You’ve probably heard it in meetings, whispered in Slack threads, or blurted out in frustration by a PM: “Why is it taking weeks to get this out the door?”

At some point, every engineering org hits this wall. New features slow to a crawl. Deployments feel like a mini project in themselves. Product managers get antsy. Leadership starts eyeing engineering like it’s the bottleneck—like you’re the problem. Ouch.

But here’s the twist: it’s not the people. It’s the pipeline.

Let’s walk through what’s going wrong—and how to fix it without burning your team out or duct-taping your way through another quarter.

The Silent Sludge in Your Pipeline

If your deploys only happen once every couple of weeks, that’s not agility—that’s a waterfall wearing CI/CD as a Halloween costume.

Here’s a pattern I see all the time:

• Engineers work in long-lived feature branches.
• Pull Requests pile up, waiting for review.
• Builds only kick off after merges (so bugs sneak in after everyone gives the thumbs-up).
• Staging environments are shared—or worse, broken.
• Rolling back? Might as well light a candle and hope for the best.

Now, no one sets out to build a pipeline like this. It just… kind of happens. Bit by bit. Like technical debt with a passport and a gym membership—it travels and grows.

Commit-Time Builds: Because Waiting Until Merge Is Like Brushing After Eating Candy

Let me ask you something: Why wait to build and test until after a PR is merged?

That’s like assembling IKEA furniture after you invite your friends over for dinner.

Shifting your CI to build and test on every commit (not just merges) does two things instantly:

1. It gives developers near-instant feedback—no more “It worked on my branch, I swear.”
2. It surfaces integration issues before they become everyone’s problem.

This isn’t some theoretical dream. With tools like GitHub Actions, GitLab CI, CircleCI, or Buildkite, triggering builds per commit is dead simple. And paired with containerized test runners, the builds stay fast and isolated.

Yes, your CI bill might tick up a bit. But how much is each delay really costing you?

No Tests, No Party

Here’s where it gets uncomfortable. You can’t fix this mess unless you get serious about tests. Like, ruthlessly consistent about them.

That means:

• Every commit runs your test suite.
• Tests must pass before merging—no exceptions.
• Flaky tests? Quarantine or delete them. Don’t argue. Fix or kill.

Automated testing is your safety net. Without it, you’re just doing trust-based engineering. And in a growing org, that’s not a compliment.

Temporary Environments, Permanent Relief

Now let’s talk staging. Or, as some teams know it, “that weird server that’s broken again.”

Reviewing features in a shared staging environment is chaos. Someone’s always testing the wrong thing. Or accidentally overwriting someone else’s changes. It’s like trying to rehearse a play on a bus during rush hour.

Instead: ephemeral environments.

With Infrastructure-as-Code tools like Terraform, Pulumi, or even just Docker Compose with some scripting, you can spin up full-featured environments per PR. Add a preview link. Let the PM or designer actually see what they’re approving.

These can be torn down automatically after merge or after a few hours. Clean, fast, and way less arguing in the #deploy channel.

Rollbacks Shouldn’t Involve Panic

If your rollback plan involves Slack, manual SSH, and someone named “Stefan” who knows the scripts—you don’t have a rollback plan. You have a Stefan dependency.

Use versioned artifacts. Container snapshots. Git tags. Whatever fits your stack. Just make sure you can redeploy a known-good version in seconds, not hours.

Tools like ArgoCD, Flux, or even a simple “git reset + docker compose up” strategy can get you most of the way there.

So What Actually Changes?

When you put all this together—commit-time CI, enforced tests, ephemeral environments, automated rollbacks—you get a pipeline that breathes. Suddenly:

• PMs don’t have to wait two weeks.
• Engineers don’t dread deploys.
• Bugs get caught earlier, when they’re cheaper to fix.
• And leadership stops treating your team like a bottleneck.

You shift from “move fast and break things” to “move fast and know when it breaks—and how to fix it fast.”

But Wait—What About Culture?

Ah yes. The human bit.

