Manual Testing Only
Zero automated tests. The team has no idea where to start and the codebase was not designed for testability.
9 minute read
Tag: Deployment-Automation
Missing Deployment Pipeline
Builds and deployments are manual processes. Someone runs a script on their laptop. There is no automated path from commit to production.
11 minute read
Manual Deployments
The build is automated but deployment is not. Someone must SSH into servers, run scripts, and shepherd each release to production by hand.
11 minute read
Snowflake Environments
Each environment is hand-configured and unique. Nobody knows exactly what is running where. Configuration drift is constant.
9 minute read
No Infrastructure as Code
Servers are provisioned manually through UIs, making environment creation slow, error-prone, and unrepeatable.
8 minute read
Configuration Embedded in Artifacts
Connection strings, API URLs, and feature flags are baked into the build, requiring a rebuild per environment and meaning the tested artifact is never what gets deployed.
8 minute read
No Environment Parity
Dev, staging, and production are configured differently, making “passed in staging” provide little confidence about production behavior.
8 minute read
Pipeline Definitions Not in Version Control
Pipeline definitions are maintained through a UI rather than source control, with no review process, history, or reproducibility.
9 minute read
Ad Hoc Secret Management
Credentials live in config files, environment variables set manually, or shared in chat - with no vault, rotation, or audit trail.
9 minute read
No Build Caching or Optimization
Every build starts from scratch, downloading dependencies and recompiling unchanged code on every run.
11 minute read
Blind Operations
The team cannot tell if a deployment is healthy. No metrics, no log aggregation, no tracing. Issues are discovered when customers call support.
9 minute read
No Deployment Health Checks
After deploying, there is no automated verification that the new version is working. The team waits and watches rather than verifying.
11 minute read
Change Advisory Board Gates
Manual committee approval required for every production change. Meetings are weekly. One-line fixes wait alongside major migrations.
11 minute read
Separate Ops/Release Team
Developers throw code over the wall to a separate team responsible for deployment, creating long feedback loops and no shared ownership.
9 minute read
Compliance interpreted as manual approval
Regulations like SOX, HIPAA, or PCI are interpreted as requiring human review of every change rather than automated controls with audit evidence.
10 minute read
Security scanning not in the pipeline
Security reviews happen at the end of development if at all, making vulnerabilities expensive to fix and prone to blocking releases.
10 minute read
Separation of duties as separate teams
A compliance requirement for separation of duties is implemented as organizational walls - developers cannot deploy - instead of automated controls.
10 minute read
Premature Microservices
The team adopted microservices without a problem that required them. The architecture may be correctly decomposed, but the operational cost far exceeds any benefit.
7 minute read
Shared Database Across Services
Multiple services read and write the same tables, making schema changes a multi-team coordination event.
11 minute read
Distributed Monolith
Services exist but the boundaries are wrong. Every business operation requires a synchronous chain across multiple services, and nothing can be deployed independently.
8 minute read
Hard-Coded Environment Assumptions
Code that behaves differently based on environment name (if env == ‘production’) is scattered throughout the codebase.
10 minute read
Manually Triggered Tests
Tests exist but run only when a human remembers to trigger them, making test execution inconsistent and unreliable.
8 minute read
API Changes Break Consumers Without Warning
Breaking API changes reach all consumers simultaneously. Teams are afraid to evolve APIs because they do not know who depends on them.
3 minute read
The Build Runs Again for Every Environment
Build outputs are discarded and rebuilt for each environment. Production is not running the artifact that was tested.
3 minute read
Every Change Requires a Ticket and Approval Chain
Change management overhead is identical for a one-line fix and a major rewrite. The process creates a queue that delays all changes equally.
3 minute read
Multiple Services Must Be Deployed Together
Changes cannot go to production until multiple services are deployed in a specific order during a coordinated release window.
4 minute read
Data Pipelines and ML Models Have No Deployment Automation
Application code has a CI/CD pipeline, but ML models and data pipelines are deployed manually or on an ad hoc schedule.
3 minute read
Database Migrations Block or Break Deployments
Schema changes require downtime, lock tables, or leave the database in an unknown state when they fail mid-run.
4 minute read
Every Deployment Is Immediately Visible to All Users
There is no way to deploy code without activating it for users. All deployments are full releases with no controlled rollout.
3 minute read
The Team Is Afraid to Deploy
Production deployments cause anxiety because they frequently fail. The team delays deployments, which increases batch size, which increases risk.
4 minute read
The Development Workflow Has Friction at Every Step
Slow CI servers, poor CLI tools, and no IDE integration. Every step in the development process takes longer than it should.
3 minute read
Releases Are Infrequent and Painful
Deploying happens monthly, quarterly, or less. Each release is a large, risky event that requires war rooms and weekend work.
4 minute read
Getting a Test Environment Requires Filing a Ticket
Test environments are a scarce, contended resource. Provisioning takes days and requires another team’s involvement.
3 minute read
The Deployment Target Does Not Support Modern CI/CD Tooling
Mainframes or proprietary platforms require custom integration or manual steps. CD practices stop at the boundary of the legacy stack.
3 minute read
Every Change Rebuilds the Entire Repository
A single repository with multiple applications and no selective build tooling. Any commit triggers a full rebuild of everything.
3 minute read
No Evidence of What Was Deployed or When
The team cannot prove what version is running in production, who deployed it, or what tests it passed.
3 minute read
Developers Cannot Run the Pipeline Locally
The only way to know if a change passes CI is to push it and wait. Broken builds are discovered after commit, not before.
3 minute read
Deployments Are One-Way Doors
If a deployment breaks production, the only option is a forward fix under pressure. Rolling back has never been practiced or tested.
4 minute read
Teams Cannot Change Their Own Pipeline Without Another Team
Adding a build step, updating a deployment config, or changing an environment variable requires filing a ticket with a platform or DevOps team and waiting.
3 minute read
Each Language Has Its Own Ad Hoc Pipeline
Services in five languages with five build tools and no shared pipeline patterns. Each service is a unique operational snowflake.
3 minute read
Releases Depend on One Person
A single person coordinates and executes all production releases. Deployments stop when that person is unavailable.
4 minute read
Security Review Is a Gate, Not a Guardrail
Changes queue for weeks waiting for central security review. Security slows delivery rather than enabling it.
3 minute read
Services Reach Production with No Health Checks or Alerting
No criteria exist for what a service needs before going live. New services deploy to production with no observability in place.
3 minute read
Pipelines Take Too Long
Pipelines take 30 minutes or more. Developers stop waiting and lose the feedback loop.
3 minute read
The Team Is Caught Between Shipping Fast and Not Breaking Things
A cultural split between shipping speed and production stability. Neither side sees how CD resolves the tension.
3 minute read
Staging Passes but Production Fails
Deployments pass every pre-production check but break when they reach production.
3 minute read
Deploying Stateful Services Causes Outages
Services holding in-memory state drop connections, lose sessions, or cause cache invalidation spikes on every redeployment.
3 minute read
Vendor Release Cycles Constrain the Team's Deployment Frequency
Upstream systems deploy quarterly or downstream consumers require advance notice. External constraints set the team’s release schedule.
3 minute read
Services in the Same Portfolio Have Wildly Different Maturity Levels
Some services have full pipelines and coverage. Others have no tests and are deployed manually. No consistent baseline exists.
3 minute read
Work Stalls Waiting for the Platform or Infrastructure Team
Teams cannot provision environments, update configurations, or access infrastructure without filing a ticket and waiting for a separate platform or ops team to act.
3 minute read