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Understanding the distinction clarifies why Kubernetes can recover from drift, how custom resources are acted on, and why reliable controllers must be idempotent, queue-driven, and explicit about status and retries.
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What a reconciliation loop does
Kubernetes treats the desired state of a resource as data stored through its API. A reconciliation loop reads that desired state, observes the cluster’s actual state, and performs actions that move reality closer to the declaration. The Kubernetes project defines a controller as “a reconciliation loop that reads the desired state of a resource from the Kubernetes API and takes action to bring the cluster’s actual state closer to the desired state.”
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The loop is intentionally continuous rather than a one-time deployment script. A pod can be deleted, a dependent object can change, or an external service can become unavailable after the original request succeeds. Each new observation gives the controller another chance to correct drift. Repeated passes converge on the declared state when the implementation is correct and the surrounding systems can satisfy the request.
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Desired state versus observed state
The desired state is normally represented in a resource specification: for example, the count, image, configuration, or relationship a user requests. Observed state includes the resources that exist now and the conditions those resources report. Reconciliation compares the two and determines whether anything needs to be created, updated, or deleted.
A well-designed controller does not blindly reapply every operation. It calculates the difference and changes only what is necessary. That reduces API traffic, limits disruptive updates, and makes a repeated pass safe after a timeout or process restart.
How one reconciliation pass works
Most controllers follow the same event-to-queue-to-reconcile pattern. The exact implementation differs, but the sequence below describes the common flow.
- An object changes. A user, an admission process, or another controller creates or updates a watched object. A change to a dependent object can also matter if the controller watches that dependency.
- A watch creates work. Kubernetes watch notifications are converted into a reconcile request. In common Go controller implementations, the request is a namespace/name key rather than a copy of the entire object.
- A worker reads current data. The reconciler retrieves the object and related resources from the API or a local cache. It must use current observations instead of trusting the event payload, because several events may be coalesced or arrive out of order.
- The controller compares states. It evaluates the specification, related resources, and any external observations, then decides which creates, updates, or deletes are needed.
- It applies the minimum changes. The implementation performs the required API operations and, where appropriate, calls an external system. Ownership and dependency relationships determine which objects belong to this reconciliation.
- It records the result. The controller writes status or conditions, handles errors, and can request that the key be processed again later. A successful pass may also requeue when a follow-up observation is expected.
The queue allows many events for the same object to collapse into useful work while workers process different keys. The important contract is not “run once for every event”; it is “eventually evaluate the current state and converge.”
Controllers: the general Kubernetes mechanism
“Controller” is the broad term for a Kubernetes control-loop component. Built-in controllers run inside kube-controller-manager and manage core resources. Kubernetes also supports custom controllers running as separate workloads. A custom controller can manage built-in resources, custom resources, or systems outside the cluster.
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Built-in controllers
Built-in controllers implement the platform’s standard behavior. They watch the resources assigned to them and create or adjust related objects so the cluster honors the built-in API’s semantics. Their code is delivered as part of Kubernetes components rather than installed as an application-specific package.
Custom controllers
A custom controller is deployed and operated like another workload. It can watch an existing resource type when the required behavior is missing from Kubernetes, or it can watch a custom resource that defines a domain-specific API. A controller that manages an external database, storage service, or other system can still use the Kubernetes API as its source of desired state while reporting external observations back into resource status.
Custom controllers own the reconciliation semantics: which events matter, which resources are dependencies, what constitutes success, and how failures are retried. They should also define what happens when a watched object is deleted or when a dependency is temporarily unavailable.
What makes an operator different
An operator is a specialized controller pattern. It combines one or more controllers with custom resources and encodes operational knowledge that would otherwise remain in a runbook or a human operator’s memory. Kubernetes documentation describes the pattern this way: “Operators follow Kubernetes principles, notably the control loop.”
The distinction is about scope and knowledge, not a separate execution engine. Every operator relies on controller behavior—watching, queueing, reconciling, updating status, and retrying. The operator label becomes useful when the controller understands an application’s lifecycle rather than merely maintaining a generic platform object.
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What a CRD contributes
A CustomResourceDefinition (CRD) adds a type to the Kubernetes API. The custom resource instance then carries the desired specification that the operator reads. The operator’s controller watches those instances and usually watches the secondary resources it creates or depends on.
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The CRD is the API surface; the controller is the behavior behind it. Creating a CRD alone does not deploy software, perform backups, or recover a failed service. Those actions occur only when a running controller observes the custom resource and implements the corresponding reconciliation logic.
Operational knowledge operators can encode
An application-focused operator can coordinate tasks such as:
- Deploying and scaling the application’s components.
- Taking or scheduling backups and checking their outcomes.
- Performing version upgrades in an order that respects application dependencies.
- Applying schema changes and recording whether migrations succeeded.
- Recovering from failures by recreating resources or invoking an external recovery procedure.
These capabilities are not implied by the word operator. They exist only when the operator’s code implements them and its custom API exposes the required configuration and status.
Where “agent” fits—and where it does not
Kubernetes does not define “agent” as one standardized primitive equivalent to a controller or operator. Teams use the term for several kinds of auxiliary process, such as a daemon that collects observations, an automation client that submits changes, or a helper that performs work on behalf of another component.
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For clarity, describe the behavior rather than relying on the label. An agent may observe Kubernetes state or issue API calls, but it is not automatically the owner of reconciliation semantics. A controller owns the decision loop: it maps observed state to desired actions, tracks dependencies, reports status, and determines retry behavior. An external automation client might trigger a change and exit, leaving a controller to converge the result; a helper agent might execute one operation while the controller remains responsible for the overall resource.
