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In today’s cloud-native environments, a single customer support ticket can be the first signal of a deeper system issue. Support engineers often notice patterns in user reports—degraded performance, error messages—that point to outages or bugs. Likewise, SREs managing incidents must keep customer-facing teams in the loop so users aren’t left in the dark. Atlassian emphasizes the importance of seamless collaboration: “Ensure all stakeholders — engineering, product, and customer support — are aligned” during incidents.
The need for coordination is intensified by scale and complexity. Distributed microservices, global user bases, and 24/7 operations mean incidents ripple across multiple fronts. Poorly handled downtime isn’t just an engineering firefight; it’s a customer service crisis. Studies show that 93% of enterprises report downtime costs exceeding $300,000 per hour, while nearly half say it tops $1 million per hour. This is why many organizations now involve support in incident war rooms and treat customer communication as a pivotal part of incident response. Reliability today is a team sport, with support and SRE each holding essential pieces of the puzzle.
Despite the clear need to work together, support and SRE functions are still divided by fragmented tooling and data silos. Support teams work in ticketing systems like Zendesk or Freshdesk, while SREs operate in dashboards, alert consoles, and log aggregators.
When issues escalate, the handoff often happens via Slack—support engineers paste ticket details or metrics, and on-call engineers begin probing: What exactly did the user experience? Which services are affected? How widespread is the issue?
Because context doesn't travel automatically across tools, critical information fails to get surfaced:
The result? It’s not just friction—it means longer downtime and slower resolution when every minute matters.
By contrast, on-call shift handovers usually follow a structured protocol: Incident channels are documented with the latest status and next steps., dashboards are reviewed, alerts are triaged, and ownership transfers clearly, ensuring no information is lost. Most teams follow what’s called a runbook, step-by-step operational procedures for diagnosing and resolving recurring issues or known failure scenarios.
According to Google’s SRE best practices, though, this level of clarity often doesn’t apply to tickets. “Very few teams do the same handoff for tickets as they do for on-call shifts,” meaning **recurring issues frequently go uninvestigated or unresolved.**
Without such mechanisms, incidents bounce between teams or sit idle in queues. The result: parts of every incident slip through the cracks, burnout increases, and teams end up fighting the same fires repeatedly.
To meet the demands of always-on services, organizations are recognizing the need for integrated workflows that align support and reliability as two halves of a whole. That means a support ticket should seamlessly feed into an SRE investigation, and SRE findings should loop back to inform customer communication.
When that communication is clear, customers are reassured that the issue is understood and being addressed, which reduces frustration and prevents a flood of duplicate tickets. Internally, support teams gain confidence in what to tell users, while engineers can focus on remediation instead of fielding status updates. This alignment shortens incident lifecycles, reduces mean time to resolution, and strengthens trust on both sides of the system.
Key practices include:
Bridging support and SRE is now possible through AI-powered agents. Traditional tools keep customer and system signals in separate silos — tickets in Zendesk, metrics and logs in monitoring platforms. Even when integrated, they only pass data back and forth without context.
AI agents can change this by ingesting telemetry, logs, tickets, and documentation together — then reasoning across them in real time. That means customer complaints and system alerts can be correlated instantly, without waiting for manual summaries or escalation handoffs.
We were surprised at first when customers began asking us to extend our AI Support Engineer into their observability stack. But once we thought about it, it made perfect sense. If the Support Engineer is already trusted to interpret customer reports with precision, then pairing it with an AI SRE that reasons over logs, metrics, and telemetry is the obvious evolution. Together, they close the loop between what customers experience and what systems reveal.
Together, these counterparts act as complementary agents:
Instead of a support engineer copying ticket details into Slack for an engineer to decipher, the AI can directly correlate user reports with logs, propose likely causes, and even kick off runbook steps.
These agents don’t replace humans — they reduce toil, accelerate investigation, and keep customer communication in sync with technical resolution. The outcome: faster fixes, fewer repeat issues, and more resilient systems.
And that connects directly to one of the most debated metrics in reliability: Mean Time to Resolution (MTTR). MTTR accepts that complex systems will fail — the question is how quickly you can detect, diagnose, and recover. Preparedness is everything: the better equipped you are to know a problem exists, understand its scope, and act with confidence, the faster you can resolve it — and the less disruption customers feel.
AI agents strengthen this preparedness loop. By learning from every incident, correlating signals across support and SRE, and surfacing transparent, evidence-backed reasoning, they shrink MTTR while also helping teams prevent the next outage. They also help measure what’s often hardest to assess in the moment — the blast radius of an issue, or which customers and services are truly affected. In other words, they don’t just minimize the cost of downtime, they turn every incident into a way to improve resilience.
That’s why the future of reliability won’t be about support or SRE in isolation. It will be about an integrated model where customer problems and system problems are treated as one, with AI as the connective tissue.
Bridging the gap between support and SRE isn’t a groundbreaking idea — it’s the way reliability should have worked all along.
However, the overhead of connecting customer-facing signals with back-end investigations was simply too high, so teams understandably operated in more manageable silos. That meant wasted effort, slower resolutions, and missed opportunities to prevent the next incident.
Now, with better practices and AI agents lowering the cost of coordination, what was once impractical is finally achievable. And solutions can be comprehensive, nuanced and near-real-time. Teams can align workflows, run joint postmortems, and continuously improve runbooks without drowning in process.
For engineering leaders, the takeaway is clear: treat support and SRE as partners in resilience. Every support ticket can be a reliability signal, and every incident can be an opportunity to strengthen customer trust.
© RunLLM, Inc. 2025
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