Autoheal raises $7.9M to evaluate and fix AI agents with… AI agents

Autoheal AI Inc. has raised $7.9 million in seed funding to push a blunt idea: if AI agents are now building more of the software, AI agents should also be the ones keeping them in line. The round was led by Innovation Endeavors, with support from Emergent Ventures, U&I Ventures, Darkmode Ventures, Batch Ventures and Param Hansa Values.

The pitch lands in a very specific mess. Companies are using AI tools to ship code faster, but that speed has also brought more production incidents, more vulnerabilities and higher token bills. In response, many teams have moved to a “software factory” setup, with dozens of specialized agents handling different parts of the workflow. That creates a new problem. The agents themselves can break, especially when they lack shared context and tight security controls.

Autoheal says its platform is meant to sit above that chaos. It connects to existing coding agents, code repositories, CI/CD pipelines and observability tools, and it can run inside a customer’s private cloud boundary. The system builds a shared engineering context graph and uses two specialized agents to keep watch: an Evaluator agent scores downstream agents using signals such as CI failures and incident reports, while a Healer agent tries to repair weak performers by opening pull requests that adjust model selection, prompts, tools and skills.

Those changes are version-controlled in Git, checked against historical benchmarks and approved by a human supervisor. Co-founder and CEO Sid Choudhury said the company’s founders previously worked on enterprise AI and engineering infrastructure at Microsoft, ThoughtSpot and Harness, and that Harness helped crystallize the need to manage agents “as code.”

Autoheal is still in stealth, but it says enterprise customers including Normura Holdings, AvidXchange and Empiric Earth have already used the platform to cut incident resolution times and save thousands of engineering hours. Now the startup is aiming at reinforcement learning techniques that would let customers train agents on private engineering data, with an eye toward enterprise-specific small language models running entirely in secure private clouds. Long term, Choudhury thinks the same setup could stretch beyond software engineering into data and security work too.