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Events are signals from your application that trigger rule evaluation. You send events via the API, and Scrip evaluates applicable ACTIVE rules across every rule set against one event-start state snapshot. Events can also come from automations, which generate events on a schedule or in response to participant state changes.

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Sending an Event

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Timestamps

Every event carries two timestamps: These serve different roles:
  • event_timestamp is the logical clock. It becomes the now variable in CEL expressions, so rule conditions that compare against time use the event’s occurrence time, not the current time. This keeps evaluation deterministic across retries and reprocessing.
  • created_at is the ingestion clock. The from and to query parameters on list endpoints filter on created_at, not event_timestamp. This makes incremental polling reliable: you can track “give me everything since my last sync” without missing late-arriving events. To filter by when events actually occurred, use the event_from and event_to parameters instead. Both pairs can be used simultaneously (AND semantics).
Because event_timestamp is customer-supplied, it can differ from created_at. A batch import might backdate events to last month, or clock skew might push timestamps slightly into the future. Rules evaluate against the current rule definitions regardless of event_timestamp. A backdated event runs against today’s rule definitions, but active_from / active_to windows are checked against the event’s event_timestamp, so a backdated event inside a past window still fires time-windowed rules. See Time-Windowed Rules.

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Processing Pipeline

Events are processed asynchronously. The API confirms receipt, not validity. Business validation (program existence and status, participant resolution) and rule evaluation happen in the background. Existing participants are automatically enrolled in the target program if not already members; see Programs: Enrollment for enrollment behavior and the on_unknown_participant setting. Validation errors surface via event.failed webhooks.
When a worker picks up an event, it loads the program’s rule sets and applicable ACTIVE rules. Sets run by ascending set order, and rules within each set run by ascending rule order. Actions from matching rules persist sequentially in that order within the same transaction. Every set participates. A matching rule that executes successfully with stop_after_match: true skips only the remaining rules in its own set, then processing continues with the next set. See Rule sets for ordering and conflict-safe reordering. The CEL context contains the participant’s current state (tags, counters, attributes, tiers), program state, and group memberships. It is captured once at event start. All rule conditions and dynamic action expressions use the same snapshot, including rules in later sets. Actions from earlier matching rules do not change what later rules see. See State Snapshot Evaluation Behavior for the implications and patterns. The rule definitions themselves are also read at processing time, not at ingestion. If you update a rule while events are queued, only events not yet processed use the new definition. A batch that spans the change can split across both versions. See Updating rules under live traffic for the full contract and how to reconcile which version applied. To check processing status:
The response includes the event status, rule evaluations that occurred, and error details if processing failed. Each evaluation records its execution-time rule_set_id, and the array follows the historical set and rule order used for that event.
Read-after-write visibility: the ID returned by the 202 is durable, but the event may briefly 404 on both GET /v1/events/{id} and GET /v1/events/by-key (typically well under a second) until Scrip finishes recording it. Poll until the ID resolves. Every accepted submission eventually becomes readable: either as a processed event or as status FAILED with an error_code if it was rejected asynchronously.
For a deeper view, use the impact endpoint to see everything an event caused: journal entries with postings, state changes, and per-entity balance impact.

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Execution Guarantees

Rules read counters, tags, and balances, then move money based on what they see. Three guarantees define what you can rely on when events arrive close together.

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One event at a time per participant

Scrip processes events for a given participant one at a time. The worker locks the participant before reading the state snapshot and releases the lock only after the event’s changes are committed. A second event for the same participant waits, then reads a snapshot that includes everything the first event wrote. This makes counter-based patterns safe under concurrent traffic. If two purchases arrive at the same moment for a participant whose purchase_count is 9, they cannot both see 9: one commits first, and the other then sees 10. A milestone rule that checks (participant.counter.purchase_count + 1.0) % 10 == 0 pays exactly once, on the event that actually crosses the milestone. The lock is per participant, so events for different participants process concurrently and one participant’s traffic does not slow another’s.

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Processing order is not guaranteed

Serialized is not the same as ordered. Events for one participant never run at the same time, but Scrip does not promise they run in the order you sent them. Two events ingested moments apart usually process in submission order, but delivery batching and automatic retries can swap them, and an event that fails transiently can retry after later events have already completed. event_timestamp never influences processing order; it only sets the now variable in CEL. Automation-generated events have no ordering relationship with events you send around the same time. A cron automation that fires at midnight enqueues its events on its own schedule, so a purchase sent near the boundary can process before or after the reset. Design for this by keeping rules order-tolerant. Accumulating counters, tag guards, and threshold checks written as (snapshot + event.amount) >= threshold reach the same totals regardless of arrival order, though which specific event crosses a threshold can differ. When the outcome genuinely depends on sequence, such as which side of a period boundary a purchase belongs to, compute that fact in your backend and send it on the event instead of deriving it from processing order. See the spend threshold pattern for a worked example.

