Autoheal Raises US$7.9M to Build a Software Factory for AI Agents

Autoheal Co-founders: Utkarsh Ohm, Sid Choudhury, and Puneet Saraswat

Autoheal, an enterprise software startup, has secured a $7.9 million seed round led by Innovation Endeavors, with participation from Emergent Ventures, U&I Ventures, and strategic angel investors.

The capital will fund a continuous improvement architecture—a context and control plane that governs, evaluates, and optimizes AI coding agents for post-commit engineering tasks like incident response and vulnerability remediation.

What is Autoheal building with its $7.9 million seed round?

Autoheal, an enterprise software startup focused on AI-powered engineering operations, has raised $7.9 million in seed funding to develop what it describes as a self-improving software factory for enterprise AI agents.

The round was led by Harpinder Singh at Innovation Endeavors, with participation from Emergent Ventures, U&I Ventures, Darkmode Ventures, Batch Ventures (CTO Fund), and Param Hansa Values.

The round also attracted a group of technology executives and founders as angel investors, including:

• Shawn Kung, Founder of GIT100
• Sumeet Arora, Chief Product Officer at Teradata
• Anshu Sharma, Co-Founder and CEO of Skyflow
• Savin Goyal, Co-Founder and CTO of Outerbounds
• Srikant Gokulnatha, former SVP at ThoughtSpot

The funding comes as enterprises move beyond experimenting with AI coding agents and confront a more difficult challenge: keeping AI-generated software reliable, secure, cost-efficient, and supportable after code has been committed.

Autoheal, co-founded by Indian founders, is positioning its platform around that operational gap.

Rather than replacing coding agents that developers already use, the company aims to provide the infrastructure, context, governance, evaluation, and continuous improvement layer that allows agents to operate across the software development lifecycle.

Why is AI coding creating a new software engineering bottleneck?

AI coding agents have accelerated feature development, but faster code generation does not eliminate the operational work required after a commit.

Engineers still need to investigate production incidents, remediate vulnerabilities, manage cloud and tooling environments, and control the growing cost of AI-assisted development.

Today, much of that work depends on individual engineers adding skills to coding agents and running workflows manually from their laptops. That approach becomes difficult to scale when:

• The engineer who understands a system is unavailable.
• An agent lacks the context required to investigate an issue.
• Skills and instructions become outdated as systems evolve.
• Different engineers prompt agents in different ways.
• Repetitive operational workflows require consistent outcomes rather than open-ended reasoning.

Autoheal argues that enterprises need to turn individual engineering expertise into a shared, continuously improving capability that can operate 24/7 inside a controlled environment.

That is the problem its software factory is designed to address.

How does Autoheal's software factory work?

Autoheal describes its platform as a shared, cloud-based system that turns ad-hoc agent workflows into continuously running enterprise capabilities.

The architecture is built around two core planes:

Layer	What it does	Key capabilities
Context Plane	Gives agents the information they need before acting	Context graph, repositories, services, tickets, releases, skills, AGENTS.md files and agent memories
Control Plane	Governs how agents operate	Agent definitions, budgets, model selection, identity, roles, approval policies and tool access

The context graph connects the repositories, services, tickets, releases, and other information an agent needs to perform a task.

A skills and memory registry then stores the instructions, AGENTS.md files, and memories that agents can reference.

The control plane determines how those agents operate, including which models they use, what tools they can access, how much they can spend, and when they must request human approval.

Autoheal also provides a tool gateway that allows enterprise integrations to be registered, scoped, and audited once across agents.

The approach is deliberately additive. Autoheal does not require enterprises to replace their existing coding agents. Instead, it changes the context and operational infrastructure around those agents so that improvements made in one workflow can benefit others.

Which enterprises are already using Autoheal?

Autoheal says its technology is already being used by Nomura Bank and AvidXchange, with incident response and alert triage among the first workflows deployed.

At Nomura Bank, Autoheal agents collect evidence across multiple systems while operating within the bank's access controls.

“Autoheal gives us a platform that takes investigation timelines down from hours to minutes.”

— Sameer Jain, CIO, Wholesale, Nomura Bank

At AvidXchange, the platform reduced incident root-cause analysis to minutes, according to the company. AvidXchange is now expanding Autoheal into additional parts of its software development lifecycle.

“Autoheal took our time to root cause to minutes, with evidence our engineers trust.”

— Krish Shetty, CTO & SVP, AvidXchange

The examples illustrate Autoheal's initial focus: applying AI agents to repeatable, high-value engineering workflows where speed, evidence, and consistency can be measured.

