Top 10 AI ops platforms in 2026

The label "AI ops" covers two things that used to be separate. The older one is AIOps: machine-learning systems that correlate alerts, suppress noise, and flag anomalies on top of a monitoring stack. The newer one is the autonomous agent: software that reads your logs, metrics, traces, and tickets, forms a hypothesis, and hands back a root cause. In 2026 the line between them is mostly gone. Every incumbent now ships an "agent," and every agent startup now claims to do correlation. What actually distinguishes them today is how much of the work the AI does without a human driving, and where it does it.

This list is ordered by that axis: how much of an investigation the platform runs without a human driving it, from standalone autonomous agents at the top to dashboards-with-AI further down. That ordering is an editorial choice, not a verdict. A correlation engine that cuts your alert volume by 90% can be worth more to your team than an agent that writes prose about an incident you already understood. Read the "best for" and "watch for" lines, not just the number.

One disclosure up front: Hyground publishes this blog. We build an AI SRE agent, so we have a stake in how this category is described. We have tried to rank on capability rather than loyalty, which is why a direct competitor sits at number one and several platforms with more revenue than us sit below agents that ship less. Check our claims against the sources at the bottom.

The short version

You want an agent that investigates production on its own, hosted for you: Resolve AI.
You want that agent inside your own cluster, with your data never leaving: Hyground.
You already live in Datadog and want investigation where your telemetry already is: Datadog Bits AI SRE.

How to choose before you compare features

The feature lists across these platforms have mostly converged. Every vendor here, us included, will tell you their root-cause analysis is faster or deeper, and the real difference in mean time to resolution between two well-implemented platforms is usually smaller than the difference in how they deploy and what they are allowed to touch. Decide on posture before features. Three questions settle most of it:

Where does your operational data live when the agent runs, inside your perimeter or in a vendor's cloud?
Who has access to that data and those credentials?
Which compliance frame does that put you in?

Answer those, and the comparison narrows fast. A SaaS agent with strong investigation is the right call for a startup already on Datadog. It is a non-starter for a bank under DORA. An open assistant on the Grafana stack fits a cloud-native team that wants to move incrementally. A correlation engine like BigPanda is the answer when the problem is noise, not diagnosis. Match the tool to the constraint you actually have, not to the demo.

1. Resolve AI

Autonomous SRE agent · SaaS · investigates and recommends

Resolve AI is the clearest example of the new category: an agent built to debug and run production rather than a dashboard with a chatbot bolted on. It was founded in 2024 by Spiros Xanthos and Mayank Agarwal, who previously ran Splunk's observability business and whose earlier company Omnition was acquired by Splunk in 2019. That pedigree matters, because the founders helped build the previous generation of observability and are now arguing it does not go far enough.

The product connects to your existing telemetry and ticketing, builds a model of your systems, and runs investigations end to end. When an alert fires, it gathers context, proposes likely causes, and produces a reasoned write-up an engineer can act on. The company says it is deployed in production at large technology, financial-services, and consumer companies, and that it reduces operational toil for on-call engineers.

In February 2026 Resolve AI confirmed a $125M Series A led by Lightspeed, with Greylock, Unusual Ventures, Artisanal Ventures, and A* participating, at a $1B valuation and more than $150M raised in total. That is the most capital in this part of the market, and it shows in the breadth of integrations and the speed of releases.

Best for: teams that want a genuinely autonomous investigation agent and are comfortable with a vendor-hosted SaaS model.

Watch for: it runs as SaaS, so your operational data and credentials reach the vendor's environment. If you operate under strict data-residency rules, confirm the deployment options before you commit.

2. Hyground

Autonomous AI SRE agent · runs in your cluster · read-only by default · bring your own LLM

Hyground is the same idea as Resolve AI with a different answer to where the agent runs. The control plane installs via Helm into a small Kubernetes namespace you own, and the agent investigates anywhere your stack reaches across the systems you already operate: VMs and cloud resources through the AWS, Azure and GCP CLIs, managed databases (PostgreSQL, MongoDB, Redis, ClickHouse), observability backends (Prometheus, Loki, OpenSearch, Elasticsearch, Jaeger), ticketing (Jira, ServiceNow), source control (GitHub, GitLab), plus Kubernetes workloads. New sources plug in through MCP or API.

