Autoheal announced a $7.9 million seed round to scale a self-improving “software factory” for enterprise platform engineering teams. The platform gives organizations a way to build, deploy, govern and continuously improve multiplayer cloud AI agents across the software development lifecycle. The round was led by Innovation Endeavors, with Harpinder Singh joining Autoheal’s board; Emergent Ventures, U&I Ventures, Darkmode Ventures, Batch Ventures and Param Hansa Values also participated.
Autoheal was created to address rising operational burdens as AI helps teams ship more code: more production incidents to respond to, more security vulnerabilities to remediate, and growing LLM token costs. The company says off-the-shelf point agents failed to deliver at customers such as Nomura Bank and AvidXchange, driving demand for a unified platform that provides shared context, secure production access, private evaluation infrastructure and cost controls across all agents.
How Autoheal works
Autoheal connects existing coding agents, code repositories, CI/CD, observability, cloud runtimes and issue trackers to create a shared engineering context graph for all worker agents. Two background meta-agents keep the factory continuously improving: an Evaluator that scores every worker agent run using downstream signals such as review comments, CI failures and caused incidents; and a Healer that opens pull requests to fix low-scoring agents by improving skills, prompts, tools or model selections and verifies those changes against historical benchmarks before engineer review. Actions are version-controlled in git, require engineer approval, and remain governed and audited with visibility into access, reasoning and costs.
‘Our experience taught us that while building the first version of an AI agent is easy, scaling it consistently across the enterprise SDLC is the real challenge,’ said Sid Choudhury, Co-Founder and CEO of Autoheal. ‘Platform engineers need a unified platform to deploy agents that don’t just execute tasks, but continuously improve alongside complex enterprise workflows. We built Autoheal so they can immediately step into the role of AI engineers and accelerate ROI, without spending a year building the underlying infrastructure.’
Autoheal traction and customers
Autoheal is already operating inside complex regulated environments, where engineering teams use it to cut incident response times, handle customer support escalations and free up engineering capacity. ‘Our production operations teams spend valuable time triaging alerts and managing incidents, while also pulling engineers away from their software development activities. Autoheal gives us a platform that takes investigation timelines down from hours to minutes. The fact that it runs entirely within our own cloud, in compliance with our controls, made it a natural fit for how we operate,’ said Sameer Jain, CIO, Wholesale at Nomura Bank.
‘In production incident response, Autoheal took our time to root cause to minutes, with evidence our engineers trust. That’s time our developers stay focused on feature work. Next, we’re shifting it left into other critical parts of our SDLC, because every engineering hour we get back goes into shipping faster for our customers,’ said Krish Shetty, CTO & SVP at AvidXchange. Vijay Pendyala, SVP Engineering & Customer Success at Empiric Earth added that Autoheal helped make engineers faster at troubleshooting across a complex environment and significantly optimized software costs across their monitoring stack.
Origins and roadmap
The founders built enterprise engineering and AI platforms at Harness, Microsoft Azure, ThoughtSpot and AppDynamics. After scaling Harness to over $200M ARR, they observed that building individual AI agents had become easy while safely deploying them across the SDLC and teams had become time- and token-consuming. That insight led to Autoheal’s software factory approach: manage agents as code overseen by continuously learning meta-agents to prevent agent sprawl and ensure day-2 governance.
Harpinder Singh of Innovation Endeavors said Autoheal is building the agent infrastructure layer that enables enterprises to operate AI agents safely and efficiently at scale. Autoheal plans to expand into sovereign intelligence using reinforcement learning on each company’s private engineering data to train enterprise-specific small language models within approved security boundaries, with the aim of powering software factory agents more cost-effectively and improving understanding of a company’s internal context. The company expects the same architecture to extend beyond software engineering into data and security engineering over time.
About Autoheal: Autoheal describes itself as a self-improving software factory for enterprise engineering teams, combining a context layer that continually learns from private engineering data with a governed control plane for AI agents across the SDLC, including coding agents. The platform aims to accelerate feature delivery while lowering LLM token costs, reducing production incident MTTR and speeding up vulnerability remediation. For more information, visit autoheal.ai.
