GitOps is a way of running infrastructure and applications where a Git repository is the single source of truth for what should be running.

The Short Definition

Git is a tool for storing files together with their full history. Every change is recorded with who made it, when, and why, and any earlier version can be restored. GitOps means that you describe the desired state of your system in files stored in Git, and an automated agent makes the real system match those files. Instead of someone typing commands against the cluster, people propose changes to the files, the team reviews them, and the agent applies them.

The word “desired state” is important. It means a description of what you want, not a list of steps to get there. You do not say “start three copies of the payments service.” You write “the payments service should have three copies,” and the agent figures out what to do, whether that means starting new ones, stopping extra ones, or doing nothing because three are already running.

Why Teams Adopt It

Before GitOps, many teams deployed by running commands from laptops or from pipeline scripts. This works for a while, but problems appear as the system grows. Nobody can say for sure what is running in production. Two people change the same thing in different ways. A fix applied in an emergency is never written down and disappears the next time someone redeploys. New team members are afraid to touch anything because the real state lives only in the cluster and in a few people’s heads.

GitOps answers these problems directly. The repository always shows the intended state. Every change has an author and a reviewer. Rolling back means reverting a change in Git. Rebuilding a lost cluster means pointing a fresh cluster at the same repository. The result is a calmer, more predictable way of working.

The biggest practical difference between traditional pipelines and GitOps is who initiates the deployment: something outside the cluster, or something inside it.

The Traditional Push Model

In a classic continuous delivery pipeline, a build system compiles your code, runs tests, and then, as its final step, connects to the cluster and applies the new version. We call this push, because the pipeline pushes the change into the cluster. To do this, the pipeline needs credentials with the power to modify the cluster. Those credentials are stored in the build system, which usually has many users, many plugins, and a large attack surface.

Push also has a blind spot. After the pipeline finishes, it goes home. If someone later changes the cluster by hand, or a node crashes and loses an object, the pipeline knows nothing until the next run. The cluster can drift away from what was deployed, and nobody is told.

The GitOps Pull Model

In the pull model, an agent runs inside the cluster. It watches the Git repository and pulls changes when they appear. The pipeline’s job shrinks to building and testing the software and writing the new version number into the repository. It never needs cluster credentials. The agent, already inside the cluster, has the access it needs and nothing more.

flowchart LR
    subgraph Push["Push model"]
        P1["Code change"] --> P2["Build pipeline"]
        P2 --> P3["Pipeline holds cluster credentials"]
        P3 --> P4["Cluster"]
    end
    subgraph Pull["Pull model"]
        L1["Code change"] --> L2["Build pipeline"]
        L2 --> L3["Update Git repository"]
        L4["Agent inside the cluster"] --> L3
        L4 --> L5["Cluster"]
    end
        

The community behind GitOps has summarized the idea in four short principles. Both ArgoCD and Flux are built to follow them.

Why Each Principle Matters

Each principle closes a different gap. Declarative descriptions mean the agent can work out the steps itself, so people do not have to write fragile scripts. Versioned storage means that disagreements are settled by looking at history, not memory. Automatic pulling removes human delay and human forgetfulness. Continuous reconciliation is the safety net: even if something outside the process changes the cluster, the agent notices and acts.

At the heart of every GitOps tool is a simple repeating cycle. Understanding it makes the behavior of both tools easy to predict.

Observe, Compare, Act

A reconciliation loop is a cycle that repeats forever. First the agent fetches the latest version of the repository. Second, it renders the files into the final Kubernetes objects, which may involve expanding templates. Third, it reads the real objects currently in the cluster. Fourth, it compares the two. Fifth, if they differ, it acts by creating, updating, or deleting objects until the cluster matches. Then it waits a short time and begins again.

flowchart TD
    A["Fetch latest Git revision"] --> B["Render files into Kubernetes objects"]
    B --> C["Read live objects from the cluster"]
    C --> D{"Do they match?"}
    D -- "Yes" --> E["Report healthy and in sync"]
    D -- "No" --> F["Apply the difference"]
    F --> G["Check health of the result"]
    G --> E
    E --> H["Wait, then repeat"]
    H --> A
        

Two Words to Remember: Sync and Drift

When the cluster matches the repository, we say it is in sync. When something differs, we say it has drifted. Drift can happen for innocent reasons, such as a person using a command to scale a service during an incident, or for worrying reasons, such as an attacker changing a setting. Both tools can show you the drift. They can also be told to fix it automatically, a feature often called self-healing.

