There is a kind of work that keeps most modern developer stacks running, but almost never shows up on a roadmap. It lives in nameless scripts, half‑documented bots, and spreadsheets that only one person really understands.
Ask around any engineering org and you will eventually find the same character: the engineer who “just knows how everything fits together.” They own a cron job that syncs feature flags into the analytics tool, a Slack bot that posts deploy summaries, a spreadsheet that maps service names to dashboards, and three tiny services that keep legacy systems talking to each other. None of this is in the main product repo. All of it is critical.
This work does not appear in status reports or quarterly goals, but when something breaks, everyone pings that engineer first. The stack looks modern from the outside—cloud infra, CI/CD, specialized tools for every need—but inside, it depends on invisible glue work that nobody planned for and few people are rewarded for doing.
1. The “scripts” and “bots” no one talks about
Imagine a developer named Lina on a mid‑size team. Her job title says “senior backend engineer,” but if you shadow her for a week, you will see something different. Every morning she scans a set of pinned tabs: a spreadsheet that tracks which services have which alerts, a private Git repo full of one‑off scripts, a chat channel where a custom bot posts deploy summaries from three different systems.
Over the years, Lina has become the person who patches the gaps between tools. When the incident system did not talk cleanly to the ticketing system, she wrote a small service to sync them. When analytics needed a stable way to know which feature flags were active for which customers, she wired up an export job. When a partner API changed its format, she quietly updated the integration so the dashboards stayed accurate.
None of this work was on a roadmap. It usually started as “Can you help us get this working?” and ended as “We can’t ship until Lina’s script runs.” The scripts live in side repos. The bots run under her account. The spreadsheets live in her personal drive, shared with a few teammates who know to look.
If Lina took a month off, the stack would technically still exist. But the experience of working inside it—deploying, debugging, understanding what is going on—would quickly deteriorate. This is glue work in action: essential, fragile, and mostly invisible.
2. What glue work looks like in modern stacks
Glue work is everything that connects tools, data, and teams but is not considered core product work. It shows up as:
Scripts that sync data between systems on a schedule.
Bots that post updates into chat, or mirror status changes across tools.
Cron jobs that clean up state, reconcile IDs, or backfill missing fields.
Ad‑hoc dashboards that combine metrics from multiple sources.
Manual workflows that live in someone’s head (“every Friday I export this CSV and re‑import it over there”).
Some of this work is highly technical; some of it is mostly process. All of it exists to make fragmented tools behave like a coherent system. The problem is that glue work often falls into a no‑man’s‑land: it is not glamorous product development, it is not formally part of the platform roadmap, and it is not recognized as leadership work unless someone goes out of their way to name it.
Writers on engineering culture have described glue work as the invisible support and coordination that holds teams together; it is crucial for delivery, yet it rarely maps cleanly to lines of code or feature launch metrics. In many orgs, the same is true of the glue holding the stack together: it is critical infrastructure that no one has formally agreed to own.
3. Why glue work emerges in fragmented toolchains
Glue work is not an accident; it is a predictable consequence of fragmented toolchains. Every time you add a new tool, you create a small gap:
Different data models that do not line up cleanly.
Events in one system that should trigger workflows in another, but do not.
Lacking native integrations between the specific tools your team has chosen.
Vendors cover the common cases, but every team has its own quirks. You need a particular label to sync from the incident tool to the pager. You care about a derived metric that spans three systems. You want feature flags, experiments, and billing to agree on the same customer IDs. Those edges are where glue work appears.
Developers step in with quick fixes: a Python script here, a webhook handler there, a spreadsheet to reconcile IDs “just for now.” These fixes live close to the problem, so they are fast to ship and easy to justify. But over time, they harden into critical infrastructure. The original author may move teams; the script stays. The cron job keeps running. The bot keeps posting.
In your earlier work on tool sprawl, you described how each additional tool adds to toolchain debt. Glue work is one of the ways that debt expresses itself: the human effort required to keep a sprawling toolchain behaving as if it were one coherent system. Calling it out explicitly helps teams see that the problem is not “people writing too many scripts” but an environment that demands those scripts in the first place.
4. The risks of invisible glue work
Invisible glue work carries three major risks: single points of failure, fragility, and burnout.
Single points of failure
When only one person understands a script or bot, that person becomes a bottleneck. They are the only one who can debug strange behavior, adjust it to match new requirements, or safely turn it off. If they leave, change teams, or are simply unavailable during an incident, the organization suddenly realizes how dependent it is on code that nobody else has ever really read.
Fragility under change
Glue is often written quickly and updated rarely. A small change in an API, field name, or authentication scheme can cause silent failures—backfills that stop running, sync jobs that partially apply, dashboards that drift out of date. Articles on managing glue code at scale note that many homegrown glue solutions are not built to withstand failure or to be easily observed. When something breaks, teams may not notice until a downstream process fails in a confusing way.
Burnout and constant firefighting
Engineers who own glue work carry a hidden on‑call load. They are pulled into issues that have nothing to do with the core product, simply because they understand how data moves between systems. In SRE literature, this kind of repetitive, reactive work is labeled “toil,” and teams are advised to keep it below half of an engineer’s time to avoid burnout. Glue work often exceeds that threshold without anyone realizing it, because it is not tracked or categorized as such.
These risks rarely show up as high‑priority Jira tickets. They show up as a slow erosion of confidence: people are afraid to change certain parts of the stack, afraid to retire old tools, or afraid to make bold architectural moves because “no one knows what that script over there really does.”
