10xCRM: Self-Healing Pipelines with AI | AgentixLake
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CASE STUDY · B2B SAAS · 10XCRM

10xCRM’s pipelines now propose their own fixes.

10xCRM runs a multi-tenant analytics platform on Google Cloud. We added an AI agent on AWS that diagnoses every pipeline failure and proposes the fix as a pull request. An engineer reviews and merges each one.

10xCRM logoAgentixLake logo

Issue resolution: 6–8 hours → 30 minutes40 hrs/month of maintenance handled by the agent

UPDATED OCTOBER 2026

The problem

10xCRM integrates more than a million records a day from its tenants, through Mage.ai pipelines on Google Cloud. Keeping those pipelines healthy cost 40 hours of paid maintenance a month.

Every failure was investigated by hand across Slack alerts, Mage.ai logs and the code in GitHub. The information existed, but it took 6–8 hours to piece it together and ship a fix.

How an incident is handled

What we built

Safety model

01Read-only investigation is the default.
02Observe, diagnose, propose, approve, and execute are separate permissions.
03Actions are bounded by environment, asset, and risk level.
04High-risk changes require explicit approval.
05Every tool call, input, recommendation, and result is recorded.
06Recovery and rollback paths are defined before automated execution.

Outcome

30 min
Issue resolution, down from six to eight hours
70%
Less manual operations work across the measured workflow
1M+
Tenant records integrated per day, across monitored pipelines
40 hrs
Monthly pipeline maintenance, now handled by the agent

For each failure, the agent proposes a known runbook, a configuration change, a replay or a code fix, with a complete diagnosis summary. Nothing reaches production on its own: an engineer reviews and merges every pull request. The 40 hours a month of maintenance 10xCRM used to pay for is now handled by the agent.

TECHNOLOGY
  • Google Cloud logoGoogle Cloud
  • Google BigQuery logoGoogle BigQuery
  • Mage.ai logoMage.ai
  • Cloud Run
  • dbt logodbt
  • Slack logoSlack
  • GitHub logoGitHub
  • Amazon Web Services (AWS) logoAWS
  • Amazon Bedrock logoAmazon Bedrock
  • Anthropic Claude logoAnthropic Claude

Where does your data team lose time every week?

Start with the pipeline failures that cost your team the most time, and let the agent propose the fixes.