How to Solve “Cannot Reproduce” Bugs That Cost Support Teams Hours

Support teams frequently face vague customer reports and incomplete data but need to offer fast resolutions autonomously without escalating to developers. In this article, learn how to equip support engineers with tools to diagnose root causes in minutes, increasing self-sufficient issue resolution. We explore eliminating the ‘Reproduction Tax’ for ‘cannot reproduce’ bugs using runtime context to achieve technical certainty at scale.

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In Support Engineering, cannot reproduce is the most expensive phrase we can hear. It means the team has exhausted costly attempts to replicate an issue, yet the issue remains unresolved.

Studies suggest that 17% of tickets are closed after being marked “cannot reproduce.” When a ticket is closed as CNR, the customer stays frustrated, the Support Engineer loses credibility, and the developer’s focus is shattered by the search for an issue that cannot be found.

Beyond the immediate friction, an unresolvable bug creates a hidden liability: it remains in production, increasing the blast radius of potential failures as more users encounter the same edge case.

As software development accelerates with AI agents – and instability climbs by nearly 10% – the way we investigate incidents must evolve.

Organizations that eliminate the Reproduction Tax (the wasted engineering capacity spent trying to mimic production behavior in client environments) can reduce incident resolution from hours to minutes, protecting developer focus and scaling investigations across the entire team.

Why is incident resolution still slow and expensive?

Bugs discovered in production are up to 600% more expensive to resolve than those found during development. Traditional data-gathering methods are fundamentally broken, leading to several key operational bottlenecks that we face as team leads:

• Environmental Mismatch: Production failures often depend on specific combinations of configuration, data state, and traffic patterns that cannot be mirrored in staging. This gap extends downtime, negatively impacting the customer experience and bleeding company revenue.

• Context gaps: Engineering teams lack the critical runtime context required to accurately diagnose a root cause. This forces support into a cycle of reactive firefighting, wasting engineering time on guesswork instead of innovation.
• AI agent limitations: While many teams now use AI agents for incident resolution, these face the same barrier: without runtime data, the AI’s effectiveness depends entirely on how well a human can guess the correct context in a prompt, moving the bottleneck from the debugger to the prompt engineer.
• Manual Data Collection: Legacy debugging requires new deployments just to collect diagnostic data. Every log-and-redeploy cycle inflates observability costs with limited ROI for the business.
• Developer dependency: Support teams are frequently paralyzed for days waiting on developer bandwidth to investigate tickets. This operational bottleneck strains client relationships and increases the likelihood of churn, while forcing developers to prioritize tedious reproduction cycles over high-value feature development.

Staging is not production

The biggest barrier to resolution is the visibility gap: modern distributed systems are now too complex to perfectly replicate in a local or staging environment. We can neither see how systems behave in production in real time, nor can we replicate the exact conditions that trigger these failures.

This leads to a phenomenon where we have to investigate an issue that only manifests under a specific, live combination of account configuration, database state, and network latency, without those same conditions. Support teams are forced to investigate production systems with incomplete evidence, relying on static historic logs rather than the runtime context needed to explain what happened.

The 8-hour investigation: A best-case scenario

Even a simple issue, like a file failing to upload, can easily consume a full engineering day. For those of us leading these teams, the traditional workflow represents a massive drain on our total capacity. 

• Report: A support engineer receives a report from a customer describing a failure.
• Context collection: We follow up to collect essential diagnostic information (e.g., file type, widespread vs local).
• Hypothesis testing: We try to eliminate potential causes by guessing at environmental variables (e.g., “does it only crash when the list exceeds 100 rows?”).
• Escalation: We escalate to engineering to check logs. If data was not captured, the trail goes cold. 
• Redeploy: We have to redeploy code changes just to see if the root cause can be captured. 
• Repeat: If the cause of the incident was not found, the cycle repeats.

The solution: AI-driven reasoning and Runtime context

To remove this lengthy process, we must move toward autonomous issue resolution. AI site reliability engineering tools (AI SREs) have been created for this purpose and they require two core capabilities:

• AI-driven reasoning: Modern enterprise systems span multiple observability vendors, cloud tools, APIs, databases, and legacy infrastructure, and the tools must be able to analyze and correlate this data to produce rapid diagnosis.
• Runtime Context: This is our source of truth. Instead of trying to recreate a failure, teams can capture the complete failure state of the system where the issue occurred. This ensures that investigations are not limited by the captured evidence and eliminates reproduction cycles and diagnostic code changes.

