Reliable Production AI: Durable Workflows

Building reliable production AI with durable workflows

Moving an AI application from prototype to production introduces complexity well past simple prompt-and-response. Prototypes are usually stateless and independent. Production systems have to manage long-running operations, partial failures, retries, and updates across thousands of records or process steps, and the hard problem stops being model performance and becomes the reliability of the execution infrastructure underneath.

Production AI systems run on long workflows rather than isolated model calls. Those workflows need coordinated execution state, error handling, recovery, and progress tracking across a lot of independent operations. How you design them affects overall reliability more than which AI model you picked.

The pitfall of prototype mentality in production

Many AI systems start with a mental model where a request comes in, a prompt gets built, the model responds, and the application moves on. That holds up for independent, short-lived requests. It falls apart when the work spans thousands of operations, each with its own dependencies, latencies, transient failures, or external tool calls.

Take generating AI output for 10,000 records. What looks like one request is a workflow. Some operations finish instantly. Others hit rate limits, validation errors, or slow tool executions. If a worker restarts or a deployment lands mid-execution, the system has to work out what completed, what is still running, and what can be retried safely without duplicating work. At that point you have stopped debugging AI behavior and started debugging distributed execution.

Identifying the true unit of work

A single row failing looks like an isolated failure, and that appearance hides the real problem: the execution itself has no explicit representation. When a worker crashes mid-process, reconstructing progress from logs and partial outputs is painful. So the question to answer is what the smallest meaningful unit of recoverable work is.

Treat the whole execution as one unit and you risk re-running thousands of successful operations. Break every tiny operation into its own workflow and the orchestration overhead becomes unmanageable. What works is defining recovery boundaries that match how engineers and users perceive progress.

Say a batch job is processing 10,000 records and a worker crashes on record 8,432. If the system does not track the completion of each record as a distinct unit, there is no way to tell whether record 8,432 genuinely failed or whether the operations before it completed. Retrying the whole batch would be wasteful.

Durable workflows as the foundation of reliability

Handling complexity and failure comes down to treating execution state as durable. Rather than relying on a running process to remember where it got to, durable workflows preserve execution history externally, independent of any single worker's lifecycle.

When a worker restarts, a new worker resumes from the last known durable state instead of reconstructing progress out of scattered logs and partial data.

The architectural pattern splits three ways. A workflow orchestration layer manages the lifecycle of the whole process. Activities are the discrete, executable units of work inside the workflow. Durable state storage persists the history and current state of workflows and activities outside the lifespan of individual workers.

In a system built with Temporal, a column execution could be the parent workflow. Row or batch-level operations become child workflows or activities with their own retry policies. If a worker fails, another worker picks up the execution by querying the durable state of the parent workflow and its activities.

// Conceptual example illustrating workflow and activity definition

// Workflow definition
type MyWorkflow interface {
	Execute(ctx workflow.Context, input string) (string, error)
}

// Activity definitions
type MyActivity interface {
	ProcessRecord(ctx activity.Context, recordID string) error
}

// Within the workflow, you might call activities multiple times
func (w *myWorkflowImpl) Execute(ctx workflow.Context, input string) (string, error) {
	// ... logic to iterate through records ...
	err := workflow.ExecuteActivity(ctx, w.ProcessRecord, recordID).Get(ctx, nil)
	if err != nil {
		// Handle activity failure, potentially with retries configured at activity level
		return "", err
	}
	// ... more logic ...
	return "Success", nil
}

Aligning retries with execution boundaries

Durable workflows give you precise control over retries. Instead of retrying an entire long-running process, you scope retries to specific activities or smaller units of recoverable work, which keeps completed operations from re-running and conserves resources like AI model quota.

A retry strategy needs boundaries that line up with the smallest logical unit of recoverable work. It needs configured policies, so exponential backoff, maximum attempts, and non-retryable error types. And the operations have to be idempotent, so that retrying one several times has the same effect as running it once.

If a model call times out while processing a single row, only that activity should be retried. Thousands of successful operations on other rows should not re-execute.

Building user trust with visible progress

Reliability is also about user confidence. Long-running AI operations need transparent status reporting, and a generic "Running..." message gives users nothing to go on. Granular updates do: cell-level status for individual generated values, row-level aggregation summarizing the work for a single record, column-level execution representing what the user actually asked for, and a worksheet-level summary of the whole operation.

Expose execution status at those levels and users can see that the system knows where it is and can manage the operation to the end.