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Failure is all-or-nothing

An event either completes with all of its effects or fails with none of them. Every rule evaluation and action for an event runs in one transaction. If any action fails, the transaction rolls back: a FAILED event has written nothing. No credits, no counter changes, no tags, no partial state to reconcile. One case continues instead of failing: a rule that would exceed its budget rolls back only its own actions, is recorded in rule_evaluations as skipped with reason BUDGET_EXCEEDED, and the remaining rules still run. Because a failed event committed nothing, retrying it is safe. A retry, automatic or manual, re-runs every rule from scratch and cannot double-pay. The retry evaluates against the participant’s state at retry time, not the state when the event first arrived; only now stays pinned to the event’s event_timestamp.

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Event Lifecycle

Events whose resolved actor or recipient is SUSPENDED or CLOSED are rejected before any rule runs: the event is accepted (202) and then recorded as a terminal FAILED event with code participant_suspended or participant_closed. See Participants: How this affects events.
Failed events carry a machine-readable error_code (when the failure has a classified code, such as participant_suspended or program_inactive) on the event resource and the event.failed webhook payload, so you can branch on failure type without string-matching the error message. If you have webhook endpoints configured, Scrip sends event.completed or event.failed notifications when processing finishes. This lets your application react to processing results without polling. Transient failures (infrastructure errors, timeouts) retry automatically with exponential backoff (2s, 4s, 8s, 16s, 32s), up to 5 retries. Validation failures are terminal; fix the cause and retry manually:
Manual retry resets the retry count and returns the event to PENDING for a fresh set of attempts. A retried event re-runs every rule from scratch against the participant’s current state; because a failed event committed nothing, nothing can apply twice. See Failure is all-or-nothing.

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Reversing an Event

When a purchase is refunded, the value its event earned has to come back. Because the ledger records exactly what each event credited, Scrip can reverse it directly:
Omit fraction to reverse all remaining value. The contract:
  • Recovery targets the original awards. Scrip claws back from the lots each award created, where the originally credited participant still holds that value. Value already spent, expired, transferred to someone else, or currently held is reported as shortfall_amount and never forced; a reversal cannot overdraw a balance.
  • Cumulative reversals are capped. Repeated partial reversals of one event cannot exceed what it originally awarded. A request over the remaining reversible value returns 409 Conflict instead of clamping.
  • The original event’s record never changes. Reversals post their own journal entries and return recovered value to the account each award drew from (the program wallet for prefunded assets). The impact endpoint shows the original event exactly as it ran.
  • State is reported, not reverted. The response includes the event’s counter, tag, and tier changes so you can decide what a refund means for them; send a compensating event if your policy requires it. Whether a refund erases a visit, and how partial refunds split across awards, are business decisions the endpoint deliberately leaves to you.
  • Plain rule-issued credits are reversible. Credit entries the reversal cannot process, such as settlement reconciliation entries, are listed in the response as ineligible_entries with reasons.
An event.reversed webhook fires with the per-award accounting. When refund policy differs per award rather than scaling uniformly, compute the amounts in your backend and use the exact clawback pattern instead.

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Idempotency

The idempotency_key ensures exactly-once processing per program. If you send the same program_id + idempotency_key combination more than once, the duplicate is ignored and the original event is returned. This applies regardless of whether the payload differs. If a network timeout occurs, re-send the same request. The duplicate is safely deduplicated. Treat idempotency keys as unique identifiers per intent. If the payload needs to change (e.g., correcting an amount), use a new key.
Use meaningful, deterministic idempotency keys like order-12345-completed or referral-user456-signup. Avoid random UUIDs, which defeat the purpose of deduplication.
You can also look up an event by its key:

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Event Data Design

The event_data payload becomes the event variable in CEL expressions. Design it with rules in mind:
Rules reference event_data fields directly as event.amount, event.category, etc. If a rule references a field that isn’t in the payload, the condition evaluates to false and the rule doesn’t match. Use has() for fields that only appear on some events. See CEL Expressions.

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Batch Ingestion

Send up to 100 events in a single request:
Each event is accepted, validated, and processed independently; a batch is never all-or-nothing. The 202 response reports per-event outcomes: each entry in results is either accepted (with the full event object) or error (with an error_code and message). Valid events proceed even when siblings fail. A 400 is returned only when the envelope itself is malformed (zero events, more than 100, or unparseable JSON). Business validation errors surface later via event.failed webhooks.

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Event Routing

By default, rule actions apply to the event’s participant. To credit a different participant, include their identifier in event_data and reference it in the rule action’s target:
The target field’s external_id accepts a CEL expression that resolves to a participant’s external ID. You can also use participant_id to resolve by Scrip UUID. The target participant must exist (they are automatically enrolled if not already a member of the program). Rules always evaluate conditions against the event’s participant (user_123). Only the action’s credit is routed to the target. See Rule Actions for more on static and dynamic targeting.