ArgoCD is a GitOps tool for Kubernetes known for its rich web interface and its clear picture of what is running and whether it matches Git.

What It Is

ArgoCD is a continuous delivery tool that you install inside a Kubernetes cluster. It started within the Argo family of projects, which also includes tools for workflows, event handling, and progressive delivery, and it is now a graduated project of the Cloud Native Computing Foundation. You tell it which Git repository and which folder hold your desired state, and which cluster and namespace should receive it. It then keeps the two in step.

The feature most people notice first is its web interface. It shows every application as a graph of connected objects: the Deployment, the ReplicaSets it created, the Pods inside them, the Services in front of them. Each object has a color showing whether it is healthy, and a badge showing whether it matches Git. For a newcomer, seeing this picture explains Kubernetes better than many pages of documentation.

What It Can Read

ArgoCD is not limited to plain files. It understands several ways of describing Kubernetes objects. It can read plain manifest files, Helm charts, which are packaged templates, Kustomize overlays, which let you customize a base description for different environments, and a few others. It converts them to final objects before comparing, so you can use whichever style your team prefers.

Behind the friendly screen, ArgoCD is a small team of cooperating programs, each with one clear job.

flowchart LR
    U["Users and CLI"] --> API["API server"]
    API --> CTRL["Application controller"]
    CTRL --> REPO["Repo server"]
    REPO --> GIT["Git repository"]
    CTRL --> K8S["Target cluster"]
    API --> CACHE["Redis cache"]
    CTRL --> CACHE
    API --> SSO["Single sign-on provider"]
        

How a Check Happens

By default, ArgoCD looks at the repository periodically, on the order of a few minutes, to see whether anything changed. You can make it react faster by configuring your Git host to send a notification the moment a commit lands. When a change is found, the repo server renders the files, the application controller compares the result with the cluster, and the application is marked either in sync or out of sync. If automatic syncing is enabled, the controller then applies the difference.

ArgoCD has a handful of core ideas. Once you know these words, its screens and messages will make sense.

The Application

The central object in ArgoCD is the Application. It is a small record, itself stored as a Kubernetes object, that connects a source to a destination. The source says which repository, which revision, and which folder or chart to read. The destination says which cluster and which namespace to deploy into. One Application usually represents one deployable unit, such as a single service or a bundle of tightly related pieces.

The Project

An AppProject is a fence that groups applications and limits what they may do. A project can restrict which repositories its applications may read from, which clusters and namespaces they may deploy to, and which kinds of objects they may create. This is how a platform team lets many teams share one ArgoCD safely. A team can deploy freely inside its own fence and nowhere else.

Two Separate Statuses

ArgoCD reports two independent things, and mixing them up is a very common beginner mistake.

An application can be perfectly synced and still degraded, for example when the configuration in Git is exactly what is deployed but the container keeps crashing. Likewise, an application can be healthy yet out of sync, for example when someone scaled it by hand. Always read both badges together.

Knowing that something is out of sync is only half the story. ArgoCD also lets you decide when and how the difference gets fixed.

Manual or Automatic Sync

By default an out-of-sync application waits for a person to press the sync button. This is manual sync, and it suits teams that want a human to confirm each change, especially in production. With automated sync, ArgoCD applies the difference by itself as soon as it detects one. Most teams use automatic sync in development and testing, and many choose either automatic or manual for production depending on their comfort level and change policy.

Two Switches That Change Behavior

Ordering with Waves

Some applications need their pieces created in a particular order. A database schema must exist before the service that uses it. A namespace must exist before objects inside it. ArgoCD handles this with sync waves: you label objects with a wave number, and ArgoCD applies lower numbers first, waiting for each wave to become healthy before starting the next.