Tech is never just tech. You’re not just changing pipelines; you’re changing habits. This stuff only sticks if your team feels safe experimenting and failing fast. You need buy-in. Some teams even gamify CI success rates. Others run weekly “deployment health” retros.

There’s no silver bullet, but here’s a mantra I’ve found useful:

“The goal isn’t speed. The goal is flow.”

Speed can lead to mistakes. Flow builds trust. It means work moves smoothly through the system, without invisible friction or surprise blockers.

Final Thought

CI/CD isn’t just about faster deploys—it’s about confidence. When your pipeline supports your team instead of dragging them down, you ship more, stress less, and stop hearing that dreaded phrase: “Engineering is the bottleneck.”

Because let’s be honest—most of the time, the real bottleneck isn’t engineering. It’s inertia.

And once you fix that? Everything else moves faster.

Want a sanity check on your current pipeline? Or just want to rant about flaky tests and broken staging servers? Shoot me a message. I’ve seen some stuff.

Auryn: A Domain-Specific Language for CLI Pipelines

Sometimes you just can’t help but write a bash script to chain together a couple of tools. Sometimes those bash scripts get out of hand, and you wish you had something that could effectively be the linux pipe symbol, but also clean and format your data for the next CLI program.

Auryn has your back.

The challenge with CLI pipeline tooling

Every pipeline engineer has attempted to chain tools together using Bash scripts or integrate them into Jenkins pipelines. The reality is that there’s a lack of standardized input and output formats across CLI tools.

Each tool serves a different purpose, making automation cumbersome—even though the process is often repetitive:

1. Read a file.
2. Optionally transform the input.
3. Apply the tool with specific parameters.
4. Save the (potentially massive) output to a file.
5. Clean the output for the next tool, which will…
6. Repeat the cycle.

Introducing Auryn: a CLI glue pipeline DSL

Enter Auryn—a domain-specific language (DSL) designed to streamline shell pipelines. Think of it as a platypus: seemingly uncertain of its identity, yet functioning exceptionally well because of it.

Here’s a glimpse of Auryn in action:

run "bash cert_transparency_check.sh __DOMAIN__"
  parsewith cert_transparency_check 
  output "__OUTPUT_DIR__/found_domains.txt"
  as $domain_list

In this snippet:

• A script is executed with a constant (__DOMAIN__) as its only known truth.
• The output is parsed using a simple parser you write in a shell script, tailored to the unique output of each CLI tool.
• The parsed text can be saved to a file and stored in a variable ($domain_list) for use in subsequent tools.

Why Auryn Matters

Auryn addresses the fragmentation in CLI tooling by providing a unified language to define and manage shell workflows. It empowers security engineers to automate complex pipelines without being bogged down by inconsistent tool interfaces.

The inspiration for Auryn

Tom learnt about CPU pipelining and thought the mental model applied to running bash scripts as well. A couple of hours talking to an LLM later, Auryn was born.

The cycle is:

• Instruction Fetch
  • Get data
• Instruction Decode
  • Parse the data for CLI input
• Execute
  • Run the CLI program with the supplied parameters
• Write to memory
  • Put the result into a variable
• Write back
  • (Optionally) write the raw output to a file

Auryn is the result of a brain blip, and was put together in a couple of hours yet results in an immensely useful output.

Get Started with Auryn

Ready to tame the beast? Grab a copy of Auryn from the project’s GitHub page: https://github.com/kapott/auryn. Feel free to expand upon it. While the repository doesn’t have a license yet, it will be free, as in beer.

Marketing fires off another flashy campaign. Emails are sent, ads are clicked, users flood the customer portal like a swarm of caffeine-fueled shoppers on Black Friday. And then—it hits.

Pages load like they’re stuck in molasses. Or worse, nothing loads at all. The system groans under the weight. Users complain. Sales stall. And someone in upper management starts asking that dreaded question: “Why weren’t we ready for this?”

You feel the sting, not just because it’s your infrastructure, but because deep down, you knew this might happen. Again.

Let’s unpack this. Not just with tech speak and bullet lists, but with some honest reflection—and a few solid ideas you can actually use.

