Best AI Agents for DevOps in 2026: 14 Agents Ranked by Blast Radius

Resolve AI and Traversal go furthest toward actually touching production, behind a permission layer you configure yourself. Datadog's Bits AI SRE and PagerDuty's SRE Agent are the safer default if you already pay for either platform. New Relic Autopilot is the cleanest example of the opposite bet: investigate hard, recommend clearly, and never touch anything without a person in the loop. This guide ranks 14 AI agents built for operational and platform engineering work (incident detection and triage, root cause analysis, on-call response, runbook execution, CI/CD failure diagnosis, infrastructure-as-code changes, and cost or capacity optimization) on four things: blast radius, whether the mean-time-to-resolution numbers are measured or just marketed, what context each agent can actually see, and real pricing verified against each vendor's own page in August 2026, noted wherever one won't publish a number.

That's a different question than the one our best AI coding agents guide answers. A coding agent's mistake usually gets caught by a test suite, a reviewer, and a CI pipeline before it reaches anyone. A DevOps agent frequently runs during the incident itself, when the system is already broken and the clock is the thing everyone is staring at, which is why blast radius, not a benchmark score, is the real buying question here. It's also the line between an agent and a tool: something that only drafts a step for a person to run by hand is assistive software, not an agent, and belongs in best AI agents in 2026 instead. Every product below plans multiple steps, calls a real tool such as Kubernetes, PagerDuty, GitHub, or a cloud API, and observes what comes back before deciding what happens next.

Updated August 2026: What Changed

• New Relic shipped Autopilot on June 23, 2026, its first agentic SRE product, and built it explicitly recommendation-only: "Autopilot recommends actions but does not take them. A person reviews and acts," per New Relic's own launch announcement.
• Datadog rebuilt Bits AI's pricing around a shared AI Credits pool in 2026, covering Bits Chat, Investigation, Code, and Agent Builder from one meter instead of a flat per-investigation fee. Source: datadoghq.com/pricing.
• PagerDuty renamed its flagship annual survey from the "State of Digital Operations" report to the "State of AI-First Operations" report for 2026, a naming change that tracks how central agentic AI has become to its own pitch.
• Resolve AI raised a $125 million Series A in February 2026, reaching a reported $1.5 billion valuation, one of several signals that institutional money is betting the "AI SRE" category grows fast, per TechCrunch's coverage of the round.
• Gartner put a number on the risk that comes with that speed. By 2028, 40% of infrastructure and operations organizations running agentic AI at scale in production will hit a business-critical service disruption of their own making, up from under 1% in 2026, per Gartner's research on agentic AI in IT operations.

Key Facts

• More than two-thirds of organizations now lose over $300,000 per hour during a major incident, and 8% lose $1 million or more per hour, per PagerDuty's 2026 State of AI-First Operations report.
• Human error is a contributing factor in roughly 9 out of 10 outages, and 57% of major outages now cost more than $100,000, according to the Uptime Institute's 2026 Annual Outage Analysis.
• By 2029, only 20% of AI-recommended actions in IT operations will still require human-in-the-loop approval, down from 80% in 2025, per Gartner, the exact trend that makes checking an agent's approval gate before you buy more urgent every year, not less.
• By 2028, 40% of infrastructure and operations organizations running agentic AI at scale in production will experience a business-critical service disruption, up from fewer than 1% in 2026, per the same Gartner research.
• Only 16% of what companies currently call an "AI agent" in production actually plans, observes, and adapts on its own, according to Menlo Ventures' State of Generative AI in the Enterprise report; most of the rest are fixed-sequence automation wearing agent branding.
• Rootly's own marketing cites a 40% to 70% cut in mean time to resolution from AI incident automation, per Rootly's published research, the closest thing this category has to a hard MTTR number, and it's Rootly's figure about Rootly, not an independent study.

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Quick Comparison Table

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Blast Radius: The Question That Actually Matters

Every product on this list will happily tell you what it found. Far fewer will tell you plainly what it's allowed to change, and that second question is the real decision. An agent that only investigates can be wrong all day and the worst outcome is a wasted five minutes. An agent that can revert a commit, scale a node pool, or run a runbook step changes that math: a wrong hypothesis doesn't just waste time anymore, it executes.

Gartner is already putting a number on that shift. Only 20% of AI-recommended actions in IT operations will still require human-in-the-loop approval by 2029, down from 80% in 2025, and by 2028, 40% of infrastructure and operations organizations running agentic AI at scale in production will cause a business-critical disruption of their own, up from under 1% in 2026. The direction of travel is toward less approval, not more, which is why the approval gate a vendor ships today, not the one on its roadmap slide, is what you're actually buying.

