger alerts through the Notify Router, Slack, PagerDuty, Email, or any webhook

Route Alerts Where They Matter

Autonomous AI Agent routes alerts intelligently based on event type and severity. Configure once, and every heartbeat failure, schema change, or pipeline error reaches the right people on the right channel.

Connect & Forget

You connect your databases and pipelines. Autonomous AI Agent learns your environment using ThinkingMemory, monitors it continuously, and alerts you the moment something needs attention, through whatever channel you prefer.