Hooks for Special Moments

Hooks are short tasks that run at particular moments of a sync. A pre-sync hook runs before anything else, perhaps to back up a database. A post-sync hook runs after everything is healthy, perhaps to run a smoke test or send a message. A failure hook runs when a sync goes wrong, perhaps to start a cleanup. Hooks let you add the small procedural steps that pure descriptions cannot express.

Real organizations have dozens of services and several clusters. Creating each Application by hand does not scale, so ArgoCD offers two patterns to automate it.

The App-of-Apps Pattern

Since an Application is just a Kubernetes object, it can be stored in Git and deployed like anything else. In the app-of-apps pattern, you create one parent Application whose source folder contains the definitions of many child Applications. When you sync the parent, ArgoCD creates all the children, and each child then deploys its own workload. Adding a new service becomes a matter of adding one small file to the folder.

ApplicationSets

An ApplicationSet goes a step further. It is a template combined with a generator, a source of values that fills in the template to produce many Applications automatically. A generator might list clusters, so that the same service is deployed to every cluster. It might scan folders in a repository, so that every folder becomes an application. It might combine two lists, such as every service in every environment.

Multi-Cluster Deployment

A single ArgoCD can manage other clusters once they are registered with it. The central installation then holds access to each target cluster. Some organizations prefer to run one ArgoCD per cluster instead, which avoids a central point of risk but gives up the single overview. Both approaches are valid, and the choice depends on how much you value central visibility versus isolation.

Flux takes the same GitOps ideas and builds them as a set of small, focused controllers that live inside Kubernetes and speak its native language.

What It Is

Flux is a GitOps tool that is also a graduated project of the Cloud Native Computing Foundation. Its current generation is built as a collection of separate controllers, sometimes called the GitOps Toolkit. Each controller handles one concern, such as fetching sources, applying manifests, or releasing Helm charts. Together they form a complete delivery system, and you can install only the pieces you need.

Flux is designed to feel like part of Kubernetes itself. You configure it by creating ordinary Kubernetes objects, and you observe it using the same tools you use for everything else. There is no central server with its own database. The state lives in the cluster, in the objects the controllers manage.

Where It Shines

Flux is easiest to learn when you meet its controllers one at a time and see how they pass work to each other.

flowchart LR
    GIT["Git, OCI registry, or bucket"] --> SRC["Source controller"]
    SRC --> KC["Kustomize controller"]
    SRC --> HC["Helm controller"]
    KC --> K8S["Cluster objects"]
    HC --> K8S
    IMG["Image reflector and automation controllers"] --> GIT
    NC["Notification controller"] --> CHAT["Chat and alerts"]
    KC --> NC
    HC --> NC
        

Getting Started: Bootstrap

Flux comes with a command-line tool that performs a bootstrap. This one step installs the controllers into your cluster and also commits their own configuration into your repository. From that moment, Flux manages itself from Git: upgrading Flux means changing a file and letting Flux apply it. This self-managing property is one of the most elegant parts of the design.

Flux’s everyday work revolves around a few object types. Learn these and you can read almost any Flux setup.

Sources: Where Things Come From

A source object tells Flux where to look and how often. A Git source names a repository and a branch. An OCI source points to configuration packaged in a container registry, which is increasingly popular because registries are fast and well protected. A Helm repository source points to a collection of charts. A bucket source points to object storage. Each source produces a verified, versioned snapshot that downstream objects can use.

Kustomizations: How Content Is Applied

A Flux Kustomization connects a source to the cluster. It names a source and a path inside it, says how often to reconcile, and can specify extras: whether to delete objects that disappear from Git, which health checks to wait for, which other Kustomizations must finish first, and even which identity to use when applying. That last option is how teams are kept inside their own boundaries.