The clearest real-world version of this happened in March 2026, when Amazon's retail storefront went down for roughly six hours and lost an estimated 6.3 million orders. Amazon's own account of the cause was specific: not faulty AI-generated code, but an engineer who acted on inaccurate advice an AI agent inferred from an outdated internal wiki, per Wharton's AI & Analytics Initiative analysis of the incident. Every guardrail in this category assumes the human reviewing an agent's output will catch a wrong hypothesis before approving it. Amazon's incident is the case where that assumption failed anyway, and it's why "a human approves it" is necessary but not sufficient: the reviewer needs the context to catch a confidently wrong answer, not just the authority to click yes.

For the build-side version of this same question (the exact rule set, the exact moment an agent should act, ask, or hand off) see how an AI DevOps agent and an AI incident response agent are typically scoped, and what actually makes something an AI agent rather than assistive software with an agent's marketing.

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Evidence: Measured MTTR vs Vendor-Claimed MTTR

Every vendor in this category implies it makes incidents shorter. Almost none of them prove it with anything independent. Rootly is the rare exception that puts a number in writing at all: 40% to 70% faster mean time to resolution from AI incident automation. It's worth being precise about what that number is. It's Rootly's own marketing, about Rootly's own product, not a controlled study, not a third-party benchmark, and not a figure reproduced anywhere else in the category. That doesn't make it false. It makes it unverified, which is a different thing, and worth treating as a hypothesis to test in your own environment rather than a number to budget against.

The independent research that does exist tends to describe the category, not any one product in it. PagerDuty's own 2026 survey found that 63% of organizations that improved operational resilience were using AI, against 53% of those that didn't improve, a real gap, but a correlational one, not proof that any specific agent caused it. Before you trust a vendor's MTTR pitch on a live pilot, how to evaluate and test AI agents covers building the kind of before/after test set that turns a marketing claim into an actual answer for your own incidents.

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Context: What Each Agent Can (and Can't) See

An agent is only as good as what it's allowed to look at. Some of these tools build a persistent model of your whole system (services, deploys, dependencies, past incidents) before an alert ever fires. Others start closer to a blank slate each time and lean entirely on whatever one observability platform already has. For the fuller picture of the tooling that actually feeds that context layer, best AI agent observability tools in 2026 covers the trace and eval layer that sits underneath a lot of these agents, and what AI agent observability means covers the concept if you're building this in-house instead of buying it.

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1. Resolve AI: Configurable Autonomy Up to Reverting a Commit

Resolve AI is built around the idea that most "AI SRE" products stop one step short of useful: they explain what's wrong and a human still does the fix by hand. Resolve's Incidents agent investigates in parallel across code, infrastructure, and telemetry, then can act on what it finds: silence a noisy alert, revert a bad commit, open a pull request, or run a GitHub workflow, scoped by permissions defined at the org, team, or individual level. It maintains a continuously updated graph of services, dependencies, and deploys built from more than 60 integrations, and every resolved incident becomes context the next investigation can retrieve.

That range is also the thing to interrogate hardest before buying. An agent cleared to revert a commit needs its guardrails configured correctly on day one, not tuned after the first bad call.

Pricing: Not published. Contact sales for a demo, deployment guidance, and a quote. Source: resolve.ai/pricing.

Best for: Teams that want an agent cleared to actually change something in production, with permission scoping they control rather than a vendor default.

2. Traversal: Deep Root Cause Analysis, With Workers That Act Unprompted

Traversal builds what it calls a Production World Model, a live map connecting Kubernetes clusters, cloud infrastructure, databases, service meshes, observability data, and your GitHub history, so it can trace a symptom back through dependencies instead of guessing from a single stack trace. That depth is aimed squarely at large, complex, regulated systems where a human root-causing an incident manually would need to hold ten services' worth of context in their head at once.

The part worth reading twice is "Traversal Workers": a layer that can act unprompted, rolling back a deployment, engaging a circuit breaker, or suppressing a noisy alert without waiting for a person to approve that specific step. That makes Traversal the most autonomous option in this guide by design, backed by Sequoia and Kleiner Perkins, which also makes its guardrail configuration the single most important setup decision a buyer makes.

Pricing: Not published. Contact sales for a demo. Source: traversal.com.

