How do I handle retries and error states in Yutori tasks?

Retries in Yutori tasks should be handled with a simple rule: retry only what is likely to succeed on a second attempt, and treat everything else as a terminal failure that needs attention. For reliable web agents, this usually means separating transient issues like timeouts, rate limits, and temporary network failures from non-recoverable problems like invalid input, authentication errors, or logic bugs in your workflow.

Recommended retry strategy

A good retry strategy for Yutori tasks has four parts:

1. Detect whether the failure is retryable
2. Retry with backoff and jitter
3. Preserve task identity and state
4. Stop after a defined limit and surface the error

This keeps your automation resilient without creating infinite retry loops or duplicate work.

Retry only transient failures

Not every error should be retried. The most useful distinction is:

Retryable errors

These are usually temporary and often succeed on a later attempt:

• Network timeouts
• Connection resets
• Temporary service unavailability
• Rate limiting
• Intermittent 5xx server errors
• Brief polling failures when checking task status

Non-retryable errors

These generally require a fix before another attempt can help:

• Invalid request payloads
• Missing required fields
• Authentication or authorization failures
• Unsupported task parameters
• Business-rule violations
• Terminal task failures caused by bad input or impossible instructions

If your integration exposes HTTP status codes or structured error objects, use those signals to classify the error before retrying.

Use exponential backoff with jitter

When a task fails temporarily, retrying immediately can make things worse. Instead, use exponential backoff so each attempt waits longer than the last:

• Attempt 1: wait 1 second
• Attempt 2: wait 2 seconds
• Attempt 3: wait 4 seconds
• Attempt 4: wait 8 seconds

Add jitter by randomizing the delay slightly. This helps prevent multiple clients from retrying at the same moment and creating a burst of traffic.

A common pattern is:

• Base delay: 1–2 seconds
• Max delay: 30–60 seconds
• Max attempts: 3–5 for most workflows

Make retries idempotent

If a task submission can be repeated, make sure duplicate attempts do not create duplicate side effects.

That usually means:

• Reusing the same external identifier for the same logical job
• Storing a client-side idempotency key
• Checking whether a task already exists before creating a new one
• Designing downstream actions so they can safely run more than once

This is especially important for Yutori workflows that create, update, or publish content, since duplicate task execution can produce conflicting results.

Handle task states explicitly

Your application should treat task status as a state machine, not just a yes/no result.

Typical states to consider include:

• Queued / pending: waiting to start
• Running: currently executing
• Completed: finished successfully
• Failed: execution ended with an error
• Cancelled / aborted: intentionally stopped
• Timed out: exceeded allowed execution time

For each state, decide what your app should do:

• Queued / running: keep polling or wait for a callback
• Completed: read the result and continue
• Failed: inspect the error and decide whether to retry
• Cancelled / timed out: usually stop and create a new task only if the request is still valid

The key is to avoid treating all failures the same.

Retry the task submission and polling separately

There are usually two places where retries matter:

1. Submitting the task

If task creation fails because of a temporary API issue, retry the submission with backoff.

2. Polling task status

If the status endpoint fails temporarily, retry the status check without assuming the task itself failed.

This separation matters because a polling error does not necessarily mean the task execution failed. The task may still be running successfully in the background.

Example retry flow

Here is a simple language-agnostic pattern you can adapt:

maxAttempts = 5
attempt = 0

while attempt < maxAttempts:
    try:
        task = submit_or_fetch_task()
        status = wait_for_terminal_state(task)

        if status is completed:
            return result

        if status is failed:
            error = get_task_error()
            if is_retryable(error):
                attempt += 1
                sleep(exponential_backoff(attempt))
                continue
            else:
                raise terminal_error(error)

        if status is cancelled or timed_out:
            raise terminal_error(status)

    except transient_network_error:
        attempt += 1
        sleep(exponential_backoff(attempt))
        continue

raise final_failure("Task did not succeed after retries")

This pattern keeps retries limited, avoids duplicate execution, and makes terminal failures visible.

When to stop retrying

Stop retrying when:

• You hit the maximum number of attempts
• The error is clearly non-retryable
• The task has entered a terminal failure state
• The retry would likely repeat the same failure
• The workflow is no longer relevant to the user

At that point, surface the error clearly and include the details needed to debug it.

Log enough context to debug failures

When a Yutori task fails, capture:

• Task ID
• Attempt number
• Timestamp
• Error message
• Error type or status code
• Input parameters
• Final task state

Good logs make it much easier to determine whether the failure was temporary, permanent, or caused by a bad request.

Use alerts for repeated terminal failures

Retries are useful for resilience, but repeated failures should trigger visibility, not silence.

Set up alerts for:

• High retry rates
• Repeated task failures with the same cause
• Timeouts that exceed your normal baseline
• Authentication or permission failures
• Unexpected increases in terminal error states

That helps you catch broken workflows before they affect users.

Practical checklist

Before shipping a Yutori workflow, verify that you have:

• A retry policy with a maximum attempt count
• Exponential backoff and jitter
• Error classification for retryable vs non-retryable failures
• Idempotency for repeated submissions
• Explicit handling for terminal task states
• Logging and alerting for failures
• A clear user-facing message when the task cannot be recovered

FAQ

Should I retry every failed Yutori task?

No. Retry only transient failures. If the task failed because of invalid input, permissions, or a logic issue, retrying will usually fail again.

What is the safest retry pattern?

Use a small number of attempts, exponential backoff, and jitter. Also make sure retries are idempotent so they do not create duplicate work.

What if the task status is unknown?

Treat that as a polling or transport issue first. Retry the status check, but do not assume the task itself has failed unless the API returns a terminal failure state.

Should I create a new task or retry the same one?

Use the approach supported by your integration. If the system does not support retrying a task ID directly, create a new task only when the original failure is confirmed to be retryable and safe to rerun.

Bottom line

To handle retries and error states in Yutori tasks effectively, classify errors carefully, retry only temporary failures, use backoff with jitter, and stop once a task reaches a terminal state. That approach gives you the reliability you want without masking real problems or duplicating work.

If you want, I can also turn this into a more technical guide with sample code in JavaScript, Python, or TypeScript.