HelmReleases: Charts as Declared Objects

A HelmRelease describes a Helm chart installation as an object: which chart, which version, which settings. The Helm controller watches it and makes the real release match, upgrading when the description changes and optionally rolling back on failure. This turns what is normally a manual command into a reviewed, repeatable, Git-managed record.

Flux can close the loop between a new container image and the repository, and it can keep people informed without anyone watching dashboards.

Updating Git Automatically

When your pipeline builds a new container image, someone must update the repository so the new version is deployed. Many teams do this with a small script in the pipeline. Flux offers a built-in alternative. One controller scans your registry for new image tags. You describe a rule for which versions are acceptable, for example “newest within the stable series.” Another controller then edits the files in Git, commits the change, and pushes it. The usual reconciliation loop deploys the result.

The key point is that the change still passes through Git. The automation writes a commit like any person would, so the history stays complete and a rollback remains a simple revert.

Staying Informed

The notification controller sends messages about important events, such as a failed reconciliation or a successful deployment, to chat tools and other systems. You choose which events matter and where they go. It can also work in the other direction: it can receive a signal from your Git host or registry when something changes, so Flux reacts immediately instead of waiting for the next scheduled check.

Both tools follow the same principles and both are mature, graduated projects. The better choice depends on what your team values most.

The tools are the easy part. How you lay out your repositories decides whether GitOps feels pleasant or painful a year from now.

Application Code and Configuration: Keep Them Apart

The most widely recommended habit is to keep application source code in one repository and deployment configuration in another. The reason is practical. Code changes constantly, with many commits that have nothing to do with deployment. If configuration lives beside code, every code commit risks triggering the agent, and the history of deployments gets buried in noise. A separate configuration repository has a clean history in which every commit means “something about what runs has changed.”

There is a security reason too. The people who may edit code and the people who may approve production changes are not always the same. Separate repositories make it simple to give each group the right permissions.

Environments: Folders, Not Branches

A common beginner idea is to use one branch per environment, such as a development branch and a production branch. It looks tidy but tends to cause trouble: branches drift apart, merges become confusing, and it is hard to see at a glance how environments differ. Most experienced teams prefer one branch with a folder for each environment. A shared base holds what is common, and small overlays in each environment folder hold only the differences. Comparing environments becomes a simple comparison of folders.

flowchart TD
    R["Configuration repository"] --> B["base: shared description"]
    R --> E1["environments: development"]
    R --> E2["environments: staging"]
    R --> E3["environments: production"]
    B --> E1
    B --> E2
    B --> E3
    R --> I["infrastructure: shared services"]
    R --> C["clusters: what each cluster should run"]
        

If Git holds everything, what about passwords and keys? The answer is never to put them there as plain text.

The Problem

Git history is permanent and widely copied. A password committed once, even if deleted in the next commit, remains in history and in every clone. It is also visible to everyone with read access to the repository. At the same time, GitOps wants the cluster to be fully described by the repository. These two facts seem to clash, but the community has found three reliable solutions.

Choosing an Approach

Plain files become repetitive quickly. Helm and Kustomize are two popular ways to avoid repetition, and both tools support them well.

Kustomize: Layers of Customization

Kustomize starts with a plain base description and applies layers of changes on top, called overlays. A production overlay might raise the number of copies and set larger memory limits, while a development overlay keeps everything small. The base stays clean and readable, and each overlay is short. Because Kustomize works with ordinary Kubernetes descriptions and needs no special syntax, many teams find it the most natural fit for GitOps.

Helm: Packaged Templates

Helm packages an application as a chart, a bundle of templates and default settings. You supply a small set of values to customize it. Helm is very popular for installing third-party software, because most open-source projects publish a chart. It is also used for in-house applications where many similar services share one template.

How the Two Tools Treat Them

ArgoCD renders Helm charts into plain objects and then applies and tracks those objects, rather than running a traditional Helm install. This means it sees and compares each individual object, which gives its interface a detailed view. Flux, by contrast, drives real Helm releases through its Helm controller, so the usual Helm history and rollback features remain available. Neither approach is wrong. They differ in which familiar feature set you keep.

