---
status: draft
last_updated: 2026-05-19
---

# Reactive Execution

Signal-driven status propagation, computed preconditions, and abort cascading for workflow template execution.

## Overview

The reactive execution layer bridges workflow template structure (DAG) to runtime behavior (call execution). It uses `@preact/signals-core` (via ujsx's reactive layer) to create a signal-backed execution model where:

- Each `<Operation>` node gets a `signal<NodeStatus>` tracking its lifecycle state
- Preconditions are `computed<boolean>` values that automatically resolve when upstream dependencies complete
- Abort cascades propagate through the signal graph — setting one node to `"aborted"` automatically prevents downstream nodes from starting

This layer does NOT execute operations directly. It provides reactive state that the hub coordinator reads and writes. The coordinator calls `registry.execute()` when a node's preconditions are met, and updates the node's status signal when the call completes.

## ReactiveRoot for Workflows

```typescript
class WorkflowReactiveRoot {
  private statusMap: Map<string, Signal<NodeStatus>>;
  private preconditions: Map<string, Computed<boolean>>;
  private graph: DirectedGraph;
  private abortMap: Map<string, () => void>;

  constructor(graph: DirectedGraph) {
    this.graph = graph;
    this.statusMap = new Map();
    this.preconditions = new Map();
    this.abortMap = new Map();
    this.initializeSignals();
  }
}
```

`WorkflowReactiveRoot` wraps the reactive state for an entire workflow execution. It takes the structural DAG (from the GraphologyHost) and creates reactive state for each operation node.

### initializeSignals()

```typescript
private initializeSignals(): void {
  for (const node of this.graph.nodes()) {
    const attrs = this.graph.getNodeAttributes(node);
    if (attrs.category !== "operation") continue;  // Skip structural nodes (already flattened)

    const status = signal<NodeStatus>("idle");

    const preconditions = computed(() => {
      const predecessors = this.graph.inNeighbors(node);
      return predecessors.every(pred => {
        const predStatus = this.statusMap.get(pred);
        return predStatus && predStatus.value === "completed";
      });
    });

    this.statusMap.set(node, status);
    this.preconditions.set(node, preconditions);
    this.abortMap.set(node, () => this.cascadeAbort(node));
  }
}
```

For each operation node in the DAG:
1. Create a `signal<NodeStatus>` starting at `"idle"`
2. Create a `computed<boolean>` that's `true` when all predecessor nodes have status `"completed"`
3. Register an abort function that cascades to all descendants

### Status lifecycle

The signal-based status lifecycle mirrors `CallStatus` with workflow-specific additions:

```
idle → waiting → ready → running → completed
                                       → failed
                    → aborted          → aborted
```

| Status | Meaning | Signal trigger |
|--------|---------|---------------|
| `idle` | Node just created, no parent completion yet | Initial state |
| `waiting` | At least one predecessor is running, none have completed | Any predecessor status change |
| `ready` | All predecessors completed (preconditions met) | `computed` resolves to `true` |
| `running` | Call executing | Hub sets `status.value = "running"` |
| `completed` | Call succeeded | Hub sets `status.value = "completed"` |
| `failed` | Call failed | Hub sets `status.value = "failed"` |
| `aborted` | Call cancelled (or parent cancelled) | Hub or cascade sets `status.value = "aborted"` |
| `skipped` | Conditional branch not taken | Conditional evaluation sets this |

The hub coordinator reads the `ready` state (via `preconditions`) and triggers execution. When the call completes, the hub writes the new status to the signal. The signal propagates to all downstream `computed` values automatically.

