Autoheal raises $7.9M to build a self-improving software factory
Autoheal has raised a $7.9M seed round led by Innovation Endeavors. Its platform enables enterprise engineering teams to create, govern, and enhance AI agents for incident response, security patches, and managing AI coding costs.
September 28, 2026 - 12:00 pm
By Utkarsh Ohm, Sid Choudhury and Puneet Saraswat
Credit: Autoheal
AI is expediting code shipping for engineering teams at an unprecedented rate, but this rapid progress comes with escalating operational burdens. This includes:
- Increasing production incidents to handle.
- Growing security vulnerabilities that require immediate remediation.
- Soaring token costs to manage.
Autoheal offers a solution already proven in major organizations like Nomura Bank and AvidXchange. The company has now secured a $7.9 million seed round to expand its self-improving software factory capabilities.
Why platform engineering needs a new operating model
Repetitive tasks within the software development lifecycle (SDLC), such as incident response and vulnerability remediation, consume over a third of an engineering team's time. As coding agent adoption grows, managing Large Language Model (LLM) spending and context becomes another significant challenge.
Platform engineering teams are turning to a "software factory" model using specialized AI agents to address these issues. However, large-scale deployment often fails due to fragmented tools, a lack of shared context, and stringent security constraints. A unified platform is needed to create, manage, and iteratively improve all software factory agents, ensuring:
- Consistent engineering context for every agent.
- Secure production access and private evaluation infrastructure.
- Cost controls and the ability to adapt to organizational changes.
What Autoheal is building
Autoheal's software factory provides enterprises with infrastructure and tools to transform their engineering teams into self-improving entities. It integrates existing coding agents, code repositories, CI/CD pipelines, observability tools, cloud runtimes, and issue trackers, creating a shared engineering context graph for all worker agents. Two key self-improvement agents are also included:
- The Evaluator agent scores each worker agent's performance based on various criteria, like the quality of generated code specifications and pull requests. Downstream signals from review comments, CI failures, and caused incidents contribute to this evaluation.
- The Healer agent fixes low-performing worker agents by optimizing their behavior.