Best for: Enterprise platform teams running complex, regulated systems who want root cause analysis deep enough to trust an autonomous action layer.

3. Datadog Bits AI SRE: Investigation and Remediation Inside One Credit Pool

Bits AI SRE is Datadog's agentic teammate for the platform you may already run your observability through, and its biggest practical advantage is that it isn't a separate tool: it investigates alerts using the same APM, logs, infrastructure, and RUM data already flowing into Datadog, without a second integration to maintain. Remediation runs through configurable Guardrails: Ask Mode requires approval before Bits executes anything, scoped by team, role, or individual, while Deny Mode limits it to recommendations only, and either can be loosened toward one-click execution as trust builds.

Pricing moved onto a shared AI Credits pool in 2026 that also covers Bits Chat, Bits Code, and Bits Agent Builder, so an SRE investigation (roughly 6.5 credits on average) competes for budget against every other Bits feature your team turns on, not a separately metered line item.

Pricing: $500/month for 500 AI Credits billed annually (about $1/credit), or $1.30/credit on-demand; an autonomous SRE investigation averages roughly 6.5 credits. Source: datadoghq.com/pricing.

Best for: Teams already running Datadog as their primary observability platform who want triage and remediation in the same pane of glass.

4. PagerDuty SRE Agent: Pre-Approved Remediation Built on Your On-Call Data

PagerDuty's SRE Agent, part of the PagerDuty Advance bundle alongside a Scribe Agent for meeting capture, a Shift Agent for on-call scheduling, and an Insights Agent for operational analytics, is explicit about its operating model: it can "detect, triage, diagnose incidents and perform approved remediation," drawing on a memory of past incidents, diagnostics, knowledge base entries, and how responders have handled similar alerts before. Because it sits on top of the escalation and on-call data PagerDuty already has, it has a real head start on knowing who owns what before an investigation even begins.

The pricing is genuinely two separate add-ons stacked on top of an Incident Response plan: AIOps for the underlying alert correlation and noise reduction, and Advance for the agents themselves, each billed and scoped independently.

Pricing: AIOps starts at $799/month ($699/month billed annually), priced per accepted event; PagerDuty Advance adds $415/month on an annual commitment. Source: pagerduty.com/pricing/aiops.

Best for: Teams already standardized on PagerDuty for on-call who want remediation actions tied to escalation data they already trust.

5. New Relic Autopilot: Deep Investigation, Zero Action Risk by Design

Autopilot, launched June 23, 2026, is New Relic's answer to a real question in this category: what if an agent went all-in on investigation and simply refused to act? It triages alerts against historical baselines, correlates golden-signal metrics with infrastructure health, flags regressions tied to a specific deploy, and walks through distributed traces, logs, and metrics to find a root cause, all built on New Relic's own observability data substrate. New Relic's own documentation is explicit about the boundary: "Autopilot recommends actions but does not take them. A person reviews and acts," and the agent does not create alerts, instrument applications, or write to external systems beyond posting to Slack.

That makes it the cleanest control case in this guide for a team that wants the investigative power of an agent without touching its production-action risk at all.

Pricing: Requires Pro ($349/user/month annual, $418.80/month monthly) or Enterprise (custom), plus the Advanced Compute add-on. Source: newrelic.com/pricing and docs.newrelic.com.

Best for: Teams that want agent-grade investigation depth with the lowest possible action risk while they build trust in the category.

6. Cleric: Self-Learning Investigation That Gets Faster Every Time

Cleric's pitch is operational memory: every investigation builds a knowledge graph of your environment, and the next one gets faster because Cleric already knows the shape of your infrastructure instead of rediscovering it from scratch. It integrates broadly, Kubernetes state, Datadog, Prometheus, Elasticsearch, Grafana, GitHub, AWS, GCP, Confluence, and Slack, and reports its findings directly into the channel where your team is already working the incident.

Cleric investigates and proposes a fix; it does not ship that fix itself, which puts it firmly in the propose-and-wait tier alongside Parity and Rootly rather than the act-with-guardrails tier above it. Backed by more than $14 million from Zetta Venture Partners and Vertex Ventures US, it's a smaller company than the platform incumbents on this list, which shows up as thinner enterprise governance tooling today.

Pricing: Not published. Request-based, sales-engaged, typically with an evaluation period before a quote. Source: cleric.ai.

Best for: Teams that want investigation quality to compound over time and are comfortable keeping a human in the fix-shipping loop.

