Introducing Autoheal, the AI for Production Engineering
Built for SRE & Support Engineering teams at the most demanding enterprises
We are proud to unveil Autoheal, a multi-agent AI platform built ground up to serve the production engineering needs of the most demanding enterprises on the planet. The name “Autoheal” embodies our vision to autonomously heal the pain of engineers across the entire Software Development Lifecycle (SDLC). We are starting with the production-facing engineering functions of Site Reliability Engineering (SRE) and Support Engineering.
The Observability Paradox
In the past two decades of the cloud era, we have seen production visibility get revolutionized. Companies like Datadog, Splunk and New Relic made it possible to see every metric, error, log, and trace. But they left the hardest part - interpretation and decision-making, entirely to us.
To cope, the industry attempted to layer on even more software: on-call schedulers, pagers, incident response orchestrators, cost optimization engines, and endless collaboration and analysis tools. Instead of simplifying our lives, these tools simply increased the production context tax. We were jumping between half a dozen dashboards while manually copying the context needed just to piece together a single story, often losing crucial decision traces in Slack or Teams channels during the process. We found ourselves burdened with managing the very software meant to help us.
In today’s AI era, this "human-for-everything" model has hit a breaking point. Thanks to AI coding agents like Claude Code and Cursor, the volume of code reaching production is growing exponentially. This also means that production complexity is scaling beyond what any human team can keep in their head. If we don't automate the "thinking" and "doing" in production with highest urgency possible, engineers will spend 100% of their time just paying the production context tax.
Why We Built Autoheal
Between the three of us, we have half a century of combined experience developing and operating mission-critical enterprise software platforms at organizations like Harness, ThoughtSpot and Microsoft Azure. We have the battle scars of 3 AM pages, high-stakes incident war rooms, and postmortems where similar failure modes appeared again and again.
What we consistently observed was not a lack of tools, but a lack of shared understanding. Each incident required rebuilding production context from scratch: what changed when, how the system actually works, which signals could be trusted vs what’s noise and how did we fix similar issues previously. Senior engineers became bottlenecks, tribal knowledge stayed trapped in heads and chats, and feature velocity suffered in the face of constant firefighting. And when that one senior engineer left the team, we became paralyzed during incident response.
Our dream was to have an experienced SRE who is available 24 x 7 x 365, never forgets, reasons across not just shared infrastructure but also inter-dependent microservices, and can scale across teams without burning out. Needless to say, we expected our SRE to operate under strict security and governance controls when operating mission-critical production systems. Autoheal is our collective learning distilled into agentic software.
How It Works
Autoheal actively investigates alerts, hypothesizes root cause, and proposes mitigating fixes under human supervision. It also automates the postmortem phase completely. At its core is the Production Context Graph (PCG), a continuously updating, living map that connects your infrastructure, application logic, production tools and tribal knowledge in real-time. The PCG is built through autonomous exploration of your observability, cloud and code stack, and iteratively refined by a Reinforcement Learning loop as you use Autoheal.. On top of the PCG lies a Multi-Agent Platform of specialized agents that collaborate with humans to solve production problems safely and efficiently.
The Curator: The knowledge keeper of your production environment. It curates instructions for the agents from all your sources of knowledge and continuously monitors the utility of each instruction . It monitors changes in real-time and proactively seeks human input when in doubt to ensure that the platform relies on the most accurate, high-fidelity information.
The Triager: Leverage unsupervised time-series anomaly detection and contextual clustering to encrich, deduplicate, group, suppress, assign and escalate the alert as needed.
The Hypothesizer: Develops root cause hypotheses by performing causal inference using PCG as the grounded context including prior decision traces. Designed with Explainable AI principles, it also proposes mitigating fixes for human review and declares incidents when needed.
The Coordinator: Coordinates incident response between on-call humans and the other agents on Slack/Zoom/Teams, while also keeping all stakeholders in sync throughout the lifecycle of an incident.
The Analyzer: Runs the postmortem phase in depth capturing accurate root cause and proposing actionable preventive fixes.
The Verifier: Adversarial agent inspired by Generative Adversarial Networks (GANs), it verifies the work of all the other agents, ensuring that their actions are backed by concrete auditable evidence.
The Tracer: Reflects on every investigation & incident deeply to identify key decision forks as decision traces that get stored back into the PCG. This creates a Reinforcement Learning-like feedback loop that finally unlocks the value trapped in your unstructured data.
What Is Unique About Autoheal
For AI agents focused on production engineering to succeed in real-world enterprise deployments, three crucial gaps must be addressed.
The Context Gap: can the AI navigate my organization’s fragmented context?
The Trust Gap: can I trust the AI to strictly adhere to my organization’s security policies?
The Value Gap: can the AI create value in my organization without increasing costs?
Autoheal is the only AI built to address these gaps simultaneously.
