Autoheal has raised $7.9 million in seed funding to expand a platform that manages the work generated by 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.
Faster code generation, the company says, has not automatically produced faster or safer software delivery: more generated code can create more alerts, reviews, vulnerabilities and cloud-model spending. Autoheal is focused on that less visible operational layer of the AI coding boom, aiming to treat post‑coding tasks as part of a coordinated software factory rather than a collection of disconnected agents.
Autoheal’s continuous healing loop
At the center of Autoheal’s approach are two supervisory agents. An Evaluator scores worker agents’ outputs using signals such as code-review comments, continuous-integration failures and incidents tied to a change. A Healer then proposes improvements to underperforming agents by adjusting prompts, skills, tools or model selections. Proposed changes are tested against historical benchmarks before review, agent behavior is version-controlled in Git, and engineers retain approval authority.
The company positions the system as an operating layer for monitoring and improving task-specific agents rather than an autonomous coding bot. Autoheal argues that ongoing evaluation and controlled revision are required to maintain reliable performance as codebases, infrastructure and organizational rules evolve.
Addressing operational issues from faster coding
Autoheal’s platform connects coding tools with repositories, CI/CD systems, observability platforms, cloud environments and issue trackers to give specialized agents a shared view of the engineering environment while keeping them inside a customer’s cloud and security controls. That architecture matters in regulated or technically complex organizations where agents need production context but cannot be given unrestricted access. The platform also aims to provide a consistent way to evaluate agent behavior, track changes and control cost as teams deploy more agents.
Early enterprise deployments and claimed results
Autoheal says it is already running in enterprise environments including Nomura Bank and AvidXchange. According to company-supplied figures, 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. The company notes these are customer claims rather than independent benchmarks. Empiric Earth is also using the platform for troubleshooting and software-cost optimization, the announcement says.
Those early deployments point to incident response and troubleshooting as initial use cases where fragmented information has a clear cost and where improvements can be measured in time returned to engineers.
Use of funds and roadmap
Autoheal was founded by Utkarsh Ohm, Sid Choudhury and Puneet Saraswat, drawing on experience at Harness, Microsoft Azure, ThoughtSpot and AppDynamics. The company 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 team plans to expand beyond software engineering into data and security operations if the platform can generalize its evaluation-and-repair model across workflows with different risk profiles and success criteria.
The seed round highlights a shift in the enterprise AI market: generating code may be becoming less the bottleneck than governing a growing population of agents and absorbing the operational work they create. Autoheal’s opportunity, the company contends, is to turn that constraint into measurable, auditable infrastructure for platform teams.
