Building a business application does not end when its features work. In production, the software becomes an ongoing service: users depend on it, business needs change, failures have consequences, and every architectural choice brings operating work. The most useful lessons are therefore about setting realistic reliability goals, seeing what the system is doing, recovering safely, and making room to improve it—not simply shipping the next feature.
The examples below are attributed to the organizations that reported them. They illustrate practical lessons, not personal incidents or experiences of this article’s author.
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What changes when software goes into production?
A tutorial usually demonstrates a successful path through a bounded problem. A production business system has to keep serving real workflows as dependencies, data, usage, and expectations change. That shifts the engineering question from “Does this feature work?” to “Can users complete the work they need, can the team detect when they cannot, and can the service recover without making matters worse?”
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How should a team define reliability?
Reliability is not an abstract score to maximize regardless of cost. It is a user and business outcome. Google Cloud Customer Reliability Engineering (CRE) describes a service-level objective (SLO) as a reliability threshold below which users become unhappy, and says teams should balance that target against user expectations and engineering expense. Its concise formulation is: “Your SLO sets a minimum reliability requirement, something strictly less than 100%.”
The practical implication is to identify the user-visible service being measured and choose a target that reflects the consequences of failure. A system supporting a critical workflow may warrant a different target from an internal tool whose users can safely wait. Aiming automatically at 100% can consume engineering effort and constrain releases without necessarily delivering proportionate value.
Google CRE’s 2019 examples use 90% and 99.95% SLOs to illustrate how differing targets can imply different rollout practices; these are examples, not recommended targets for every application. The same article describes running a service that is 10 times more reliable as “100 times more expensive.” Treat that as the article’s illustrative explanation of how reliability costs can rise, not as a universal measured law.
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Once an SLO is defined, teams need a way to observe whether they are meeting it and to connect misses to user impact. An SLO that exists only in a document cannot guide release decisions or incident response. Google CRE recommends setting reliability targets, measuring impact, and learning from failures rather than relying on optimism: “Hope is not a strategy.”
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What should teams monitor beyond averages?
Monitoring should help answer whether an important user workflow is healthy, not merely whether machines are producing metrics. Averages can conceal a slow or failing experience for a subset of users. In its reliability retrospective, Atlassian says its teams had focused on averages without examining important 90th- and 99th-percentile values. Those tail measurements helped expose behavior that a mean could obscure.
The right indicators depend on the service. Teams should select service-level indicators (SLIs) that represent outcomes users care about, then make the associated SLOs visible to the people who build, deploy, and operate the service. Meta’s account of its internal SLICK system describes standardizing SLI and SLO definitions, making reliability information easier to find, and integrating it into workflows and incident response. As reported in December 2021, SLICK offered per-minute metric granularity and up to two years of retention; those are specifications of Meta’s internal system, not requirements for every team.
Visibility also includes knowing who is accountable for a service and where operational information lives. GitHub’s Engineering Fundamentals program used scorecards covering availability, security, and accessibility, and associated services with information such as tier, quality-of-service, type, owner, sponsor, and contact. GitHub also describes connecting unmet requirements to action items in the service repository. The underlying lesson is organizational as much as technical: an alert or gap is easier to act on when the service has a clear owner and a place to track the work.
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How do incidents become useful learning?
An incident is not automatically a lesson. Without a written record and completed follow-up, a team can restore service yet leave the same conditions in place for the next failure. Google CRE advises writing postmortems after significant SLO hits and near misses, recording what happened, and identifying concrete improvements. It calls postmortems “your best tool for turning hope into concrete action items.”
A useful review describes impact, timeline, contributing conditions, response, and actions with owners. Its purpose is to improve the system, not to find a person to blame. Google CRE writes: “A blameless culture recognizes that people will do what makes sense to them at the time.” It encourages teams to examine the conditions that shaped decisions—including alert quality, training, workload, and processes—so the system can better support a response next time.
Atlassian describes tracking incident recurrence as a signal of whether root causes were being addressed, as well as measuring how long post-incident actions took to complete. Those measures help expose two common failure modes: repeated incidents with no effective corrective work, and action lists that remain open indefinitely. A postmortem’s value depends on follow-through, not on the document alone.
Why do ownership and operational readiness matter?
When a service behaves unexpectedly, teams lose time if nobody knows who owns it, how it is expected to behave, or how to recover it. Operational readiness is the work of making those answers available before an incident. Atlassian’s operational reviews covered data integrity and recovery, monitoring, alerting, logging, on-call plans, security, deployments, and rollbacks.
These checks are useful because production failure is not limited to a process going offline. A deployment might corrupt data, a rollback might be unsafe, an alert might arrive too late, or an on-call responder might lack the context to choose a safe recovery. Readiness reviews turn such assumptions into explicit questions for the team responsible for the system.
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GitHub’s scorecard approach offers one example of connecting expectations to ownership and repository-level follow-up. Its reported areas included durable ownership, code scanning, secret scanning, incident readiness, and accessibility. The specific program is GitHub’s, but the general operational pattern is broadly practical: define expectations, make status visible, assign an owner, and track the work needed to close gaps.
How should teams balance features with technical debt?
Feature delivery is visible, while debt reduction, observability, and incident follow-up can look like work that delays visible progress. Yet deferred maintenance can make future changes harder and incidents more difficult to diagnose. GitHub says its Engineering Fundamentals governance program was created to address technical debt, reliability, and observability as enterprise needs and platform innovation grew.
Atlassian reports a related tension: complexity, observability gaps, and root-cause work accumulated during a large migration and period of fewer features, while later feature demand made it difficult to reserve roadmap time for that debt. These cases do not quantify an industry-wide effect, but they show why operational health should be treated as planned work rather than whatever is left over after feature commitments.
The Software Engineering Institute maintains a collection of technical-debt resources, including organizational recommendations, research reviews, and field studies. That makes technical debt an established subject of software engineering practice; the collection itself does not establish one universal definition or a single statistic that applies to all systems.
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Does a distributed architecture make a business system easier to evolve?
Not by itself. Architecture changes tradeoffs; they do not eliminate complexity. Atlassian says its move from a smaller number of monolithic codebases to more distributed services introduced unintended complexity and reduced confidence in adding capabilities. The company describes responding not only with technical changes but also with changes to hiring, training, tools, and fail-safe processes.
This is a case study, not proof that monoliths are always better or that distributed systems always fail. A service split can offer useful flexibility, but it also creates more interfaces, deployment and monitoring concerns, and coordination work. The choice should follow the product’s needs and the team’s ability to operate the resulting system—not a tutorial’s implication that a particular architecture is the inevitable next step.
What should a team put in place before maintaining a production application?
Before relying on a business application, make the operational expectations explicit and confirm that people can act on them. A compact review can include:
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- User outcome: identify the workflow that matters and define a measurable reliability target appropriate to its impact.
- Signals: choose indicators that reflect user experience, and inspect tail behavior where averages could hide degraded service.
- Ownership: record who owns the service, who can be contacted, and where its operating and recovery information is maintained.
- Recovery: verify data integrity, backup or recovery procedures, deployment and rollback plans, and on-call readiness.
- Learning loop: document significant incidents and near misses, assign specific corrective actions, and track recurrence and completion.
- Roadmap capacity: make space for reliability, observability, and technical-debt work alongside features.
These are not guarantees against failure. They make it more likely that the team will notice consequential problems, understand responsibility, and respond with evidence rather than guesswork.
Further reading
Google’s book Site Reliability Engineering: How Google Runs Production Systems provides deeper reading on SLOs, incident response, and operating services. The principles in it are useful context, while implementation choices still need to fit a team’s users and system.
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