There is no Hyground-hosted control plane and no multi-tenant runtime today. Every adapter is read-only by default, with a small set of gated writes such as Jira comments and ServiceNow incident notes that you switch on explicitly. The agent uses whatever LLM you point it at through LiteLLM, including Azure OpenAI, Anthropic Claude, Google Gemini, AWS Bedrock, or a self-hosted open-weights model. Run it on your own cloud account, on EU sovereign infrastructure, or fully air-gapped. Your data does not leave your perimeter, which makes it a fit for regulated environments under DORA, NIS2,...

Best for: teams that need an autonomous agent inside their perimeter across Kubernetes and non-Kubernetes workloads (VMs, managed databases, cloud resources), especially under DORA, NIS2, or air-gap constraints.

Watch for: the control plane runs in Kubernetes, so a small cluster is needed for the agent itself even when the workloads it investigates are not on Kubernetes. If you have no compliance constraint and want the lightest setup, a SaaS agent may onboard faster.

3. Datadog Bits AI SRE

Observability-native agent · SaaS · investigates over Datadog telemetry

Bits AI SRE is Datadog's autonomous on-call agent, generally available since December 2025 after about six months of preview. It triggers on a monitor alert, gathers context from linked runbooks and prior investigations, forms competing hypotheses in parallel, and validates them against telemetry. The investigation shows as a tree of every hypothesis and data point with citations, which makes its reasoning unusually easy to audit. Datadog was named a Leader in the Forrester Wave for AIOps Platforms in Q2 2025.

Its reach matches Datadog's reach. Bits AI SRE reasons over data Datadog has already ingested: metrics, APM traces, logs, RUM, profiling, change tracking, and Watchdog anomalies. If your telemetry already lives in Datadog, time to value is close to zero. If your stack is hybrid, the agent has partial visibility into the rest. Third-party knowledge sources exist, with GitHub generally available and Splunk, Grafana, Dynatrace, Sentry, and ServiceNow in preview, but these supplement Datadog's ingest without federating a query across your other systems.

Best for: organizations already standardized on Datadog that want investigation where their telemetry sits.

Watch for: it is a separate, metered SKU priced per investigation on top of existing Datadog spend, there is no bring-your-own-LLM option, and EU data-residency support should be verified directly. If your stack is hybrid, the telemetry ceiling will show.

4. New Relic

Observability-native agentic platform · SaaS · SRE agent plus agent builder

New Relic spent 2026 moving from an AI assistant toward agentic operations. In February it launched an Agentic Platform, a no-code way to build, deploy, and govern custom AI agents and workflows, alongside a dedicated New Relic SRE Agent designed to work across the incident lifecycle rather than just answer questions. It also shipped New Relic Knowledge, which correlates telemetry with prior incidents, system changes, and service relationships so both engineers and agents can move from detection to explanation faster. New Relic was named a Leader in the IDC MarketScape for Worldwide AIOps in 2026.

The pitch is that you are not limited to one vendor-defined agent. If your team has a repeatable operational task, you can assemble an agent for it inside the platform and govern it centrally. For shops already paying for New Relic, that turns the AI layer into something you extend rather than just consume.

Best for: existing New Relic customers who want a native SRE agent and the option to build their own agents on top of their telemetry.

Watch for: like all observability-native agents, its reach is strongest over data already flowing into New Relic. Usage-based pricing can be hard to predict as agent activity grows, so model the cost before you scale it.

5. Dynatrace

Observability-native agent · SaaS and managed · causal RCA plus generative assistant

Dynatrace's differentiator is Davis AI, which combines predictive, causal, and generative AI rather than relying on a language model alone. The causal engine uses Dynatrace's topology model (Smartscape) to determine the actual root cause of a problem instead of guessing from correlation, which is a real advantage on complex, fast-changing environments. On top of that sits Davis CoPilot, now generally available, which turns natural-language prompts into Dynatrace Query Language, builds dashboards and notebooks, and explains root causes in plain language. Dynatrace has signaled Dynatrace Assist as the next iteration of that assistant.

For 2026 Dynatrace has been previewing generative remediation: Davis CoPilot for Workflows can summarize incidents, recommend fixes, and autonomously edit manifests to autoscale infrastructure. That moves it past detection toward action, inside the boundaries of the Dynatrace platform.

Best for: teams that want deterministic, topology-aware root cause analysis backed by a deep monitoring platform, with a generative layer on top.

Watch for: Dynatrace is a heavy, opinionated platform that wants to own the full observability stack. The causal engine is strongest when your environment is fully instrumented by Dynatrace, which is a meaningful adoption commitment.