System of Record for Decision Traces in Complex Environments
Traditional alerting systems cannot distinguish between novel failures and the noise of unrelated, spurious errors that often appear simultaneously. SREs manage this through sheer experience, especially at large enterprises running a complex web of legacy and modern applications. They compensate for the fragmented context by manually discarding known flapping alerts and irrelevant errors. However, this manual filtering is the primary driver of alert fatigue, causing novel, high-impact failures to be buried and ignored.
Autoheal bridges this gap through its built-in Incident Management capabilities covering both On-Call Management and Slack/Teams-native Incident Response. Rather than requiring a separate training phase, Autoheal routes the requests to the right on-call engineer as soon as it has root cause hypotheses available. The engineer can work with Autoheal to mitigate or can decide to declare a formal incident. This means they start collaborating with other engineers on chat and video conferencing with the goal of rapid incident mitigation. As engineers collaborate by building on top of the proposed hypotheses, discussing known issues, rejecting unrelated signals, zoning into novel failures, Autoheal captures the resulting decisions in real-time. It then records these decision traces in the PCG, ensuring the fleeting intuition used in incident response becomes a permanent, institutional memory.
Security & Governance For The Most Demanding Enterprises
Enterprises trust SREs to be extremely careful when executing commands on production systems even if those commands are read only. Autoheal aims to gain the same trust by providing the same strict security and governance guarantees. Organizations have fine-grained visibility and controls to configure precisely which commands Autoheal can run, how frequently, and under what credentials. A detailed, immutable audit trail records every action (command, time, credential) for complete traceability and compliance.
Additionally, the platform supports strong enterprise security standards, including Bring Your Own Cloud (BYOC) and airgapped deployment options that are powered by the customer's own LLM API key. It also provides Single Sign-On (SSO) and Role-Based Access Control (RBAC), and adheres to rigorous compliance certifications such as SOC 2 and ISO 27001. It even supports self-hosted runners for customers looking for something in between fully-managed SaaS and BYOC. All these aspects ensure that the AI operates within the organization's established GRC posture.
Unified AI Platform for Incident Management
The days of managing production incidents through a fragmented and expensive patchwork of tools are finally behind us. For too long, SRE teams have been forced to stitch together a disparate ecosystem—relying on a legacy on-call scheduler like PagerDuty/OpsGenie for alerts, an incident coordinator like FireHydrant/incident.io for structured response, and a standalone, often siloed, AI SRE bot for nascent automation.
Autoheal is engineered to end this era of fragmentation. It is not just another tool to add to the stack; it is the consolidation. It obviates the need for these separate, single-purpose solutions by integrating all the critical capabilities required to manage the entire incident lifecycle, from initial alert to postmortem, into an unified AI platform. If you are unhappy with the value delivered by legacy incident management tools (including their archaic seat-based pricing models), then Autoheal is your answer.
Who Autoheal Is For
Autoheal is designed for enterprise SRE teams managing complex distributed systems with need for reliability, explainability, and control. These teams face alert overload and are seeking faster mitigation with the additional constraint that they have no additional capacity available to manage another system. These same challenges also apply to the customer front lines. Support Engineers often lack the deep system and code context needed to solve customer escalations. Autoheal provides them with the exact system state and correlated code changes, allowing them to resolve issues without the constant "ping-pong" between Support and Engineering.
The most significant benefit engineering leaders can derive from Autoheal is the reallocation of engineering capacity. Currently, developers spend up to 70% of their time on toil which are non-development tasks such as production firefighting, alert fatigue, repetitive troubleshooting, and responding to customer escalations. SRE teams likewise struggle to allocate sufficient capacity for proactive reliability work, system hardening, and infrastructure engineering. By accelerating investigations and proposing evidence-backed fixes, Autoheal helps teams shift focus from reactive firefighting to proactive reliability improvements and strategic initiatives.
If you are looking to solve any of the following use cases, we built Autoheal for you:
Why Talk to Us Now
AI-powered development is accelerating change velocity across the industry to levels never seen before. Production engineering cannot afford to be a bottleneck. Every day that passes without better automation compounds operational risk: more code, more complexity, and the same finite pool of experienced engineers. Teams that invest early in AI-native production engineering will not only resolve alerts and incidents faster but also build systems that learn, adapt, and improve over time. Every alert, incident, and customer escalation is a learning opportunity that cannot go wasted in the AI era.
We have successfully proven Autoheal’s core value proposition through close collaboration with our initial design partners, ranging from large enterprises to fast-growing startups. We are now looking to partner with more engineering organizations ready to integrate AI into production decision-making responsibly with the goal to reduce MTTR and preserve operational knowledge.
If this resonates, book a demo with us. Our Forward Deployed Engineers will make sure that you are able to see value in the shortest time possible. And, together we will bring production engineering to the AI era.