When “Success” Becomes a System Failure

Here’s the irony: the portal’s underperformance usually stems from something good—growth. More users, more activity, more data flying around.

But legacy systems? They don’t celebrate your wins. They choke on them.

One day it’s a monolith that hums quietly at 15% load. The next, it’s burning CPU like it’s auditioning for a role in Mad Max: Fury Load. And that tiny, single-threaded component you inherited five CTOs ago? It’s now holding your entire digital reputation hostage.

What’s worse is that customers don’t care why it’s slow. They just know it’s not working. They’re trying to pay their bill, check their status, file a claim—and they’re getting a spinning wheel of doom. Cue the angry tweets, lost conversions, and that cold, creeping sense of dread.

So… What Can You Do?

Let’s cut the fluff. The fix isn’t a motivational poster in the dev room. It’s targeted, iterative change.

Here’s a roadmap that’s worked for real teams facing this exact pain:

Start With Load Testing, Not Guesswork

You wouldn’t try to tune a race car without knowing where it breaks down at high speed, right?

Same thing here. Run proper load tests. Simulate real user traffic. Ramp it up. Push it beyond what your campaigns expect. You’ll spot bottlenecks faster than you can say “timeout error.”

Often, it’s not the whole system that fails—just one poorly designed function. One slow database query. One dependency that starts to domino under pressure.

And honestly? You might not like what you find. But knowing is better than waking up to a downed system.

Find the Rotten Core (Hello, Legacy)

Every seasoned engineer has faced it: a dusty piece of logic that no one touches because “it just works.”

Until it doesn’t.

Sometimes it’s a synchronous job queue. Other times it’s a memory-hungry reporting module that slams your backend when traffic spikes. We once found a SOAP connector—yes, SOAP—that was quietly blocking dozens of threads under load. Brutal.

This is where profiling tools and call tracing shine. Tools like Jaeger, Prometheus, or even good old strace can light up the exact moment things go sideways.

Cache Like You Mean It

Here’s where Redis or Varnish come in handy. They’re not magic—but close.

The idea’s simple: don’t ask your backend the same thing 5000 times per minute. If something doesn’t change often (like pricing info, account summaries, static content), cache it aggressively. Front it with a CDN. Make it boring.

The payoff? Less stress on the core. Fewer round-trips. Happier users.

Go Stateless—or at Least Less State-Obsessed

Monoliths don’t scale well. Especially ones clinging to session state like a toddler to a teddy bear.

You don’t need to break the whole thing into a thousand microservices overnight. That’s a recipe for burnout and missed deadlines. But do peel off the high-traffic routes. Things like login, dashboard views, or payment status.

Move those to lightweight, stateless services. Use JWTs. Offload session management. You’ll sleep better.

And yeah, containerizing helps. But not if you’re dragging old habits into shiny new pods.

Autoscaling Isn’t a Luxury—It’s Survival

Your Kubernetes setup might be stable now. But is it ready to flex?

Autoscaling isn’t just a checkbox in a YAML file. It needs thoughtful metrics. CPU alone won’t cut it. Use custom metrics if you must—queue lengths, request latency, memory pressure.

And for the love of uptime, test the autoscaler. Don’t assume it kicks in just because you told it to.

Think of it like hiring backup staff before a sale. You want them trained and ready—not arriving after the shelves are already empty.

The Real Fix: Culture, Not Just Code

Let’s be honest—technical fixes are only part of the equation. The other half? Communication.

When Marketing spins up a campaign, does Engineering even know? Are there alerting thresholds tied to business events? Does anyone talk about performance before users start yelling?

If not, that’s the first change to make.

Create a culture where campaigns and capacity planning go hand in hand. Where load testing isn’t a “once-a-year” task, but a habit. Where developers get curious about why a certain endpoint spikes, not just how to make it faster.

In the End (Not That Kind of Ending)

Systems will fail. That’s a given. But the teams who recover fast—and earn user trust—are the ones who get ahead of the next peak. Who know their limits. And who make just enough time to fix the stuff no one else sees coming.

