Autoheal Raises $7.9M to Build AI Agents That Fix Other AI Agents in Enterprise Software Development

Autoheal Secures $7.9M to Address Enterprise AI Agent Challenges

Autoheal has closed a $7.9 million seed round led by Innovation Endeavors, with participation from Emergent Ventures, U&I Ventures, Darkmode Ventures, Batch Ventures, and Param Hansa Values1

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. The funding will scale its platform that enables enterprise engineering teams to build, govern, and continuously improve AI agents for incident response, security remediation, and AI coding cost management. Harpinder Singh from Innovation Endeavors has joined Autoheal's board following the investment1

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The startup addresses a critical pain point in AI-driven software engineering. While AI tools help teams ship code faster than ever, this acceleration creates operational burdens including more production incidents, security vulnerabilities, and spiraling token costs. Repetitive software development lifecycle workflows like incident response and vulnerability remediation now consume over a third of engineering team capacity1

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. Managing LLM costs and context has joined this list as coding agent adoption grows across enterprise software development.

Platform Engineering Needs Unified AI Agent Management

Many enterprise engineering teams have shifted toward a "software factory" model powered by specialized AI agents to handle these demands. However, large-scale rollouts often fail due to fragmented tools, lack of shared engineering context, and strict security constraints1

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. Off-the-shelf point agents have proven insufficient at industry leaders such as Nomura Bank and AvidXchange, where Autoheal is already battle-tested1

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"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 Autoheal1

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. "Platform engineers need a unified platform to deploy agents that don't just execute tasks, but continuously improve alongside complex enterprise workflows."

Establishing a unified platform for creating, managing, and iteratively improving all software factory agents has become an immediate priority. This ensures every agent receives the same engineering context, secure production access, private evaluation infrastructure, and cost controls1

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Self-Improving Software Factory Architecture

Autoheal's platform connects existing coding agents, code repositories, CI/CD pipelines, observability tools, cloud runtimes, and issue trackers to create a shared engineering context graph accessible to all worker agents1

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. The platform can be hosted within organizations' private clouds, maintaining secure boundaries while integrating with existing development infrastructure2

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Two specialized meta-agents power the continuous improvement loop. The Evaluator agent scores every worker agent's performance by analyzing specs, pull requests, review comments, CI failures, and caused incidents1

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. The Healer agent then autonomously fixes underperforming agents by opening pull requests that improve skills, prompts, tools, or model selections1

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. Each change is version-controlled in Git, verified against historical benchmarks for regressions, and requires engineer approval before deployment1

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This creates a continuous agent healing loop that adapts as systems change. Actions remain governed and audited, with full visibility into access, reasoning, and costs. The goal is higher accuracy, faster execution, and lower cost per successful task, with engineers expanding autonomy as agents prove reliable1

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Proven Results in Regulated Environments

Autoheal is already operating inside complex regulated environments, delivering measurable impact. Sameer Jain, CIO of Wholesale at Nomura Bank, reported that investigation timelines dropped from hours to minutes. "Our production operations teams spend valuable time triaging alerts and managing incidents, while also pulling engineers away from their software development activities," Jain said1

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. "The fact that it runs entirely within our own cloud, in compliance with our controls, made it a natural fit for how we operate."

Enterprise engineering teams are using the platform to cut incident response times, handle customer support escalations, and free up thousands of hours of engineering capacity1

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. At AvidXchange, production incident response now provides evidence engineers trust, keeping developers focused on feature work rather than firefighting1

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Future Plans Beyond Software Engineering

Autoheal plans to develop new reinforcement learning techniques to train customers' AI agents on their own private engineering data2

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. The vision is enabling customers to create enterprise-specific small language models that operate entirely within secure private cloud environments. These SLMs would power each customer's fleet of software factory agents, reducing costs while enhancing industry-specific knowledge2

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Long-term, Choudhury believes Autoheal's architecture can expand beyond software engineering into data and security engineering2

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. Watch for announcements around private SLM training capabilities and expansion into adjacent engineering domains as the company scales with its seed funding.