Trusted by leaders like Nomura Bank and AvidXchange, Autoheal helps platform engineering teams build, govern, and continuously improve AI agents for post-coding workflows like coding cost efficiency, incident response, and vulnerability remediation, all grounded in their own systems and processes.

AI is helping engineering teams ship more code, faster than ever. But that acceleration comes with a growing operational burden: more production incidents to respond to, more security vulnerabilities to remediate, and spiraling token costs to contain. Autoheal is built for these challenges and already battle-tested at industry leaders such as Nomura Bank and AvidXchange, where off-the-shelf point agents failed to deliver.

Today, the company announced a $7.9 million seed round to scale the industry’s most advanced self-improving software factory, giving enterprise platform engineering teams 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, alongside participation from Emergent Ventures, U&I Ventures, Darkmode Ventures, Batch Ventures, and Param Hansa Values.

Why platform engineering needs a new operating model 

Repetitive software development lifecycle (SDLC) workflows such as incident response and vulnerability remediation consume more than a third of an engineering team’s capacity. As coding agent adoption grows, controlling LLM spend and managing context is joining that list. To manage these demands, platform engineering teams are shifting toward a “software factory” model powered by specialized AI agents.

However, at-scale rollout of these agents often fails due to fragmented tools, a lack of shared context, and strict security constraints. Establishing a unified platform for creating, managing, and iteratively improving all software factory agents, including the existing coding agents, has therefore become an immediate priority. This ensures every agent gets the same engineering context, secure production access, private evaluation infrastructure, cost controls, and a way to stay current as the organization changes.

“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.”

What Autoheal is building 

Autoheal’s software factory gives enterprises the infrastructure and tools to transform their engineering organization into a self-improving machine. It connects existing coding agents, code repositories, CI/CD, observability, cloud runtimes, and issue trackers, giving all worker agents in the factory a shared engineering context graph. Two additional self-improvement agents keep working in the factory in the background:

• The Evaluator agent scores every worker agent’s run. For example, a coding agent can be evaluated by scoring the specs and PRs it generates, using the downstream signals of review comments, CI failures, and caused incidents as the evaluation criteria.
• The Healer agent fixes low-scoring worker agents by opening pull requests that improve skills, prompts, tools, or model selections. It even verifies those changes against historical benchmarks for regressions before engineer review.

For platform engineering teams, this creates a continuous agent healing loop as systems change. Every behavior change is version-controlled in git and requires engineer approval. Actions remain governed and audited, with visibility into access, reasoning, and costs. The end goal is higher accuracy, faster execution, and lower cost per successful task, with engineers expanding autonomy as agents prove reliable.

Traction

Autoheal is already operating inside complex regulated environments, where engineering teams are using it to cut incident response times, handle customer support escalations, and free up thousands of hours of 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.

“Autoheal helped us tackle two major challenges at once: making our engineers faster at troubleshooting across our complex environment, and significantly optimizing our software costs across our monitoring stack,” said Vijay Pendyala, SVP Engineering & Customer Success, at Empiric Earth.

Origin story 

Autoheal grew out of the founders’ experience building enterprise engineering and AI platforms at Harness, Microsoft Azure, ThoughtSpot, and AppDynamics. After scaling Harness to over $200M ARR, the team recognized a new reality: while building individual AI agents had become easy, safely deploying them across the SDLC and across engineering teams had become extremely time- and token-consuming. To prevent agent sprawl and ensure day-2 governance, a platform must manage agents as code, overseen by continuously learning meta-agents. That insight became Autoheal’s software factory.

“Enterprises are moving quickly from experimenting with AI agents to asking how they can operate them safely and efficiently at scale across the entire software factory,” said Harpinder Singh of Innovation Endeavors. “Autoheal is building the agent infrastructure layer that makes that possible. The opportunity is much larger than one agent or one workflow. It is giving platform teams a repeatable scalable way to deploy specialized intelligence across the engineering organization.”

What’s next

Autoheal plans to expand further into sovereign intelligence by using reinforcement learning on each company’s private engineering data to train enterprise-specific small language models within its approved security boundary. These models will power all software factory agents cost-effectively while addressing a fundamental limitation of frontier models: extensive knowledge of the external world but limited understanding of a company’s inner workings.

In the long term, the same architecture can extend beyond software engineering into data and security engineering. Autoheal is betting that every large enterprise will run a software factory that has its own population of specialized agents, and wants to be the platform that engineering teams use to build, govern, and continuously improve them.

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