Cloud & DevOps
CI/CD pipelines: test, build and deploy every commit automatically
A CI/CD pipeline automates the path of a code change to production: continuous integration builds and tests every commit, continuous deployment delivers validated versions to staging and then production, with checks and a planned rollback. This is what makes it possible to ship often without multiplying incidents.
This service is for teams that still deploy manually, whose tests are not run systematically, or whose existing pipelines are slow, fragile and bypassed. It covers web applications, APIs, mobile applications and infrastructure described as code.
We design GitHub Actions or GitLab CI pipelines tailored to your code and platform: unit and integration tests, static analysis, Docker image builds, deployments to servers, ECS or Kubernetes, database migrations, and tested rollback procedures.
When do we step in?
Manual deployment by a single person
Going to production relies on a local script or a series of commands known to one team member. An absence or a mistake blocks or breaks the release.
Tests exist but are not run
The test suite exists but rarely runs, because nobody launches it before merging. A pipeline makes it mandatory on every pull request.
Existing pipeline that is slow or flaky
Builds take over half an hour, fail randomly and the team has got used to re-running or ignoring them. Caching, parallelisation and isolating flaky tests put the pipeline back at the team's service.
Multi-environment releases
You need to deploy to development, staging and production with different configurations, manual approvals at certain stages and traceability of what is deployed where.
Improvised rollbacks
When a release causes problems, reverting to the previous one happens in a rush, with incompatible database migrations. Rollback should be one command, not a rescue operation.
How we work
- 1
Code and delivery flow analysis
We study repository structure, build tools, existing tests, target environments and branching strategy, to design a pipeline that reflects the way you work.
- 2
Continuous integration
Pipeline triggered on every pull request: installation with dependency caching, linting and static analysis, unit and integration tests with containerised services, coverage reports and vulnerable dependency checks.
- 3
Artifact builds
Multi-stage Docker images built with layer caching, tagged by commit and version, scanned with Trivy and pushed to a private registry. For mobile applications, binary builds and signing.
- 4
Continuous deployment
Automatic deployment to staging on every merge, then to production after approval or automatically depending on your maturity, with migrations executed, blue/green or rolling update strategy and post-deployment health checks.
- 5
Rollback and security
One-action rollback procedure, database migrations designed to stay compatible with the previous version, secrets stored in the platform's secret manager or OIDC to the cloud, and minimal permissions for pipelines.
- 6
Measurement and improvement
Tracking pipeline duration, failure rate and deployment frequency, then continuous optimisation: parallelisation, job splitting, flaky tests identified and fixed.
Technologies we use
- GitHub Actions
- GitLab CI
- Docker and BuildKit
- Amazon ECR and GitHub Container Registry
- Trivy
- Helm and Argo CD
- AWS ECS and EKS
- Terraform
- SonarQube
- Jest, JUnit and pytest
- Fastlane
- OIDC and secrets management
Why choose Agencei?
Pipelines designed for your code
We develop in Node.js, Java, Python and React Native: we know the test and build tools and the pitfalls of each ecosystem, and we integrate them properly.
Speed as a requirement
A slow pipeline is a bypassed pipeline. We set up the caching, parallelisation and job splitting needed for fast feedback on every commit.
Tested rollback, not theoretical
We actually run the rollback procedure before going live, including for database migrations.
Delivery chain security
No plaintext secrets in repositories, OIDC authentication to the cloud without long-lived keys, restricted permissions and dependency and image scanning on every build.
In brief
- What is this service?
- Setting up CI/CD pipelines with GitHub Actions or GitLab CI that automatically test, build and deploy every code change, with migrations, zero-downtime deployment strategies and tested rollbacks.
- Who is it for?
- Development teams that deploy by hand, do not run their tests systematically, or whose existing pipelines are slow and bypassed.
- What problem does it solve?
- Shipping frequently and without stress, catching regressions before production, tracing what is deployed and being able to roll back with one action.
- How long does it usually take?
- A first continuous integration pipeline is usually set up in a few days. Multi-environment continuous deployment with rollbacks often takes a few weeks depending on the target platform.
- What factors influence the price?
- The number of repositories and applications, the state of existing tests, the deployment platform (server, ECS, Kubernetes, mobile), approval and compliance requirements, and optimisation of existing pipelines.
- How does the engagement run?
- Code and delivery flow analysis, continuous integration on every pull request, artifact builds and scanning, continuous deployment per environment, rollback and hardening, then measurement and improvement.
- What are the risks?
- Slow or flaky pipelines bypassed by the team, exposed secrets, deployments without health checks, migrations incompatible with rollback and overly broad permissions for runners.
- What alternatives exist?
- A PaaS platform that deploys from Git, documented manual deployment scripts for very small projects, or other CI tools such as Jenkins or CircleCI if the existing setup requires them.
Frequently asked questions
GitHub Actions or GitLab CI: which should I choose?
Usually the one from the platform that already hosts your code. Both cover the same needs: containerised jobs, caching, secrets, environments and manual approvals. GitLab CI offers a built-in registry and deployment tracking; GitHub Actions has a very large library of actions.
How long should a pipeline take?
There is no universal number, but feedback on a pull request should stay short enough that developers wait for the result rather than moving on. Dependency caching, test parallelisation and job splitting are the usual levers.
Can we deploy to production automatically?
Yes, provided you have sufficient test coverage, progressive deployments and a reliable rollback. Many teams start with a manual approval before production, then remove it once confidence is established.
How do you handle database migrations in a pipeline?
By running them as a separate step before deploying the application, and by designing them to stay compatible with the previous version (adding columns before removing, for example). That way an application rollback remains possible without reverting the migration.
What happens if a deployment fails?
The pipeline stops, the previous version keeps serving traffic and the team is notified. With a rolling or blue/green strategy, users notice nothing. Rollback is triggered with one action if needed.
Should infrastructure be in the pipeline too?
It is recommended. A Terraform pipeline that shows the plan on every pull request and applies it after approval prevents manual changes and keeps infrastructure in sync with code.
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