How does Autoheal continuously improve its AI agents?

Autoheal's central differentiation is that deploying an agent is only the beginning.

Software systems, APIs, infrastructure, and tools change continuously. As they do, an agent's instructions and context can become outdated.

The platform therefore uses two agents—the Evaluator and the Healer—to create a continuous improvement loop.

What does Autoheal's Evaluator do?

The Evaluator scores every agent run against outcomes generated by the software development lifecycle.

For example, a remediation workflow can be assessed based on downstream signals such as:

• Code review comments
• CI/CD reruns
• Production alerts
• Incident outcomes
• The actual root cause identified after an incident

Rather than relying exclusively on whether an agent completed a task, Autoheal evaluates the trajectory and outcome of the run.

Those evaluations are performed using the enterprise's own data and remain inside its security boundary.

How does Autoheal's Healer improve agents?

The Healer takes the weaknesses identified by the Evaluator and turns them into specific changes to an agent's operating context.

Potential changes include:

• Updating a skill
• Correcting an AGENTS.md file
• Adding a new memory
• Narrowing a tool's permissions
• Changing the model used for a task

Each proposed change is opened as a pull request and back-tested against historical runs.

Changes are promoted only when they meet the required blast-radius threshold, while engineers retain final approval.

This creates a feedback loop in which agent performance improves through real-world engineering outcomes rather than static configuration alone.

How is Autoheal designed for enterprise security and governance?

Autoheal is targeting enterprises that need AI agents to operate within existing security, compliance, and governance requirements.

The platform supports deployment in a customer's own cloud, or in fully air-gapped environments, with controls around data sovereignty.

Its enterprise controls include:

• Customer-controlled deployment: Agents can operate within the enterprise's own cloud or an air-gapped environment.
• Least-privilege access: Tools default to read-only access with fine-grained policies.
• Auditability: Tool calls are recorded through an audit trail.
• Private evaluation: Agent performance is evaluated using the customer's own runs and outcomes.
• Model flexibility: Enterprises can use frontier models for complex reasoning and lower-cost or open-weight models where evaluations show they are effective.

For regulated organizations, the ability to determine where an agent runs, what information it can access, and what actions it can take is increasingly important as AI moves deeper into software development.

How quickly can enterprises deploy an Autoheal agent?

Autoheal says its first agent can be running within minutes once the necessary integrations are connected, with team-level rollout possible within hours, depending on an enterprise's access and deployment requirements.

Organizations first connect Autoheal to their existing development and operations stack, including:

• Code repositories

• CI/CD pipelines

• Observability platforms
• Cloud runtimes
• Issue trackers
• Existing coding agents

The platform then builds the context graph that agents use.

Autoheal recommends starting with the post-coding workflow generating the greatest amount of manual work, such as incident response or vulnerability remediation, before expanding into other engineering workflows.

What results is Autoheal targeting?

Autoheal is initially offering two routes into its platform, with specific performance targets.

Starting point	Target outcome
Alert triage and incident response	Up to 80% lower MTTR
Vulnerability remediation	3x faster vulnerability burndown
AI coding cost and context management	Up to 40% lower AI coding cost

The company plans to validate those outcomes through a three-week proof of value conducted inside the customer's environment.

Success is measured against an agreed baseline rather than simply whether an agent has been deployed.

Key metrics include:

• Time to reach an evidence-supported root cause
• Engineering effort required to produce a validated vulnerability fix
• Cost per successful coding task

This measurement-first approach is important because enterprise AI adoption is increasingly shifting from experimentation toward measurable productivity, reliability, security, and cost outcomes.

How can enterprises get started with Autoheal?

Autoheal is offering two starting points for organizations evaluating the platform.

1. Automate the biggest operational pain point

Enterprises can begin with alert triage, incident response, or vulnerability remediation, with Autoheal targeting:

• Up to 80% lower MTTR
• 3x faster vulnerability burndown

2. Control AI coding cost and context

Alternatively, a DevEx team can deploy an Autoheal agent to govern the context and spending consumed by existing coding agents, without requiring developers to change the coding tools they already use.

Autoheal targets up to 40% lower AI coding costs through this approach.

Both paths begin with a three-week proof of value in the customer's own environment, using agreed success metrics and the organization's existing baseline.

With its $7.9 million seed round, Autoheal is now looking to build on early deployments at Nomura Bank and AvidXchange and establish its software factory as the operational layer for enterprises moving from AI-assisted coding toward continuously managed, autonomous engineering workflows.