Autoheal Raises $7.9M Seed Round for Its Self-Improving Software Factory

Autoheal has raised $7.9 million in seed funding to expand a platform designed to manage the work that follows AI-assisted coding: investigating incidents, remediating vulnerabilities, controlling model costs, and keeping software agents aligned with an enterprise’s systems and policies.

Innovation Endeavors led the round, with participation from Emergent Ventures, U&I Ventures, Darkmode Ventures, Batch Ventures, and Param Hansa Values. Harpinder Singh of Innovation Endeavors is joining Autoheal’s board.

The financing arrives as companies are learning that faster code generation does not automatically produce faster or safer software delivery. More code can mean more alerts, reviews, vulnerabilities, and cloud-model spending. Autoheal is focused on that less visible layer of the AI coding boom.

An operational problem created by faster coding

AI coding assistants have lowered the effort required to produce code, but many downstream engineering tasks remain labor-intensive. Production incidents still need investigation, security findings still need remediation, and platform teams still need to understand why an automated action succeeded or failed.

Autoheal argues that these jobs should be treated as part of a coordinated software factory rather than handed to a collection of disconnected agents. Its platform connects coding tools with repositories, CI/CD systems, observability platforms, cloud environments, and issue trackers. The aim is to give specialized agents a shared view of the engineering environment while keeping them inside the customer’s cloud and security controls.

That architecture matters in regulated or technically complex organizations, where an agent may need production context but cannot be given unrestricted access. It also addresses a practical governance problem: as teams deploy more agents, they need a consistent way to evaluate behavior, track changes, and control cost.

How Autoheal’s continuous healing loop works

At the center of Autoheal’s approach are two supervisory agents. An Evaluator scores the output of worker agents using signals such as code-review comments, continuous-integration failures, and incidents connected to a change. A Healer then proposes improvements to poorly performing agents by changing their prompts, skills, tools, or model selections.

The company says those proposed changes are tested against historical benchmarks before review. Agent behavior is version-controlled in Git, and engineers retain approval authority. In practice, that makes the system less like an autonomous coding bot and more like an operating layer for monitoring and improving a group of task-specific agents.

The distinction is important. Building a convincing agent demo is relatively straightforward; maintaining reliable performance as codebases, infrastructure, and organizational rules change is harder. Autoheal is betting that ongoing evaluation and controlled revision will become a standard requirement for enterprise agent deployments.

Early deployments put the focus on incident response

Autoheal says it is already running in enterprise environments including Nomura Bank and AvidXchange. According to figures supplied by the company, Nomura reduced mean time to resolution from two hours to 15 minutes in one deployment. AvidXchange reports that the system has shortened root-cause analysis to minutes and freed engineering capacity for product work.

Those results are company and customer claims rather than independent benchmarks, but they point to the initial use case Autoheal believes is most urgent: incident response. Troubleshooting often requires gathering evidence across logs, monitoring systems, tickets, code changes, and cloud services. An agent with access to that context may be able to assemble a useful diagnosis faster than an engineer moving manually between systems.

Empiric Earth is also using the platform for troubleshooting and software-cost optimization, according to the announcement. Together, the deployments suggest Autoheal is starting with workflows where the cost of fragmented information is easy to see and where improvement can be measured in time returned to engineers.

What the seed round is intended to fund

Autoheal was founded by Utkarsh Ohm, Sid Choudhury, and Puneet Saraswat, drawing on experience at Harness, Microsoft Azure, ThoughtSpot, and AppDynamics. The team says the new capital will support development of reinforcement-learning systems and private, enterprise-specific models trained on engineering data that remains within a customer’s boundaries.

The longer-term plan extends beyond software engineering into data and security operations. That expansion will depend on whether the platform can generalize its evaluation-and-repair model across workflows with different risk profiles and success criteria.

For now, the fundraise highlights a shift in the enterprise AI market. The next constraint may not be generating more code, but governing the growing population of agents and absorbing the operational work they create. Autoheal’s opportunity rests on turning that constraint into infrastructure that platform teams can measure, audit, and improve.