Getting a change to production safely means moving it through environments and, ideally, releasing it gradually instead of all at once.

Promoting Between Environments

Promotion means moving a tested version from one environment to the next. In GitOps this is simply a change in Git: the version number in the staging folder is copied into the production folder through a reviewed change. Because both are just files, the pull request shows exactly what is being promoted, who approved it, and when. Some teams automate the first steps, such as development to staging, and keep production as a deliberate human approval.

Releasing Gradually

Even a well-tested change can surprise you in production. Progressive delivery reduces the risk by exposing the new version to a small share of users first, measuring how it behaves, and only then widening the rollout. Two common styles exist. In a canary release, a small percentage of traffic goes to the new version while the rest stays on the old one. In a blue-green release, the new version runs alongside the old one and traffic is switched over all at once after checks pass, with a quick way back.

Both GitOps tool families have companions for this. The Argo family offers Argo Rollouts, and the Flux family offers Flagger. They plug into the same Git-driven flow: you declare the rollout strategy in Git, and the companion controls the gradual traffic shift and automatic rollback on bad metrics.

GitOps changes where the power sits. Securing the repository and the agent becomes as important as securing the cluster.

Git Is Now the Control Plane

If the repository decides what runs in production, then anyone who can merge to it can change production. Protect the repository accordingly. Require reviews, restrict who can approve production changes, use protected branches so nobody can push directly, and consider requiring signed commits so that changes can be tied to verified identities. The review process you already know from code becomes your change-approval process.

The Agent’s Power

The agent inside the cluster usually has broad permissions, since it must create all kinds of objects. Limit it where you can. Use projects in ArgoCD or impersonation in Flux so that each team’s changes are applied with that team’s limited identity instead of a cluster-wide one. Run the agent with the least privilege that works, and restrict who can reach its interface or its objects.

A tool that deploys everything must itself be dependable. Plan for its failure and for the failure of the clusters it manages.

What Happens If the Agent Is Down?

The good news is that your applications keep running. The agent only reconciles; it is not in the path of user traffic. If it stops, nothing breaks immediately. You simply lose automatic updates and drift correction until it returns. Once it comes back, it catches up with the repository. Still, you should monitor the agent and alert when it stops, because a silent agent means silent drift.

Rebuilding from Nothing

One of GitOps’s greatest gifts is simple disaster recovery. If a cluster is destroyed, you create a new one, install the agent, and point it at the same repository. The agent recreates everything that Git describes. The speed of recovery depends on how much of your state lives in Git. Data in databases and volumes is not in Git, so it needs its own backup plan, but your configuration comes back automatically.

Scaling to Many Applications

As the number of applications grows into the hundreds, the agent must read repositories, render files, and compare many objects. Both tools can be scaled: ArgoCD by spreading its controller work across several instances, and Flux by running controllers with more resources or sharding. Keep individual repositories and applications reasonably small, because a huge application takes longer to render and compare and makes every change riskier.

A GitOps system should tell you when it is unhappy, ideally before your users do.

What to Watch

Both tools publish metrics in a standard format that monitoring systems understand. These tell you how many applications are in sync, how many are failing, how long reconciliations take, and how often the agent encounters errors. Chart these and set alerts on the important ones. A sudden rise in failures or a flat line of no activity both deserve attention.

Telling People

Connect notifications to the chat tools your teams already use. Useful messages include a deployment started, a deployment succeeded, a sync failed with its reason, and an application degraded after a change. Send each message to the team that owns the application, not to a crowded shared channel, so that alerts are acted on instead of ignored.

flowchart LR
    A["GitOps agent"] --> M["Metrics"]
    A --> L["Logs"]
    A --> N["Notifications"]
    M --> D["Dashboards and alerts"]
    L --> T["Troubleshooting searches"]
    N --> C["Team chat channels"]
    G["Git history"] --> H["Audit trail of who changed what"]
        

Teams that adopt GitOps tend to stumble in the same places. Knowing them in advance saves months.

Best Practices at a Glance

Most GitOps problems fall into a handful of patterns. A calm checklist finds the cause faster than guesswork.

