Kahuna Labs Blog

From Troubleshooter to Orchestrator

By John Ragsdale, SVP of Marketing, Kahuna Labs

In many enterprise technology companies today, support engineers remain caught in a reactive loop: the alarms go off, tickets pile up, issues are escalated, patches get built, and everyone breathes a sigh of relief—until the next incident.

But what if that role could evolve? What if instead of fixing the problem, support engineers became the orchestration engine that stops major problems from ever surfacing, aligns diagnostics and troubleshooting into optimized flows, and elevates the leverage of the team across the entire customer lifecycle?

In this article I’ll explore why we’re at an inflection point, what’s holding support engineers back, and how you should start thinking about the “orchestrator” role today—so that you’re ready to realize not just incremental improvements, but a big productivity leap.

The Inflection Point

The pace and complexity of modern enterprise products has exploded. Not only are today’s products incredibly complex, but heavy configuration means each customer’s environment is different. Often vastly different.

Support engineering teams remain large cost centers—with engineers spending up to ~70% of time researching, recreating, and diagnosing rather than innovating or driving value.

Meanwhile, forward-looking support ororganizations are being asked: Can you prevent issues? Can you accelerate time to resolution? Can you become strategic partners instead of fire-fighters?

Agentic AI platforms are now viable for real support engineering rather than just ticket routing or simple chatbots. This sets the stage for transforming the role of the support engineer. Not by replacing them, but by redefining them.

The Traditional Support Engineer Role: Ripe for Transformation

The core support approach with multiple tiers of engineers, each level with more experience, has been around for decades. And while the basic structure works, in an AI world, there is a lot of room for transformation.

What works:

• Deep product/technical knowledge
• Ability to diagnose and troubleshoot under pressure
• Institutional expertise and years of experience
• Strong customer-facing skills when things go awry

What’s broken:

• Much of the “tribal” knowledge lives in heads or ad-hoc tickets; not easily reused or democratized.
• Engineers spend too much time on researching and recreating issues rather than higher-leverage work (e.g., architecture, root causes, proactive improvements).
• Systems are reactive: a ticket arrives, perform triage, try a number of things to address the issue, ultimately close the ticket, then repeat with the next ticket. Little orchestration, many silos and   approaches.
• Training and ramp time are long; turnover is high; knowledge transfer is weak.
• Scaling is hard: when volume increases, simply adding more engineers yields diminishing returns.

The Orchestration Model

An orchestration-centric support engineering model features the following:

1. Pre-ticket diagnostics aligned to journey – The path a customer takes, the dependencies and configuration, telemetry, version, product build all feed into a Troubleshooting Map™, with paths for every conceivable twist and turn of the journey.
2. Dynamic decision flows – Instead of a static diagnostics checklist, you have decision flows that evolve based on previous ticket outcomes and customer context.
3. Knowledge reuse and routing leverage –Tribal knowledge is captured and surfaced dynamically to the right engineer at the right time, and constantly improved as new issues and resolutions emerge.
4. Escalation avoidance / resolution avoidance mindset – Rather than just fixing a problem, the AI enables an engineer to orchestrate prevention, mitigation, learning loops, and continuous improvement.
5. Engineered force-multiplier – The support engineer acts as the conductor of the diagnostics and resolution engine, rather than doing every step manually. The AI enables the system and team to run more efficiently by having SEs handle exceptions, not every step, which are predicted and automated.
6. Metrics shift – From tickets closed per engineer and time to respond and resolve, metrics can shift to more experiential data, such as auto-resolved tickets, number of steps and iterations streamlined or eliminated, with proven correlations to customer satisfaction, customer effort, and ultimately having a strategic impact on annual recurring revenue (ARR).

The Time to Act is Now: Getting Beyond Barriers

In a subscription economy, every interaction with a frontline employee has an impact on long-term account value. And no one has more post-sales interactions with customers than support. B2B support organizations are faced with mounting pressure:

• Customers demand faster, more proactive support with less downtime and less cost.
• Engineering teams are being held accountable not just for features shipped but also for post-release support burden.
• Emerging AI platforms are becoming mature enough to power orchestration and enable complex problem diagnostics, so the focus of AI can move beyond deflection and automating Level 1 issues.
• Competition in the technical product arena means support is a differentiator, not just a cost center.

But there remain a lot of barriers to overcome:

• Data fragmentation: Support history, configuration data, telemetry, and documentation are siloed, fragmented, and incomplete.
• Tribal knowledge: A lot of learning lives in engineers’ heads and is not accessible, or fragmented across multiple systems and difficult to correlate into meaningful or actionable content.
• Risk aversion: The idea of automating diagnostics or dynamic flows still triggers caution around “what if the AI is wrong?” The focus needs to be on how to allow human intervention when needed, and letting AI handle known issues/flows on its own.
• Investment mindset: Many execs still view support as a cost center, and only envision incremental improvements rather than strategic transformation. Support’s role in renewals and ARR is not front of mind for many companies.

Actionable Steps for Support Leaders Today

1. Stop obsession about deflection. Pacesetter companies have made great progress in improving self-service success and deflection. But they are blind to emerging AI approaches that tackle the real expense of support: the complex tickets that may take days, weeks, or even months to resolve.
2. Map your current “time-to-first-action” metrics — Identify bottlenecks: How long do engineers spend just getting context? How many tickets escalate because of the time required to manually understand, research, and recreate issues?
3. Surface your diagnostic flows and decision trees — Look for AI that can capture the repeatable paths your engineers take, understand the forward-looking journey for each approach, and enforce standardization for paths with the highest confidence and shortest resolution time.
4. Systematically capture Tribal Knowledge — Asking SMEs to write more documents is a waste of time. There are too many variables in each decision point to create an all-encompassing knowledge article. Rely on AI to create a Troubleshooting Map that dynamically pivots according to unique customer configurations and handling instructions, and product versions.
5. Do some internal marketing — Support offers immense strategic value for the organization. They have extensive data on product features that are poorly designed and hard to use. They know typical friction points new customers experience that could be addressed in customer success onboarding to boost adoption. Customer experience with support has a big impact on renewals and upsells. If your company execs don’t realize this, start educating them about support’s strategic value.

Conclusion

The traditional model of support engineering—where more engineers equal more throughput—is increasingly unaffordable and unsustainable in complex product environments. The future belongs to teams that think in terms of orchestration, engineer-enablement, and manual intervention only for exception handling.

Your Support Engineers can become the linchpins of product adoption and customer success rather than the reactive fire-fighters they’re forced to be today. Start the transformation now, and you’ll be ahead of the curve when the next wave of product complexity hits. And I promise you it is coming soon.