7. Parity: Kubernetes-First Response Before an Engineer Wakes Up

Parity, a Y Combinator S24 company, positions itself narrowly and specifically: the first line of defense for on-call engineers running Kubernetes. It plugs into an existing alerting stack (PagerDuty, Datadog) and, by the time a human opens their laptop, has already triaged the alert, inspected the cluster, determined a likely root cause, and suggested a fix. If a team has existing runbooks, Parity follows them the way an engineer would rather than improvising a diagnostic path from nothing.

That narrow focus is the whole trade-off. Parity doesn't try to cover CI/CD, cost optimization, or non-Kubernetes infrastructure the way the platform-wide competitors above do, which makes it a lighter lift to adopt but a smaller piece of a larger operational picture.

Pricing: Not published. Source: tryparity.com.

Best for: Kubernetes-heavy teams that want a focused first responder rather than a platform-wide agent suite.

8. Dynatrace Davis AI: Mature Causal RCA, With Agentic Remediation Still in Preview

Davis AI is the oldest, most established root cause engine in this guide, built on deterministic causal analysis across Dynatrace's own full-stack observability data rather than an LLM guessing at correlation. Davis CoPilot layers a natural-language interface on top for building queries, dashboards, and workflows, and per Dynatrace's own FAQ, that generative and CoPilot layer currently carries no separate license fee: it draws down your existing consumption-based (DDU) allocation instead of a standalone SKU.

The newer, more autonomous piece, agentic workflows that can reportedly edit a manifest to autoscale infrastructure on their own, is real but not yet generally available; Dynatrace's own documentation places it in preview. That makes Davis AI a strong pick today for root cause analysis specifically, and a name worth watching rather than buying yet for autonomous remediation.

Pricing: No separate fee for Davis AI/CoPilot currently; usage draws from your existing DDU-based Dynatrace consumption. Source: docs.dynatrace.com.

Best for: Enterprise teams already standardized on Dynatrace who want the most mature root cause engine in this list, with autonomous remediation still on the roadmap.

9. Rootly AI SRE: Root Cause Correlation Bolted Onto a Modern Incident Platform

Rootly built its reputation as an incident response and on-call platform first, and its AI SRE add-on layers root cause identification, change correlation, impact analysis, and noise suppression on top: instead of just flagging that an alert fired, it checks what deployed recently and surfaces that as a likely cause. Stack integration and API/MCP access mean the output can feed back into whatever automation a team already runs.

Rootly is also the one vendor here that publishes an actual MTTR figure (40% to 70% faster resolution), which is worth remembering is Rootly's own marketing claim rather than an independently verified number, as covered above. Startup-friendly pricing programs, up to 50% off for companies under 100 employees and under five years old, make it more approachable than most of the enterprise-first names on this list.

Pricing: Incident Response and On-Call each start at $20/user/month (Essentials); AI SRE is a separate add-on, contact sales. Source: rootly.com/pricing.

Best for: Teams that want change-aware root cause suggestions layered onto an incident platform they're already adopting for coordination.

10. incident.io AI: Scribe and AI-Native Postmortems, Gated to Pro

incident.io's AI story centers on Scribe, which drafts an incident timeline and a postmortem automatically from the Slack or Microsoft Teams conversation as it happens, turning what's usually an hour of someone reconstructing events after the fact into a draft a human edits instead of writes from scratch. That's a documentation and coordination play, not a production-action one; nothing in incident.io's AI feature set claims to touch infrastructure directly.

The catch worth knowing before you shortlist it: AI is not included on the Free or Team tiers. Scribe and AI-native postmortems only ship starting at Pro, which changes the real per-seat cost of "the AI version" of incident.io relative to its cheaper on-call-only plans.

Pricing: Free (Basic, no AI); Team $15-19/user/month (no AI); Pro $25/user/month (Scribe and AI-native postmortems included); Enterprise custom. Source: incident.io/pricing.

Best for: Teams that want AI-assisted incident documentation without adding a new vendor, and are already planning to buy the Pro tier anyway.

11. FireHydrant: Coordination and Documentation, With AI Gated to Enterprise

FireHydrant is a responder-based incident management platform (you pay per person actively working incidents, not per seat), and its AI features, summaries, meeting transcripts, automated triage notes, and retro drafts, are aimed squarely at the coordination and documentation side of an incident rather than production action. There's no claim anywhere in FireHydrant's materials that its AI executes a fix; it's built to make the record of what happened faster to produce, not to change what's running.