Problem: It Is Always Out of Sync

This is the most common puzzle. You merge a change, the agent applies it, and a moment later the application is out of sync again. Usually another system is changing the same field. An autoscaler may adjust the number of copies, an admission webhook may add default values, or a controller may write extra information into an object. Each time the agent compares, it sees a difference and tries to correct it, while the other system changes it back.

The cure is to tell the tool which fields to ignore, or better, to stop describing those fields in Git. If an autoscaler controls the number of copies, do not also hard-code that number in the repository. Both tools offer a way to ignore specific differences.

Problem: The Change Never Arrived

Work from the outside inward. Did the change actually reach the branch the agent watches? Can the agent still log into the repository, or has a token expired? Did it fetch the new revision? Did rendering the files fail because of a syntax mistake or a missing value? Both tools report these errors on the application or source object, so read the status message before anything else.

GitOps is easier to adopt in steps than in one dramatic switch. Here is a path many teams have followed successfully.

A Little History

The term GitOps was popularized around 2017 by the team at Weaveworks, who also created the original Flux. ArgoCD grew out of the Argo project family and was shaped heavily by engineers at Intuit. Both projects are now community-driven and have reached the graduated level in the Cloud Native Computing Foundation, a sign of broad adoption and maturity. Companies in many industries, from finance to retail to telecommunications, run them in production.

The Adoption Path

If you are still unsure where to start, this short path points toward a sensible first choice.

flowchart TD
    S["Starting with GitOps"] --> Q1{"Do you want a built-in visual interface?"}
    Q1 -- "Yes" --> A["Lean toward ArgoCD"]
    Q1 -- "No or not important" --> Q2{"Do you prefer everything as native Kubernetes objects and small parts?"}
    Q2 -- "Yes" --> F["Lean toward Flux"]
    Q2 -- "No" --> Q3{"Do many teams share one central platform?"}
    Q3 -- "Yes" --> A
    Q3 -- "No" --> Q4{"Do you want built-in image update automation?"}
    Q4 -- "Yes" --> F
    Q4 -- "No" --> E["Try both in a test cluster for a week"]
        

Reading the Guide

The guide is only a nudge. If your developers and managers want to see the state of everything at a glance, ArgoCD’s interface is a strong reason to choose it. If you value a minimal, composable system that feels like a natural extension of Kubernetes, Flux is attractive. If a central platform team serves many squads, either works, with ArgoCD offering friendlier tools for central visibility and Flux offering strong tenant isolation through native access control. When honestly undecided, a week of hands-on trial in a throwaway cluster is worth more than any article.

In GitOps, a merged change goes live quickly. That makes checking the change before the merge one of the most valuable habits you can build.

Catch Mistakes in the Review Stage

Because the agent applies whatever reaches the watched branch, an error in the repository becomes an error in the cluster. The cheapest place to catch it is before the merge, while the change is still only a proposal. Most teams add automatic checks to every proposed change. These checks confirm that the files are valid, that templates render without errors, that the final objects follow the expected structure, and that company policies are respected, such as having resource limits and approved image sources.

A particularly helpful check is a rendered difference. Since many repositories use templates and overlays, the files that a reviewer reads are not the final objects. A rendering step produces the final objects for both the old and new versions and shows the difference between them. The reviewer then sees exactly what will change in the cluster, not just which template lines were edited. This often reveals surprises, such as a small value change that affects twenty objects.

Previews and Test Environments

Some teams go further and create a temporary environment for each proposed change. The agent deploys the proposal into a short-lived namespace, tests run against it, and the environment disappears when the proposal is merged or closed. This gives reviewers a real, running copy to try instead of only reading files. It costs more resources, so teams usually reserve it for changes that are risky or that touch many users.

Drift is not always a villain. Understanding why it appears helps you decide whether to fix it, tolerate it, or redesign around it.

