Autoheal
by Autoheal AI Inc.
Self-improving AI agents for incident response, vulnerability fixes and release work after code is written
Autoheal is an agent platform for platform and engineering leaders that runs AI agents on the work that follows coding: production incident investigation, vulnerability remediation, release readiness and support escalations. An Evaluator agent scores the worker agents and a Healer agent opens pull requests to fix the ones that slip.
Autoheal is a San Francisco startup that sells what it calls a self-improving software factory: a platform that deploys always-on AI agents across the post-coding stages of the software development lifecycle. The platform runs inside a company's own cloud, or as SaaS, and connects to existing coding agents (Claude, Codex, GitHub Copilot, Cursor), code repositories, CI/CD pipelines, observability tools, cloud runtimes and issue trackers to build a shared engineering context graph. Prebuilt agents cover incident response, vulnerability remediation, release readiness testing, support escalation response and AI coding cost optimization, and teams can build custom agents on the same harness. Two supervisory agents make the loop: an Evaluator scores worker agents using downstream signals such as code review comments, failed builds and production incidents, and a Healer proposes changes to prompts, tools, skills or model selection by opening a pull request, which is version-controlled in Git, benchmarked and approved by a human. Agents can be invoked from a CLI, API, webhook, MCP, Slack or Microsoft Teams. On September 28, 2026 the company announced a $7.9 million seed round led by Innovation Endeavors. It names Nomura, AvidXchange, Empiric Earth, Nauto and Oscilar as users; Nomura's CIO is quoted saying investigation timelines fell from hours to minutes, and the company says Nomura's average incident resolution dropped from two hours to 15 minutes. VentureBeat noted that these customer figures come without sample size, methodology or time period, and that no independent benchmark of the feedback loop has been published. The founders previously held senior roles at Harness, Yugabyte, ThoughtSpot and Microsoft.
A VP of platform engineering or SRE leader whose on-call and security backlog is growing faster than headcount, and who already runs coding agents and wants them governed and measured in one place.
Shorter time to root cause on production incidents, with agent changes reviewed as Git pull requests rather than silent prompt edits.
At a Glance
- Category
- Developer Tools
- Pricing
- Usage-based, Contact for pricing
- Target Market
- CTOs, VPs of Engineering, Platform Engineering Teams, SRE Teams, Security Teams
- Deployment
- Cloud-first, Self-hosted, Hybrid
- Headquarters
- San Francisco, USA
- Team Size
- 11-50
Key Features
- ✓Evaluator agent
Scores every worker agent using downstream signals such as CI failures, review comments and production incidents, so agent quality is measured from real outcomes.
- ✓Healer agent
Opens pull requests that change an underperforming agent's prompts, tools, skills or model, keeping every change benchmarked, version-controlled and human-approved.
- ✓Prebuilt SDLC agents
Ready agents for incident response, vulnerability remediation, release readiness, support escalations and coding cost efficiency shorten time to a first deployment.
- ✓Multi-harness and multi-model routing
Runs on its own harness or alongside Claude, Codex, Copilot and Cursor, and routes routine tasks to cheaper models to cut cost per task.
- ✓Sovereign deployment
Can run fully air-gapped in the customer's own cloud with pre-approved models, which matters for regulated firms such as banks.
- ✓Per-agent guardrails
Per-agent budgets, approval gates, scoped short-lived credentials and full audit trails limit what an autonomous agent can touch and spend.
- ✓Multi-interface invocation
Agents can be triggered from CLI, API, webhook, MCP, Slack or Microsoft Teams, so they fit existing on-call and chat workflows.
Capabilities
Use Cases
- •On-call incident response
An agent pulls telemetry, recent changes and tickets when an alert fires, so engineers start from an evidence-backed root cause instead of a blank dashboard.
- •Vulnerability backlog burn-down
Agents generate fix pull requests for known vulnerabilities, letting security teams close findings in minutes or hours instead of waiting weeks for sprint capacity.
- •Support escalation triage
Oscilar uses an agent that triages support tickets across Grafana, Slack, ClickHouse and Pylon before an engineer picks them up.
- •AI coding cost control
Platform teams track cost, latency and accuracy per agent and team, then shift routine tasks to smaller models to lower spend per task.
Ideal For
Best For
- ✓Production incident investigation and root-cause analysis across logs, traces and recent deploys
- ✓Automated remediation of known vulnerabilities through pull requests a human approves
- ✓Release readiness checks before a deploy goes out
- ✓Triage of support escalations that need engineering context
- ✓Measuring and cutting the per-task cost of AI coding agents through model routing
Not Ideal For
- ✗Teams looking for an AI pair programmer to write new features, since Autoheal targets the work after code is written and integrates with coding agents rather than replacing them
- ✗Buyers who need a published rate card or a self-serve trial, because pricing is consumption-based, undisclosed, and access starts with a demo and a roughly three-week evaluation
- ✗Organizations that require independent proof before buying, since the published customer results are company-supplied and unaudited
Deployment
Market Analysis
Pros
- ✓Named enterprise users including Nomura and AvidXchange, with on-record quotes from their CIO and CTO
- ✓Every agent change is a Git pull request with human approval, which keeps an audit trail
- ✓ISO 27001 and SOC 2 Type II, plus an air-gapped deployment option
- ✓Founders with prior scale experience at Harness, Yugabyte and ThoughtSpot
Cons
- ✗Customer results (such as two hours to 15 minutes MTTR) are company-supplied without methodology, as VentureBeat pointed out
- ✗No published pricing, and the definition of a billable session and whether model costs are included are unclear
- ✗Seed-stage company with a team of about 13 engineers, so vendor continuity is a real diligence question
- ✗Almost no independent practitioner discussion yet; the Hacker News launch post drew 2 points and no reviews exist on G2
Pricing
Consumption (per agent session)
Contact for pricing
- ✓Billed per agent session
- ✓Prebuilt and custom agents
- ✓SaaS or sovereign deployment
- ✓Demo and roughly three-week evaluation before purchase
Autoheal publishes no rate card. Billing is consumption-based per agent session; VentureBeat reported illustrative figures of about $20 for a complex incident investigation and $2 for a simple vulnerability fix. It is not disclosed whether underlying model API costs are included, or whether there are minimums, platform fees or volume discounts. Buying starts with a demo.
Security & Compliance
Sources
This page was written from 6 sources, 4 on domains other than autoheal.ai.
- 1.autoheal.ai — autoheal.aivendor
- 2.autoheal.ai — about usvendor
- 3.siliconangle.com — autoheal raises 7 9m to evaluate and fix ai agents with ai a
- 4.venturebeat.com — autoheal wants to manage the work ai coding agents leave beh
- 5.globenewswire.com — autoheal raises 7 9m to build a self improving software fact
- 6.hn.algolia.com — search
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