## Computed Preconditions

The core innovation of reactive execution: each node's "can I start?" question is a `computed` signal that automatically resolves based on upstream states.

```typescript
const preconditions = computed(() => {
  const predecessors = graph.inNeighbors(node);
  return predecessors.every(pred => statusMap.get(pred)!.value === "completed");
});
```

This means:
- Adding a new predecessor automatically includes it in the check (if the DAG changes)
- A predecessor completing automatically re-evaluates all dependent preconditions
- An aborted predecessor prevents all dependents from becoming `ready`
- No manual event wiring or callback chains

### Sequential preconditions

In a sequential group (A → B → C):

- A's preconditions: `true` (no predecessors, or root-level)
- B's preconditions: `A.status === "completed"`
- C's preconditions: `B.status === "completed"`

When A completes → B's preconditions become true → hub starts B → B completes → C's preconditions become true → hub starts C. All without manual event wiring.

### Parallel preconditions

In a parallel group (A starts B and C simultaneously):

- B's preconditions: `A.status === "completed"` (same as any sequential dependency)
- C's preconditions: `A.status === "completed"` (shared predecessor)

Both B and C become `ready` at the same time, and the hub starts them in parallel.

### Join preconditions

When a node depends on multiple predecessors (e.g., D depends on both B and C completing):

- D's preconditions: `B.status === "completed" && C.status === "completed"`

D only becomes `ready` when all predecessors complete. This is the "join" in fork-join parallelism.

## Abort Cascade

Abort cascading is signal-driven. When a node is aborted:

```typescript
cascadeAbort(nodeId: string): void {
  const status = this.statusMap.get(nodeId);
  if (status && !isTerminal(status.value)) {
    status.value = "aborted";
    // Cascade to all descendants
    for (const desc of this.graph.descendants(nodeId)) {
      const descStatus = this.statusMap.get(desc);
      if (descStatus && !isTerminal(descStatus.value)) {
        descStatus.value = "aborted";
      }
    }
  }
}
```

This sets the status of the aborted node and all of its descendants to `"aborted"`. The `computed` preconditions of these nodes automatically re-evaluate — but since aborted nodes never become "completed", their dependents will never become "ready".

### Interaction with call protocol abort

There are two abort mechanisms:

1. **Signal cascade** (this layer) — sets `status.value = "aborted"` for the node and all descendants. This is immediate and graph-based.
2. **Call protocol abort** (operations layer) — `PendingRequestMap.abort(requestId)` propagates `call.aborted` events through the pub/sub layer. This is network-aware and handles remote calls.

The hub coordinator should invoke both:
```typescript
// When aborting a call:
workflowRoot.cascadeAbort(nodeId);           // Signal cascade
prm.abort(requestId);                        // Protocol cascade
```

The signal cascade is for local state (the reactive graph). The protocol cascade is for remote state (the running calls). They're complementary — the protocol cascade may take time to propagate, but the signal cascade is instant.

## NodeStatus vs CallStatus

`NodeStatus` extends `CallStatus` with workflow-specific states that have no call protocol equivalent:

| NodeStatus | Meaning | CallStatus equivalent |
|-----------|---------|----------------------|
| `idle` | Not started, no preconditions evaluated | None (call doesn't exist yet) |
| `waiting` | Preconditions not met (upstream still running) | None |
| `ready` | Preconditions met, eligible to start | None |
| `running` | Call in progress | `running` |
| `completed` | Call succeeded | `completed` |
| `failed` | Call failed | `failed` |
| `aborted` | Call cancelled | `aborted` |
| `skipped` | Conditional branch not taken | None |

The hub coordinator maps between these:

```typescript
// NodeStatus → CallStatus (when starting a call)
function nodeStatusToCallAction(status: NodeStatus): "start" | "skip" | "abort" | "none" {
  switch (status) {
    case "ready": return "start";
    case "skipped": return "skip";
    case "aborted": return "abort";
    default: return "none";
  }
}

// CallStatus → NodeStatus (when call event arrives)
function callStatusToNodeStatus(callStatus: CallStatus): NodeStatus {
  // Direct mapping for shared states
  return callStatus as NodeStatus;
}
```