Where Drift Comes From

Drift appears for several different reasons, and each calls for a different response. Some drift comes from people: an engineer changes a live object during an incident. Some comes from other automation: an autoscaler changes the number of copies, or a certificate tool rewrites a secret. Some comes from the cluster itself: it fills in default values for fields that your files left empty, and these defaults then look like differences. And some comes from attackers, though this is the rarest cause. Treating all drift the same way leads either to constant noise or to missed warnings.

Ownership of Fields

A helpful mental model is that every field of every object has exactly one owner. If Git owns the number of copies, then nothing else should change it. If an autoscaler owns it, Git should not mention it. Problems arise when two owners claim the same field, and the agent and the other system fight. Clearing up ownership, rather than adding more exceptions, is the lasting cure for most never-ending sync problems.

GitOps brings real benefits, but it also asks for changes in habits and a small amount of extra machinery. An honest view helps teams plan well.

The Human Side

The hardest part of adopting GitOps is usually not technical. People who are used to fixing things directly feel slowed down by the requirement to propose, review, and merge every change. The answer is to make the Git path fast and pleasant: quick reviews, helpful automatic checks, and a clear break-glass procedure for real emergencies. When the proper path is easy, people use it willingly, and the benefits become visible within weeks.

When It Might Not Be Worth It

For a tiny setup with a single small cluster and one or two engineers who deploy rarely, the extra moving parts may outweigh the gain. Even then, many people adopt a light version because the benefits of history and recoverability appear early. The decision should reflect how many people change the cluster, how often they do so, and how costly a mistake or an outage would be.

The ideas apply anywhere you can describe desired state and reconcile it, but the mature tools discussed here are built for Kubernetes, which is naturally declarative and so fits very well.

Yes. You still build, test, and scan your software. GitOps changes the final step: the pipeline updates the repository, and the agent inside the cluster does the deployment.

They keep running. The agent only reconciles configuration and is not in the path of user traffic. You lose automatic updates and drift correction until it returns, so monitor it and alert if it stops.

Revert the change in Git. The agent then applies the previous state. This is usually faster and safer than hand-crafted rollback commands, and the revert itself becomes part of the history.

Most experienced teams use a single main branch with a folder for each environment and share a common base. Long-lived environment branches tend to drift and make comparisons confusing.

Never in plain text in Git. Use encryption before committing, encryption with a key service, or an external secrets manager referenced from Git. Pick the one that fits your compliance and operating model.

It reverts manual changes to match Git. It is safe when your team understands it and follows the rule that all changes go through Git. Introduce it gradually and tell people before switching it on.

Another system is probably modifying the same fields, such as an autoscaler or an admission webhook. Stop describing those fields in Git or tell the tool to ignore those specific differences.

Technically yes, but not on the same objects, because they would fight each other. Some organizations use one for platform components and the other for applications, but running one tool is simpler for most teams.

Both poll on a configurable schedule, typically every few minutes by default. You can make them react almost instantly by having your Git host notify the agent when a commit lands.

With ArgoCD, one central instance can manage many clusters, and ApplicationSets generate applications per cluster. With Flux, you usually run an instance in each cluster that follows the same repository, with definitions that say what each cluster should run.

No. GitOps ensures that what is in Git is applied, but it does not judge whether it is safe. Keep admission rules, policy checks, scanning, and access control in place alongside it.

No. Git holds configuration, not the contents of databases or volumes. Those need their own backups and recovery plan, even though your configuration can be restored automatically.

Pick one low-risk application in a development cluster, put its configuration in a new repository, install either tool in report-only mode, and watch what it tells you. Expand only after you are comfortable.

GitOps makes a Git repository the single source of truth for what runs in your cluster, and uses an agent inside the cluster to keep reality matching it. ArgoCD offers a rich interface and central control, while Flux offers a Kubernetes-native toolkit of small controllers. Both follow the same four principles, and both turn deployment, rollback, and recovery into ordinary Git operations. Success depends less on the tool and more on good habits: separate repositories for code and configuration, folders for environments, safe secret handling, reviewed promotion, careful security, and clear monitoring. Start small, learn how drift and reconciliation behave, and grow step by step.