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Technology

AI SRE: The Next Critical Application of AI in Software Engineering

10/03/2025
10 min read
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Generative AI has already transformed the way we develop software. Code generation tools accelerate development, shorten feedback loops, and remove friction from everyday tasks. Companies like Robinhood, JPMorgan Chase, Walmart, Microsoft, Coinbase, and Google have all gone on public record, citing broad adoption of agents in code development and review.

However, the truth is that coding was never the bottleneck. It represents just 30 percent of engineering time. The harder 70 percent is running that code in production, where complexity, tool silos, knowledge gaps, and the pace of change all collide. You can code faster, but engineering velocity is not improving because teams still spend the majority of their time fighting production issues.

IDC analysis shows developers dedicate far more hours to operational and background work than to writing code, with some studies finding that only about 16 percent of time is spent directly coding¹. The world’s most expensive engineering talent is spending most of its time firefighting, triaging incidents, and wrestling with workflows designed for a different era.

This is the productivity paradox. Code gets faster, production gets harder. Without solving the 70 percent problem, gains from investments in code generation barely make a dent.

Production environments are the real bottleneck

Today’s production environments are sprawling and noisy. Cloud-native architectures, containerized workloads, and Kubernetes orchestration have created more telemetry, more dependencies, and more moving parts than ever. When something breaks, engineers are pulled into a series of cascading war rooms. It becomes a situation where multiple teams are engaged, with experts of specific components of the production system. They bounce between dashboards, logging systems, incident workflows, chat tools, and static runbooks, each with its own query language, data format, and context.

Additionally, production systems are rarely greenfield. They are the product of years of layered builds, legacy migrations, and shifting deployment models. Enterprises typically run a patchwork of on-prem, private cloud, multi-cloud, and SaaS services, each with its own failure modes, operational quirks, and layers of dependencies. This accumulated complexity makes downtime harder to prevent, degradations harder to detect, and remediation slower.

The result is not only costly outages but also frequent downtime and degraded performance. These are far more common, and while less visible to customers, they drain developer productivity. Every time engineers are pulled into a war room, roadmap work stalls, context switching rises, and incident fatigue sets in. What feels like “just a few hours” of degraded service quickly adds up to thousands of lost developer hours each year.

The business cost of this downtime is enormous. Oxford Economics estimates that downtime and service degradation cost the Global 2000 about $400 billion annually². Other analyses suggest the price of downtime for large organizations can reach $9,000 per minute³. For global enterprises, every wasted second translates into lost revenue, broken trust, and missed opportunities.

Why legacy approaches cannot solve reliability

Organizations have been employing automation to address these problems for years. Site Reliability Engineering codified best practices. Pipelines made deployments faster. APIs made integration easier. Dashboards made telemetry visible.

But all of this shares the same limitation: it scales data and costs, not understanding. Runbooks automate known steps but fail in novel situations. Observability tools surface metrics but still place the cognitive load on engineers to decide what matters. Traditional workflow tools escalate issues but do not solve for the root cause.

The outcome: more alerts, more dashboards, more logs, and more manual decisions. Ultimately, the promise of legacy automation failed to deliver, instead amplifying toil rather than eliminating it.

Why an AI SRE is flipping the script on software engineering

AI has already proven its value in software engineering. The 2025 Stack Overflow Developer Survey found that 84 percent of developers are using or plan to use AI tools, up from 76 percent the year before⁴. Adoption is widespread, but trust is uneven. Engineers will not hand over production operations to AI unless it is transparent, reliable, and grounded in real systems.

AI SRE changes the equation. Purpose-built AI SRE systems use large language models and multi-agent intelligence to correlate code, infrastructure, and telemetry across logs, metrics, traces, past incidents, and their own memories. Instead of forcing engineers to query tools manually, AI SRE generates real-time narratives of what is happening, pinpoints likely root causes with supporting evidence, and recommends prescriptive remediation steps.

This relieves the heaviest burden on engineers: figuring out what went wrong, why it broke, and how to fix it. AI SRE does not replace engineers. It gives them the same leverage AI brought to coding, but applied to the complexity of production systems. Instead of scaling data, it scales understanding at machine scale.

Why an AI SRE needs to be part of your AI strategy now

Several converging forces make AI SRE urgent today, not a year from now:

  • Downtime and outages are expensive. Uptime Institute’s 2025 analysis found that 54 percent of operators reported their most recent significant outage exceeded $100,000, and 20 percent cost over $1 million, up from the previous year⁵. Those figures only capture the most visible events. Oxford Economics estimates that day-to-day downtime and degradation cost the Global 2000 around $400 billion annually².
  • War rooms drain developer productivity. Frequent degradations and performance slowdowns pull senior engineers into incident response cycles. Roadmaps stall, context switching rises, and on-call fatigue grows. The productivity cost is as damaging as the financial cost.
  • Engineering time is scarce. With only about 16 percent of developer time spent coding¹, the real bottleneck is reliability in production, not productivity in coding.
  • Manual steps slow recovery. Each incident demands repetitive triage, log searches, and write-ups, often adding hours of collective engineering effort. AI SRE reduces the mean time to resolution by automating much of this work and reducing the manual steps required per incident.
  • AI adoption is already mainstream. With 84 percent of developers using or planning to use AI tools⁴, the cultural shift has happened. The only question is where it drives the most leverage.
  • Gartner confirms the ceiling of code assistants. Their research shows most developers report productivity gains of 10 percent or less from AI code assistants. By 2028, however, teams that strategically apply AI across the full SDLC will achieve productivity gains of 25 to 30 percent, nearly triple the impact of code-focused tools⁸.
  • Agents are the next wave. Gartner also projects software engineering agents will improve team productivity by 30 to 50 percent by 2028, surpassing the modest 0 to 20 percent gains from today’s assistants⁹. These agents plan and execute multistep workflows, maintain context, and orchestrate across CI/CD, runtime, and observability systems. AI SRE is this vision realized in the hardest domain of all: production.