Next time the portal groans under pressure, let it be because you planned for it. Not because you hoped it wouldn’t happen again.

And if you’re still waiting for someone to sign off on the load test budget—just show them last campaign’s downtime stats. That usually does the trick.

TL;DR

• Simulate traffic with real-world load tests
• Hunt and kill your bottlenecks (legacy code, slow queries, blocking threads)
• Add a cache layer with Redis, Varnish, or both
• Break off high-traffic routes into stateless services
• Use autoscaling like you actually believe in it
• Tie marketing and infra planning closer together

Need help convincing the rest of the team? Send them this post. Or better yet—print it out, tape it to the fridge in the break room, and add a sticky note:

“This is why we can’t have nice things—unless we fix it.”

Want help turning this into an action plan? Let’s talk.

Basic information

• System or platform name
  • What are the internal names, project names, jargon, nicknames, pet names for the platform(s) and environment(s)?
• What environments are there?
  • Classic DTAP or another model?
• Business purpose of the platform (what does it do for the company?)
• Owner / single point of contact for the platform
  • If there is no single point of contact, contacts for each environment or separated responsibility within the platform
    • Software deployment lifecycle (SDLC)
    • Infrastructure
    • Datacenter team
    • Crisis management
    • Information security
    • Architecture
• Who uses it (per environment) ?
  • Internal users (+estimated number)
  • External users (+estimated number)
• If non-standard DTAP
  • Criticality level per environment

People & Processes

• How do users interact with the system daily?
• Which business processes rely on this system?
• Any undocumented tribal knowledge?
• Any user groups at risk during migration?

Risks & Constraints

• What absolutely cannot go down during migration?
• Any critical dates (e.g. billing cycles, audits)?
• Sensitive data or legal/compliance constraints?
• Outstanding incidents, bugs, or known issues?

Architecture

• Monolith, microservices or hybrid?
  • Schema’s, flowcharts, graphics, data flow diagrams?
• On-prem, cloud or hybrid?
• Running on VM’s, bare metal or containers?
• OS details (types, versions?)
• Languages / frameworks used in application(s)?
• Is there a CI/CD pipeline? How is code deployed?

Network & Infrastructure

• Existing network diagrams
  • If nonexistent, sketch one
• Inbound
  • Ports
  • Protocols
  • Load balancers
• Outbound
  • APIs
  • Third party services
• Firewalls, NAT, proxies?
• DNS structure / domain naming?
  • Internal
  • External

Data & Storage (state)

• Databases
  • Type(s)
  • Version(s)
  • Size(s)
• Where is the data physically stored?
  • Does this location matter for the company?
• Any data that _must_ not move (e.g. compliance?)
• Existing backups? Last tested when?
• Any shared filesystems, blob/object storage?

Application landscape

• Which applications are running?
  • Don’t forget cronjobs / systemd timers
  • Don’t forget custom scripts
• What depends on what?
  • Dependency graph from app. perspective
    • Sketch one if it’s not there
• Any blackbox components?
  • Closed source
  • Undocumented
  • Unknown-operation application/code?
• Upcoming (or passed) or EOL components?

Security & Access

• What authentication methods are used?
  • Local
  • LDAP
  • SSO
  • OAuth?
• User roles / access control model?
  • Is there a matrix table for it?
  • If not – make it or have it made
• How are secrets handled?
  • Hardcoded
  • Ansible vault
  • env files
  • etc.
• Network segmentation
• Firewall rules
• Last security audit?
  • Any open issues?

Monitoring & Logging

• What logs exist?
• Where are they stored?
• What is their (mandatory) retention?
• Is monitoring in place?
  • Prometheus, Zabbix..
• Are there alerts?
  • Who gets them?
• Any trends in resource usage?

External Dependencies

• Are there integrations with third-party API’s or services?
• API keys or tokens in use?
• Any licenses, usage limits or contracts?

Ready for Migration Planning

• Is the environment fully mapped?
• Can parts of the system be moved independently?
• What goes first? What can wait?
• What needs testing before production cutover?