Comparing AI approaches for production debugging

Debugging is the process of identifying, isolating, and fixing defects in software. At its core, debugging is hypothesis testing. You observe unexpected behavior, form a theory about its cause, gather evidence to test that theory, and iterate until you converge on the root cause. This loop hasn't changed in decades, but what has changed is the complexity of the systems being debugged and the tools available for the investigation.

Fun fact: The term “debugging” traces back to the 1940s, when an actual moth caused a malfunction in the Harvard Mark II computer. But the practice predates the name: any time a program behaves differently than intended, someone has to figure out why.

How debugging evolved in the last 2 decades

Client-server and multi-tier (1990s-2010s)

As systems split across machines, debugging required coordinating information from multiple sources. A bug in a web application might involve the browser, the application server, the database, and the network between them. Engineers learned to correlate logs from different components, trace requests across system boundaries, and think about timing and concurrency.

Tools like log aggregation systems, application performance monitoring (APM), network packet capture, and more such observability tools evolved to match the increasing complexity of systems. But each tool showed one slice of the system. Engineers are required to stitch those slices together mentally.

Distributed and cloud-native (2010s-present)

In today’s production environments, a single user request might touch dozens of microservices running across hundreds of containers that could be rescheduled to different nodes at any moment. Infrastructure is ephemeral. The container that experienced the bug might not exist by the time you start investigating.

This created several debugging challenges that tools have struggled to address:

• Context is fragmented. Logs are in one tool, metrics in another, traces in a third, deployment history in a fourth, code in a fifth. Each tool provides partial visibility. Correlating them requires switching between tools, translating between data models, and holding the full picture in your head.
• Expertise is siloed. Understanding the full path of a failure often requires knowledge that spans application code, infrastructure configuration, database behavior, and network topology. Engineers specialize in their domains and organizations evolve to separate ownership of systems.
• State is transient. There's no single process to attach a debugger to. The relevant state is scattered across services, databases, caches, message queues, and infrastructure components. Much of it exists only briefly.
• Causality is hard to trace. An error in Service A might be caused by a configuration change in Service B that affected how Service C handles timeouts, which cascaded into failures that only manifest in Service A. Following this chain requires understanding dependencies that aren't always documented.

What are the limitations of traditional debugging approaches?

Most debugging in production today follows one of a few patterns:

Log searching involves querying centralized logs for error messages, stack traces, or suspicious patterns around the time of the incident. This works when the relevant information was logged and when you know what to search for. It fails when the root cause is in a component that logs sparingly or when the symptom appears far from the cause.

Metrics correlation uses dashboards to identify metrics that changed around the incident time. A spike in latency, a drop in throughput, a jump in error rate can point toward the affected component. But metrics show what happened, not why. A CPU spike doesn't tell you which code path caused it.

Distributed tracing follows individual requests across service boundaries, showing the sequence of calls and where time was spent. This is powerful for latency investigation but limited for errors that don't produce traces or for issues caused by interactions between requests rather than within a single request.

Code inspection examines recent changes to identify potential causes. This works well for bugs introduced by new deployments but misses issues caused by environmental changes, data patterns, or interactions between components that haven't changed recently.

Each approach provides valuable signal. But in complex incidents, the root cause often lies in the connections between what each tool shows. The deployment that introduced the bug, the infrastructure state that triggered it, the telemetry that captured its effects—understanding the incident requires synthesizing all of these.

3 approaches to use AI for debugging production

Different AI systems address production debugging in fundamentally different ways. Understanding the tradeoffs helps explain why some approaches work better for certain problems than others.

AI tethered to existing observability tools

The most common approach is to use AI capabilities provided by your existing tools like coding platforms, observability, or infrastructure. For example: AI assistants in your APM tool can query logs using just natural language. These tools benefit from deep integration with their specific data source. They understand the schema, can optimize queries, and know what patterns typically appear in their domain. For problems that can be diagnosed from a single data source, they can be effective.

The limitation is architectural: they operate within the boundaries of one tool. When the root cause requires correlating a deployment event from your CI/CD system with a configuration change in your infrastructure, and an error pattern in your logs → a single tool that only sees logs can identify the symptom but not the cause. Engineers still have to be the integration layer, manually correlating findings across tools.

This approach also inherits the data model of the underlying tool. If your observability platform treats telemetry as text to be searched, the AI layer treats it that way too. This creates a potential gap in missing semantic relationships that would be obvious to an engineer who understands what the data represents.

AI built on historical pattern matching

A second approach treats past incidents, runbooks, and documentation as a knowledge base. A common approach is using retrieval-augmented generation (RAG), these systems surface relevant historical context during new investigations. When a new incident occurs, the system finds similar past incidents and presents their resolutions as guidance.

This has genuine value. Many production issues rhyme with previous incidents. They will have similar resource exhaustion patterns, similar service interactions under load, similar configuration mistakes in different contexts. When the current issue resembles something you've solved before and documented well, pattern matching accelerates investigation significantly.

The problem is when historical pattern matching becomes the only debugging mechanism rather than one input among many.

Production environments are ephemeral. The infrastructure configuration, service topology, and deployment state that existed during a past incident may be completely different now. A solution that worked six months ago might not apply or might actively cause harm in the current environment. Pattern-matching systems that can't verify historical context against current system state risk surfacing outdated or misleading guidance.

Systems that rely primarily on historical retrieval can only surface the closest matches they find, which may be superficially similar while being causally unrelated. Historical knowledge is one piece of the puzzle. It shouldn't be mistaken for the whole picture.

AI designed to debug like an engineer

A third approach treats debugging as the investigation process it actually is: forming hypotheses, gathering evidence, testing theories, and iterating toward root cause. Rather than searching text or matching patterns alone, these systems reason about production environments the way experienced engineers do.

The AI must understand how production systems actually work: not just as data sources to query, but as interconnected components with causal relationships. It must navigate directly to data that matters rather than pulling everything. It must operate tools the way an expert would: knowing which queries to run, how to interpret results in context, and where to look next based on what the data reveals. And it must pursue multiple hypotheses in parallel, the way a team of engineers with different domain expertise would approach a complex incident.

Resolve AI takes this approach. It connects to production systems across code, infrastructure, telemetry, and documentation to investigate with the targeted, hypothesis-driven methodology that effective debugging requires. When an incident occurs, it builds understanding of how the affected services relate to each other, what changed recently, and what the telemetry shows. It forms theories about potential causes, gathers evidence to test each theory, and iterates until it can trace a causal chain from root cause to observed symptoms.

Historical knowledge fits within this larger framework. Resolve AI incorporates patterns from past incidents, runbooks, and documentation; but validates them against current system state rather than applying them blindly. When an engineer corrects its reasoning or when an investigation path leads to successful root cause identification, that learning becomes available for future incidents. The system builds institutional knowledge over time, but treats it as one input to reasoning rather than a lookup table for answers.

Because this approach treats debugging as reasoning rather than retrieval, it handles novel issues that don't match historical patterns. It can identify root causes that span domains because it reasons across domains rather than operating within tool boundaries.

The tradeoff is complexity. Systems that reason about production environments require deeper integration and more sophisticated architecture than systems that search logs or retrieve documents. But for the cross-domain, causally complex debugging problems that consume the most engineering time, reasoning-based approaches can reach conclusions that simpler methods cannot.