The first wave of AI delivered coding assistants. The second wave is delivering AI SRE: from scaling code to scaling reliability.

The Resolve AI view

At Resolve AI, we believe AI SRE is not about chatting with logs, metrics, or dashboards. It involves embedding intelligent agents into the core of production workflows, which requires both deep domain and AI expertise to approach the problem holistically.

An AI SRE must be built with these core capabilities:

  • Knowledge, to maintain a real-time understanding of your systems, code, dependencies, and incident history.
  • Reasoning, to form and test hypotheses, adapt plans as new evidence emerges, and rank possible causes by confidence.
  • Action, today, be able to execute safe workflows such as generating remediation plans, creating PRs, or scripts.
  • Learning and improvement, to refine investigations and remediation patterns over time, based on your environment, outcomes, and direct feedback.
  • Collaboration, to work transparently with your engineers, showing its reasoning so your team can redirect, validate, or extend investigations without starting over.

Because it captures and codifies knowledge across systems, AI SRE also shortens onboarding time for new engineers, reduces the ad-hoc ‘shoulder taps’ that consume peacetime hours, and automates large parts of postmortem creation. This means faster ramp, fewer interruptions, and less fatigue for teams already stretched thin.

AI SRE is not an experiment. It is already running in production at some of the world's largest organizations, delivering measurable improvements in mean time to resolution, reducing downtime costs, and empowering engineers to run their production systems more efficiently with complete system context at their disposal.

Closing the Software Engineering Loop with AI SRE in Production

Code generation solved the easy part. The hard part is running software reliably in production, where downtime, degradations, and outages cost millions; incident fatigue is rising, and engineers are overwhelmed by war rooms and workflows.

AI SRE is how the world’s largest organizations reclaim engineering time, improve resilience, and turn site reliability engineering into a competitive advantage. For leaders, the value is measurable: fewer teams pulled into incidents, fewer people required to respond, shorter MTTR, and reduced downtime costs. These are the levers that determine whether engineering velocity improves or stalls.

The question is no longer whether you need an AI SRE, but whether you will build or buy. That is where your evaluation must begin.

We cover both here:

  • AI SRE: The Build vs. Buy Debate
  • How to Evaluate an AI SRE



References

  1. IDC via InfoWorld, Developers spend just 16% of their time writing code, April 2024. Link
  2. Oxford Economics, The hidden costs of downtime: The $400B problem facing the Global 2000, 2024. Link
  3. Forbes Tech Council, The true cost of downtime and how to avoid it, April 2024. Link
  4. Stack Overflow, 2025 Developer Survey, June 2025. Link and Blog summary
  5. Uptime Institute, Annual Outage Analysis 2025, July 2025. Link
  6. ArXiv, How much does AI impact development speed? An enterprise-based randomized controlled trial, October 2024. Link
  7. McKinsey, Unleashing developer productivity with generative AI, 2024. Link
  8. Gartner, How to Capture AI-Driven Productivity Gains Across the SDLC, April 2025 (ID G00827469).
  9. Gartner, Innovation Insight for AI Software Engineering Agents, September 2025 (ID G00830388).
Ben Jaderstrom's avatar

Ben Jaderstrom

VP of Worldwide Sales

@ Resolve AI

I’ve spent the last decade helping build and scale high-growth software companies. At Grafana, I was part of a journey that grew the business 40× and helped redefine modern observability. Most recently, at Windsurf, I led GTM efforts as we merged missions with Cognition, the team behind Devin. Now at Resolve AI, I’m focused on building a world-class GTM organization to help the world’s most strategic customers ship reliable software in the AI-native era.

Manveer Sahota's avatar

Manveer Sahota

Product Marketing

    content title iconContent
  • Production environments are the real bottleneck
  • Why legacy approaches cannot solve reliability
  • Why an AI SRE is flipping the script on software engineering
  • Why an AI SRE needs to be part of your AI strategy now
  • The Resolve AI view
  • Closing the Software Engineering Loop with AI SRE in Production
  • References
Ben Jaderstrom's avatar

Ben Jaderstrom

VP of Worldwide Sales

@ Resolve AI

I’ve spent the last decade helping build and scale high-growth software companies. At Grafana, I was part of a journey that grew the business 40× and helped redefine modern observability. Most recently, at Windsurf, I led GTM efforts as we merged missions with Cognition, the team behind Devin. Now at Resolve AI, I’m focused on building a world-class GTM organization to help the world’s most strategic customers ship reliable software in the AI-native era.

Manveer Sahota's avatar

Manveer Sahota

Product Marketing

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