## Effect-Driven Execution

The hub coordinator uses `effect()` to react to precondition changes:

```typescript
for (const [nodeId, preconditions] of workflowRoot.preconditions) {
  effect(() => {
    if (preconditions.value) {
      const status = workflowRoot.statusMap.get(nodeId)!;
      if (status.value === "idle" || status.value === "waiting") {
        // All preconditions met — start the call
        status.value = "running";
        const operationId = graph.getNodeAttributes(nodeId).name;
        prm.call(operationId, getInput(nodeId), { parentRequestId: parentCallId })
          .then(result => { status.value = "completed"; })
          .catch(error => { status.value = "failed"; });
      }
    }
  });
}
```

Each node gets an `effect()` that watches its `preconditions` computed value. When preconditions resolve to `true` and the node is in a startable state (`idle` or `waiting`), the effect starts the call via `PendingRequestMap.call()`.

The call's promise resolution updates the node's status signal, which triggers downstream preconditions to re-evaluate, which triggers their effects, and so on.

### Effect disposal

Each `effect()` returns a dispose function. The `WorkflowReactiveRoot` tracks all effect disposers and provides a `dispose()` method that tears down the entire reactive graph:

```typescript
dispose(): void {
  for (const disposer of this.effectDisposers) {
    disposer();
  }
  this.statusMap.clear();
  this.preconditions.clear();
  this.abortMap.clear();
}
```

This is critical for cleaning up when a workflow completes, fails, or is aborted. Without disposal, signal subscriptions leak.

## Constraints

- **Signals are in-memory** — `WorkflowReactiveRoot` state is not persisted. If the hub restarts, the reactive state is lost and must be reconstructed from call protocol events + template re-render.
- **Effect-driven execution is optional** — the hub coordinator can choose not to use `effect()` and instead poll `preconditions.value` manually. The reactive layer provides the building blocks; the coordinator decides how to use them.
- **Abort is immediate in signals, delayed in protocol** — setting `status.value = "aborted"` is instant, but `prm.abort(requestId)` takes time to propagate through the call protocol. The hub should invoke both.
- **`skipped` is set by conditional evaluation, not by the call protocol** — a `Conditional` node whose test evaluates to `false` sets its child's status to `skipped`, which prevents the call from ever starting.
- **`NodeStatus` and `CallStatus` share terminal states** — `running`, `completed`, `failed`, `aborted` map directly. `idle`, `waiting`, `ready`, `skipped` are workflow-specific additions.

## Open Questions

1. **Should preconditions support OR logic?** Currently all predecessors must complete. An `anyOf` predicate would allow "start this node as soon as any predecessor completes." This would require an edge attribute or node-level configuration.

2. **How are retries handled at the signal level?** If an operation fails and should be retried, the status would go `running → failed → ready → running`. This requires resetting the status back to `ready`, which the current state machine doesn't support (failed is terminal). A `retried` status or a separate `retryCount` attribute may be needed.

3. **Should the reactive graph support partial re-rendering?** If a template changes mid-execution (e.g., a step is added), the ujsx reconciler could diff the old and new trees. But the ReactiveHost only supports mount rendering. Re-rendering would require reconciler support.

4. **How does `maxConcurrency` interact with preconditions?** A `Parallel` group with `maxConcurrency: 3` should only start 3 nodes at a time, even though all preconditions are met. This is a scheduling concern, not a structural one. The reactive layer could implement this as a semaphore signal, or it could be the coordinator's responsibility.

## References

- ujsx reactive layer: `@alkdev/ujsx/docs/architecture/reactive-layer.md`
- ujsx reconciler: `@alkdev/ujsx/docs/architecture/reconciler.md`
- Schema: [schema.md](schema.md) — `NodeStatus`, `CallStatus`
- Host configs: [host-configs.md](host-configs.md)
- Workflow templates: [workflow-templates.md](workflow-templates.md)
- Call protocol: `@alkdev/alkhub_ts/docs/architecture/call-graph.md`