6. PagerDuty

Incident-response platform · SaaS · AI agent suite including an SRE agent

PagerDuty is the incumbent in on-call paging and has repositioned around the PagerDuty Operations Cloud. In October 2025 it launched an AI agent suite of four agents: an SRE Agent for triage and remediation, a Scribe Agent that turns incident calls and chat into summaries, a Shift Agent that resolves on-call schedule conflicts, and an Insights Agent for analytics questions. The agents live behind the PagerDuty Advance add-on, and the SRE Agent's full surface also requires PagerDuty AIOps.

The architecture is worth understanding before you compare it to a standalone agent. PagerDuty's SRE Agent reasons primarily over PagerDuty's own incident graph: incident records, related past incidents, change events, and runbook sections. It reaches into your telemetry by fetching from integrations on demand (Datadog, Grafana, New Relic, CloudWatch, Confluence, GitHub) without building its own infrastructure topology. If your operational center of gravity is already PagerDuty, that incident-context model is a natural fit.

Best for: organizations whose incident process already runs through PagerDuty and who want AI layered onto paging, scheduling, and post-incident review.

Watch for: the AI lives behind add-ons with credit-based consumption, so the real cost depends on usage. Investigation depth into the actual infrastructure is bounded by what its integrations return, not by a live system model.

7. Splunk Observability (Cisco)

Observability and ITSM platform · SaaS and on-prem · AI troubleshooting agents

Since the Cisco acquisition, Splunk and AppDynamics have been consolidated into a single portfolio, Splunk Observability, that unifies AppDynamics application visibility, the Splunk platform's log analytics, Observability Cloud, and IT Service Intelligence. Cisco has introduced agentic AI across that portfolio, including AI Troubleshooting Agents in Splunk Observability Cloud and AppDynamics that automatically analyze incidents and surface likely root causes.

This is the enterprise-scale option. If you already run Splunk for log analytics or AppDynamics for application performance, the AI layer arrives on data you are already paying to collect, and ITSI gives you service-level health modeling on top. In return for the weight, Cisco's broader portfolio adds federated identity, network visibility, and a security tie-in that pure-play observability vendors cannot match. The trade-off is the trade-off of any large incumbent platform: depth and breadth come with longer procurement and a roadmap shaped by a very large company's priorities.

Best for: large enterprises already invested in Splunk or AppDynamics that want AI investigation on their existing data and service models.

Watch for: the portfolio is mid-consolidation, with unified capabilities still rolling out across products. Confirm which AI features are generally available on the specific products you license, rather than assuming the whole suite is shipping.

8. ServiceNow

ITSM and ITOM platform · SaaS · predictive AIOps plus Now Assist agents

ServiceNow approaches AI ops from the service-management side. Predictive AIOps uses machine learning to model normal metric behavior and set adaptive thresholds automatically, which removes the manual work of tuning thousands of static alerts and catches silent failures before users feel them. Now Assist for ITOM then summarizes and triages alerts so that even first-line responders can understand an issue quickly. Around this, ServiceNow has built an agentic layer: an AI Agent Fabric for agents to coordinate across IT, HR, and security, and an Action Fabric MCP Server that lets external agents trigger governed ServiceNow workflows.

The advantage is the CMDB and the workflow engine. If your organization already runs change, incident, and asset management in ServiceNow, AIOps that sits on that data and can drive your existing automation is hard to match for closed-loop process.

Best for: ITSM-first enterprises with a mature ServiceNow footprint and a populated CMDB that want AIOps wired into existing workflows.

Watch for: ServiceNow is strongest as an orchestration and service layer rather than as deep, code-level production investigation. For engineering-led debugging of distributed systems, pair it with a tool built for that.

9. BigPanda

Event-correlation AIOps · SaaS · noise reduction across many tools

BigPanda is the clearest example of classic AIOps done well, and it is moving toward agentic operations. Its strength is mature, deterministic alert correlation across a long tail of monitoring tools, with integrations and ML models tuned over a decade. Few competitors come close on multi-tool ingest depth. It acts as an event hub that sits above your existing monitoring and observability tools, ingesting and normalizing alerts from a large number of sources and using machine learning to correlate them into a smaller set of actionable incidents. The company is now expanding from correlation toward agent-driven detection, incident coordination, and change-risk analysis.