Controllers, operators, and agents compared
| Aspect | Controller | Operator | Agent |
|---|---|---|---|
| Meaning | General Kubernetes reconciliation component | Controller pattern with custom resources and domain-specific operational knowledge | Non-standard term for an auxiliary process or automation client |
| State it may manage | Built-in resources, custom resources, or external systems | Usually application-specific custom resources plus their dependencies | Whatever state its implementation observes or changes |
| API surface | Existing Kubernetes resources or CRDs | CRDs commonly define the application API | May use the Kubernetes API, another API, or both |
| Operational knowledge | Generic or component-specific rules | Deployment, backup, upgrade, schema, and recovery procedures encoded in software | Task-specific behavior; no standard Kubernetes contract |
| Reconciliation ownership | Owns the event, queue, comparison, and retry semantics | Owns those semantics for the application lifecycle | Not inherently responsible for convergence |
| Typical deployment | Built into Kubernetes or deployed as a workload | Deployed as an application controller, often with CRDs | Runs wherever its auxiliary task requires |
Retries, requeues, and idempotence
Errors are normal in a distributed system. An API request can time out after the server accepted it; a dependency can be unavailable for several minutes; or an external service can return a transient failure. A controller therefore needs a deliberate retry model. It can return an error for framework-managed retry or explicitly requeue a key for later processing when waiting is expected.
Retries make idempotence essential. An idempotent reconcile function can run again with the same desired state without duplicating resources or causing harmful side effects. Before creating an object, the controller should determine whether the intended object already exists. Before updating, it should compare the relevant fields. For an external action, it should use a stable identity or another mechanism that lets the operation be recognized as already completed.
Status and conditions
Status is the controller’s report of what it observed and what remains to be done. Conditions can communicate states such as progressing, ready, degraded, or failed, together with a reason and useful detail. Status must describe observations rather than silently treating an attempted API call as success. Updating status also creates another event, so the controller should avoid writing unchanged values on every pass.
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Convergence versus hot loops
A controller should requeue when more work is genuinely pending, not as a substitute for state comparison. Repeatedly writing the same spec or status can generate needless events and consume worker capacity. Conversely, failing to requeue after an asynchronous operation can leave the resource stuck until an unrelated event arrives. The correct interval depends on what is being observed and how quickly it can change.
Designing a reliable reconciliation loop
When choosing between a simple controller, an operator, and an auxiliary agent, evaluate the design on concrete axes:
- State scope: Is the source of truth a built-in resource, a CRD, an external system, or a combination?
- API surface: Can an existing Kubernetes type express the desired state, or does the application need a CRD?
- Operational knowledge: Are there ordered upgrades, backups, schema changes, or recovery actions that should be automated?
- Watch and dependency graph: Which objects and external observations can change the outcome, and how will those changes enqueue the owning resource?
- Idempotence and retries: Can every action be safely repeated after a timeout, restart, or duplicate event?
- Status reporting: Can users tell whether the resource is progressing, ready, or blocked, and why?
- Deployment ownership: Which team installs, upgrades, monitors, and removes the controller and its CRDs?
Keep the reconcile function organized around current observations. Read the primary object, fetch dependencies, calculate the desired child or external state, apply changes, and then report status. Separating those stages makes it easier to test drift, retries, and partial failure.
Building a controller or operator
Operator SDK documents a workflow for projects written in Go, Ansible, and Helm:
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- Define the custom resources and generate their CRDs.
- Define the controller watches and ownership relationships.
- Implement the reconcile logic, including status, errors, and requeue decisions.
- Generate deployment manifests and install the controller in a cluster.
For Go implementations, controller-runtime provides much of the event-to-request and worker-queue foundation used by controllers. The framework does not decide your application’s desired state or make non-idempotent code safe; those responsibilities remain in the reconciler.
A practical operator flow
Consider an application custom resource whose specification asks for a particular deployment, backup policy, and version. A user creates or edits that resource. The operator’s watch enqueues its namespace/name key, then the reconciler reads the current custom resource and the child resources representing the deployment and backup components.
If the deployment is missing, the operator creates it. If the deployment exists with a different image or replica setting, it updates only the relevant fields. If a backup is running, the operator records progress rather than starting a duplicate backup. If an upgrade requires a schema change, it performs the documented sequence and reports failure in a condition when migration cannot complete. Once observations match the specification, the operator marks the resource ready. A later event—such as a deleted child or failed backup—starts another pass and restores or reports the new state.
Common failure modes and diagnosis
- Nothing happens after a custom resource is created: Check that the CRD exists, the controller is running, and the watch includes the resource kind and namespace.
- The controller creates duplicates: Review idempotence and stable naming. A retry must recognize an object or external action that already succeeded.
- Status never becomes ready: Confirm that the controller observes the dependency it is waiting for and requeues when that dependency changes.
- API traffic or CPU is excessive: Look for unconditional writes, broad watches, or a hot loop that requeues without a state transition.
- Changes appear to be ignored: Verify that the reconciler reads the current object and that the changed field is part of the comparison and watch path.
- An external service remains out of sync: Distinguish a transient error from a permanent one, record the reason in status, and ensure retries cannot repeat a destructive operation.
Logs should identify the resource key, the phase of reconciliation, and the reason for a retry without exposing sensitive configuration. Conditions should give users enough information to decide whether to wait, correct the specification, or investigate a dependency.
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Use controller for the general Kubernetes control-loop role. Use operator when that role is paired with CRDs and application-specific operational expertise. Use agent only with an explicit definition, because Kubernetes assigns no single standard meaning to it. In every case, the quality of the system depends on the same fundamentals: observe current state, make minimal changes, report status, retry safely, and design each pass to converge.
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