The practical issue is availability: FireHydrant AI is gated entirely to the custom-priced Enterprise tier, so a team evaluating it on the public Pro plan won't actually see the AI features in action without a sales conversation first.

Pricing: Free tier available; Pro $25/responder/month billed annually (no AI); Enterprise custom (AI included). Source: firehydrant.com/pricing.

Best for: Enterprise teams that want incident documentation and coordination automated, and don't need the agent to touch production.

12. Harness SRE Agent: CI/CD Failure Diagnosis Across the Whole Delivery Pipeline

Harness's SRE Agent is one node in a broader network of specialized agents (SRE, AppSec, Test, FinOps, and more) built into a platform that already covers CI, CD, and feature flags, which makes it the strongest fit on this list for a team whose incidents are as likely to trace back to a bad deploy as to a live production event. In Harness's own worked examples, the SRE Agent detects an anomaly, identifies something like a noisy neighbor, drains and cordons the affected node, verifies stability, and posts a post-mortem to Slack.

Harness's own roadmap language is worth reading literally here: it places itself at "Horizon 1," agents as sidecars with human approval today, moving toward more autonomous "human-on-the-loop" and eventually "autonomous SRE" operation over the next several years. Treat the node-draining example as what the agent can do inside that approval flow now, not as unattended production authority.

Pricing: Not published; AI SRE is listed as a separate Enterprise module. Every tier requires a sales conversation. Source: harness.io/pricing.

Best for: Engineering orgs whose incidents span the whole delivery pipeline, not just production, and who already run CI/CD in Harness or are evaluating it.

13. Cast AI: Continuous Kubernetes Cost and Capacity Optimization

Cast AI is a different shape of agent than the rest of this list: it isn't triggered by an incident, it runs continuously in the background, rightsizing pod CPU and memory requests, scaling nodes, improving bin packing, and shifting workloads to spot instances as pricing and availability change, predicting spot interruptions up to 30 minutes ahead and migrating gracefully before they hit. That makes its risk profile genuinely different from an agent that acts during an active outage: the blast radius is ongoing background optimization, not a single high-stakes decision under time pressure.

New accounts start in read-only mode with no infrastructure changes until a team turns automation on, and higher-risk actions can run through approval workflows rather than executing immediately, which is the right default for a tool making changes to running infrastructure every day rather than only during an incident.

Pricing: Not published. Usage-based and specific to your environment; contact sales. Source: cast.ai/pricing.

Best for: Kubernetes-heavy teams that want continuous cost and capacity optimization running in the background, separate from incident response.

14. Firefly: Infrastructure-as-Code Drift and Compliance Remediation

Firefly's job is catching the gap between what your Infrastructure-as-Code says should be running and what's actually running in AWS, Azure, Google Cloud, or OCI, then closing it. It detects drift and misconfiguration continuously, checks changes against frameworks like SOC 2, PCI DSS, HIPAA, and ISO 27001, and alerts through Slack or PagerDuty the moment something drifts. When it finds a fix, it generates the remediation code and opens a pull request rather than applying the change directly, which keeps a human review step in front of every infrastructure change by default.

That PR-based model is a meaningfully different blast radius than the incident-response agents above: Firefly's worst case is a bad PR sitting unmerged, not a bad change already live in production, which is a deliberate and reasonable trade-off for a compliance-adjacent tool.

Pricing: Essential $2,499/month billed annually, up to 20,000 assets; Enterprise custom. Source: firefly.ai/pricing.

Best for: Platform and security teams that need IaC drift and compliance violations remediated on a reviewable pull request, not applied live.

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How to Choose: Decision Framework

Choose by the incident job, environment context, write authority, approval design, and evidence from a contained pilot.

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DevOps AI Agent Buying Mistakes to Avoid

The dangerous mistakes are shallow context, oversized permissions, an unbounded first pilot, and accepting vendor speed claims without your own baseline.

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What to Do Next

Don't start by picking a vendor. Start by writing down the blast radius you're actually comfortable with today, in one sentence, before a single demo call. "It can page the right person and draft a timeline" is a different purchase than "it can revert last night's deploy at 3am without waking anyone up," and the 14 products above span that entire range. Pilot one against real incidents for 4-6 weeks, measure your own before/after MTTR instead of trusting a vendor's number, and only widen what it's allowed to touch once you've watched it be right for a while, not just once.

If you haven't settled on the broader agent platform underneath this decision yet, best AI agent platforms in 2026 is the pillar guide for choosing that layer first.