If your core pain is alert fatigue more than deep diagnosis, this is the category that addresses it directly. BigPanda's value is in turning a flood of low-signal alerts into a manageable stream and routing the right incident to the right team, on top of whatever monitoring you already run.

Best for: large operations teams drowning in alerts across many monitoring tools that need correlation and noise reduction more than autonomous root-cause work.

Watch for: correlation is not the same as investigation. BigPanda tells you which alerts belong together and which incident matters; it is less about reading your logs and code to explain why. Vendor ROI figures are marketing claims, so validate them against your own alert volume.

10. Grafana Cloud

Open observability stack · cloud and self-managed · Sift diagnostics plus Grafana Assistant

Grafana rounds out the list as the option for teams built on open standards. Sift is a diagnostic assistant included in Grafana Cloud that runs automated checks across metrics, logs, and traces during an incident and surfaces a curated list of interesting findings. Grafana Assistant adds a conversational agent that helps you investigate incidents, write queries, and understand your data, and as announced at GrafanaCON 2026 it is extending beyond Grafana Cloud to Enterprise and OSS, which brings AI assistance into self-managed environments.

Because Grafana sits on Prometheus, Loki, and Tempo, it fits naturally into a cloud-native, open-source-leaning stack, and the AI features arrive without forcing you onto a proprietary data platform. It is closer to assisted investigation than to a fully autonomous agent today, but it is the most open entry here and the easiest to adopt incrementally.

Best for: teams already running the Grafana stack who want AI-assisted investigation without committing to a closed observability vendor.

Watch for: Sift and Assistant guide a human through an investigation. They do not run it end to end. If you want an agent that closes the loop on its own, this is a starting point, not the finish line.

Honorable mentions

These platforms did not make the top 10 but are worth tracking if your context narrows the choice differently.

Honeycomb. Observability with strong AI-assisted investigation: BubbleUp for anomaly drilldown, the Honeycomb Query Assistant for natural-language exploration. A good fit if your team already lives in distributed tracing and wants AI alongside the data, not as a separate agent.
Causely. Causal AIOps startup focused on causal inference instead of statistical correlation. Smaller footprint than the rest of this list, but a real architectural differentiator if you want deterministic root cause over probabilistic guesses.
Robusta. Kubernetes-native AIOps with open-source roots. Includes Robusta AI for incident response and KRR (Kubernetes Resource Recommender) for capacity work. A good entry point for cloud-native teams that want an OSS-grounded option.
Sumo Logic. Established observability incumbent shipping AI Mosaic for incident analysis. If you already pay for Sumo Logic for log analytics, the AI layer arrives without a vendor switch.
Komodor. Kubernetes troubleshooting platform with AI-assisted root cause analysis focused on cluster events and resource state. Useful adjunct if you run K8s at scale and want troubleshooting tooling that already understands controllers and CRDs.

FAQ

What is an AI ops platform? An AI ops platform applies machine learning or large language models to IT operations work: correlating alerts, detecting anomalies, investigating incidents, and in newer products, running a root-cause investigation autonomously. The term now spans both classic AIOps correlation engines and the new generation of autonomous SRE agents.

What is the difference between AIOps and an AI SRE agent? AIOps usually means statistical correlation and anomaly detection on top of a monitoring stack, which reduces alert noise and groups related events. An AI SRE agent goes further: it reads logs, metrics, traces, and tickets, forms hypotheses, and returns a diagnosis, doing the investigation a human on-call engineer would otherwise run.

Which AI ops platform is best for regulated or air-gapped environments? Most platforms on this list are SaaS, which sends operational data to the vendor. If you need data to stay inside your own perimeter, look at agents that deploy into your own infrastructure. Hyground installs into your Kubernetes cluster, stays read-only by default, lets you bring your own LLM, and supports EU sovereign and fully air-gapped deployments.

Do these tools replace observability platforms like Prometheus or Datadog? No. Most of them sit on top of your existing observability stack and reason over the data it collects. An agent reads your Prometheus, Loki, or Datadog data; it does not replace the act of collecting it.

Can an AI ops agent fix incidents automatically? A few platforms are moving toward automated remediation, but most autonomous agents today are read-first: they investigate and recommend, and a human approves any change. Closed-loop remediation exists in limited, gated forms, and is still uncommon as a default.

Disclosure: this article is published by Hyground, which builds an AI SRE agent included in the list. We ranked on capability and have linked sources for the claims about every platform.