5. How glue work distorts priorities and velocity
Glue work also quietly distorts how teams allocate time and how leaders interpret velocity.
On the individual level, glue tasks steal time from feature development, learning, and exploratory work. An engineer may start the week planning to work on a new customer‑facing capability, only to spend half their time debugging a failing sync job or updating an integration to match a vendor change. None of that effort appears under “new value shipped,” but it is necessary just to keep the lights on.
On the team level, glue work undermines the story told by infra metrics. The pipeline might show fast builds and deploys. Cloud dashboards might show healthy latency and error budgets. Yet the experience of shipping still feels slow and brittle, because a lot of energy is being spent on stitching everything together. Glue work becomes one of the reasons execution feels sluggish even when deployment itself is no longer the bottleneck.
Context switching makes this worse. Glue issues often arrive as interrupts—an alert that a sync failed, a confused Slack ping about missing data, a last‑minute request to wire one more field into a dashboard before a demo. Each interrupt pulls engineers out of deep work and into the weeds of integrations and edge cases. Over time, this pattern drains momentum in exactly the way you have described when writing about context switching and execution drag.
Without a name for glue work, teams misdiagnose the problem. They attribute slow progress to lack of focus, or to individual engineers “getting distracted,” rather than to a structural reality: a growing fraction of engineering time is being consumed by invisible, under‑rewarded work that the current stack demands.
6. Making glue work visible
The first step toward fixing glue work is to see it. That means turning private scripts, personal bots, and unwritten routines into something the team can actually talk about.
A simple “glue work inventory” can help:
Ask each team to list every script, bot, cron job, one‑off service, and manual recurring task they rely on to move work from one system to another.
For each item, capture what it does, where it lives (repo, server, owner account), how often it runs, and what happens if it fails.
Include process‑level glue: recurring manual steps like exporting CSVs, copying data between dashboards, or hand‑assembling reports.
SRE guides on eliminating toil recommend exactly this kind of audit: identify repetitive, manual, low‑leverage tasks and quantify how much time they consume so you can decide what to automate or redesign. The same approach applies to glue work; until you write it down, it is easy to underestimate how much of your stack depends on it.
Once you have an inventory, create a rough map of “how data and events actually move” across tools and glue components. Draw the systems as nodes and the glue as edges: scripts, bots, and manual workflows. This does not need to be perfect, but it should be honest. The goal is to make clear where the stack is relying on bespoke connections rather than native flows.
Even this basic visibility changes the conversation. Decisions about new tools, deprecations, or architecture are no longer made in isolation; you can see which glue paths will be affected and who will need to be involved.
7. Reducing glue work with more unified execution
You cannot eliminate glue work entirely. Some amount of custom logic will always be needed to connect your specific workflows and constraints. The question is how much glue you need and how brittle it is.
Unified execution environments offer one answer: reduce the surface area where glue is required by bringing more of the workflow into a single, coherent environment. Instead of scattering planning, state, logs, and automation across many tools and asking humans to be the integration layer, you center the work in one place.
In a unified execution environment, common paths are native: triggering a deploy, observing its impact, attaching metrics to a change, looping back into the next iteration. Integrations still exist, but they are fewer, more deliberate, and easier to observe. Platforms that take glue code seriously show how you can turn scattered scripts into first‑class, observable workflows instead of “that one shell script Lina runs when things get weird.”
The goal is not “no glue ever”; it is “far less brittle, bespoke glue.” When the platform gives you strong primitives and consistent execution contexts, you need fewer one‑off scripts to compensate for gaps between tools. The glue that remains can be designed, reviewed, and monitored like any other part of the system.
This is where your broader narrative about unified execution environments comes back in. By shrinking the number of moving pieces and offering more native paths for common flows, you reduce both the amount of glue required and the risk carried by the people who maintain it.
8. A cultural shift: valuing and intentionally designing glue
Technical changes alone are not enough; teams also need a cultural shift in how they think about glue work.
First, you have to value it. Glue work that keeps the stack functioning is real engineering work, even if it does not live in the main product repo. When you recognize and reward it, you make it easier for engineers to speak up about the hidden jobs they are doing and to advocate for better solutions.
Second, you have to decide what to do with it. For each major piece of glue, there are really only three sustainable options:
Productize it. Bring it into a proper repo, give it an owner, add tests and observability, and treat it as part of your platform.
Eliminate it. Replace it with a more integrated tool or platform that makes the custom glue unnecessary.
Consciously accept it as temporary. Mark it as a short‑term bridge with a clear plan and timeline to either productize or eliminate it.
The default state—where glue accumulates by accident and remains invisible—is what gets teams into trouble. It leaves critical infrastructure without owners, hides a large fraction of engineering effort under the radar, and makes every large‑scale change scarier than it needs to be.
Unified execution environments can help by reducing the amount of glue you need, but they do not absolve teams from making choices. You still have to decide which integrations you will own, which ones you will let the platform handle, and how you will keep glue work from creeping back into the shadows as the stack evolves.
What to do next
If this sounds uncomfortably familiar, start with a glue work inventory this month.
Ask every team to list the scripts, bots, cron jobs, and recurring manual tasks that keep their stack usable.
Mark which ones are truly critical, which are historical accidents, and which no one feels comfortable touching.
Decide, explicitly, which glue you will formalize, which you will retire, and where a more unified environment could eliminate entire categories of glue altogether.
If you are exploring ways to move from fragile, hidden glue toward more native, unified execution paths, stay tuned. The next posts will show concrete patterns for consolidating workflows so the stack runs on intentional design, not on whoever happens to remember which script to run.
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