We built Lightrun AI SRE on these two principles, because their combination allows support teams to understand issues, find and test mitigation suggestions and resolve many incidents without escalation to developers.

Introducing Lightrun AI SRE: Reliability across the SDLC

In an AI-accelerated engineering environment, reliability cannot start after the incident. It must extend across the entire software development lifecycle. Engineers should be able to ask questions about their systems as they work, and support teams should be able to query behavior the moment it is flagged.

Lightrun AI SRE transforms how teams investigate by acting as a unified analysis layer that connects directly to your existing observability tools.

Since AI cannot resolve what it cannot see, Lightrun AI SRE observes the system as it actually runs, safely instrumenting running systems and correlating context from multiple perspectives, unifying logs, metrics, traces, infrastructure signals, and change history.

By grounding this analysis in live execution state, Lightrun provides root cause analysis with a level of confidence that static code or disconnected logs simply cannot match.

This fundamentally changes how we can ensure reliability for our customers:

• Instant system understanding: Lightrun AI SRE explains how the system works, answers behavior questions, and clarifies configuration and architecture. This translates into fewer escalations, faster MTTR, and smoother customer onboarding.
• Failure classification: It enables faster triage and reduces the false escalations that clutter developer backlogs by distinguishing real bugs from setup or environment issues. 
• Intelligent incident routing: By identifying whether an issue is application-level, infrastructure-related, or dependency-driven, it routes the incident to the relevant team immediately. This eliminates the “ping-pong” between departments and ensures clear ownership.
• Strengthen resilience: It provides tested, actionable remediation suggestions and improves long-term resilience with automated postmortems that ensure that the resolution of one ticket can prevent the next ten.

Case study: The bug that was actually a hard limit

In our own work here at Lightrun, we test out our own product whenever we face engineering issues. I like to approach root cause analysis like a detective, collecting all the evidence I can from logs, telemetry, code, and recent changes.

Recently, we had an interesting use case with a client. The issue turned out to be simple, but finding it was a real challenge. One of our APIs was returning an unexpectedly small amount of records to one of our customers, and they could not see all their entities in the plugin interface.

Initially, we were stuck. The API appeared functional and the database returned results, but something was clearly wrong. When we tried to reproduce it locally, everything worked perfectly. It turned out our local test data didn’t hit the specific volume thresholds present in production, allowing the bug to remain hidden.

We were building Lightrun AI SRE so we tested it out. It was only when we dived into the client’s production environment that the truth came out. Collecting live runtime context, the AI agent placed a snapshot right after the database query but before the return to the user. Without escalating to a developer, it set an expression to compare the rows returned from the database vs. those from the API, and the live context revealed the discrepancy instantly: 

The client was calling 100 records from the database, but the API could only return 20 due to a wrong REST controller configuration.

It wasn’t a ghost; it was a hard-limit annotation in the code.

While the root cause was visible in the code once we knew where to look, the “killer feature” was being able to get the data to prove it without a single reproduction attempt. Having the AI SRE agent dynamically collect this evidence from the actual environment satisfying gave us the technical certainty to implement a long-term solution, using search and pagination, in minutes. Impressively we could do all of this without a single redeploy.

The new support investigation workflow

Using Lightrun AI SRE we’ve adopted an automated five-step workflow that lets our team work without the Reproduction Tax:

1. Triage automatically: Lightrun AI SRE connects reports to live system signals directly within tools like Slack or PagerDuty to determine if a behavior is a user error or a bug.
2. Assess impact: It identifies failing services and the exact percentage of users impacted in real-time.
3. Eliminate unknowns: The AI agent dynamically tests hypotheses by correlating runtime snapshots and environment variables to rule out common culprits like configuration drifts or local data mismatches.
4. Prove root cause: It investigates directly in production systems to capture live variable values without manually checking code.
5. Convert to knowledge: We then take all the investigation details, automatically update the initial Jira ticket to explain the event to engineers alongside technical proof like logs and snapshots.
6. Propose and validate fixes: We can then suggest product changes to prevent recurrence, and using the Lightrun MCP the AI code agent engineers can generate and validate the changes, before updating the customer of a successful resolution.

Switch to 4-minute incident resolution

Our goal isn’t just to close tickets faster, it’s to end the “Ping-Pong” between departments that wastes our engineering bandwidth. By removing the Reproduction Tax, enterprise teams at organizations like Taboola and AT&T have achieved a 90% reduction in MTTR.

When your team has access to live production evidence, you move beyond the limits of reactive triage. You stop simply routing tickets and start delivering the technical certainty required to resolve incidents in minutes, not days.