Yes, this is exactly the right stage to discuss it. Retrofitting asynchronous
boundaries into a strictly synchronous stack machine later on usually requires
rewriting the entire execution loop.
If your goal is strict, deterministic execution times where a program can
demand intermediate states from incomplete tasks, standard JavaScript Promises
or async/await will work against you. They abstract away control over the
microtask queue, making deterministic time-boxing almost impossible.
To solve this in a high-performance environmentsuch as a Fastify gateway
interacting asynchronously with a heavy backend engineyou must completely
decouple the VM's execution from Node's standard event loop using Worker
Threads and shared memory.
Here is the architectural blueprint for deterministic, pollable async
execution.
The Architecture: Workers and Ring Buffers
To allow a main thread to demand state from a running task without blocking,
they must share memory. You can utilize an SPSC (Single-Producer,
Single-Consumer) Ring Buffer backed by a SharedArrayBuffer.
 * The Spawner (Consumer): The main VM loop (or gateway). It launches a task
and holds a reference to the ring buffer.
 * The Task (Producer): A separate VM instance running in a Node.js Worker
Thread. As it executes instructions, it periodically writes its current state,
intermediate calculations, or progress percentage into the SharedArrayBuffer.
Because reading from a SharedArrayBuffer is a synchronous, atomic operation,
the main VM can poll the status of the worker at any exact instruction cycle
without yielding the thread.
1. Expanding the AST and Opcodes
We introduce two new concepts: spawn (to kick off a background task) and poll
(to synchronously check its state).
[
  "let", [
    ["task_id", ["spawn", "heavy_computation", [1000000]]]
  ],
  ["if", ["<", "time_elapsed", 50],
    ["poll", "task_id"],
    ["force_terminate", "task_id"]
  ]
]

We add corresponding opcodes:
const Opcodes = Object.assign(Opcodes || {}, {
  SPAWN: 'OP_SPAWN', // Pops function & args, spins up worker, pushes Task ID
  POLL:  'OP_POLL'   // Pops Task ID, reads shared buffer, pushes { status,
data }
});

2. The Worker Thread (Producer)
When OP_SPAWN is hit, the engine fires up a Worker. The worker runs its own
isolated VM loop, but it is handed a SharedArrayBuffer.
Inside the worker's execution loop, you can enforce a rule: every N
instructions, or at specific explicit yield points in the code, the worker
serializes its current top-of-stack data or progress counter and writes it to
the buffer.
/ Inside the Worker Thread's VM loop
let instructionCount = 0;

while (callStack.length > 0) {
  // ... execute instruction ...

  instructionCount++;
  if (instructionCount % 100 === 0) {
    // Write intermediate state to the SPSC Ring Buffer atomically
    RingBuffer.write(sharedBuffer, {
      status: 'pending',
      ip: frame.ip,
      partialResult: dataStack[dataStack.length - 1] 
    });
  }
}

/ Write final state
RingBuffer.write(sharedBuffer, { status: 'complete', finalResult:
dataStack.pop() });

3. The Deterministic Polling Loop (Consumer)
Back in the main VM, the OP_POLL instruction allows the program to read that
buffer instantly. Because the main VM executes synchronously, you can enforce
a strict maximum execution time (or instruction count).
If the time limit is reached, the main VM executes OP_POLL, grabs whatever
intermediate state the worker managed to produce, and returns it immediately
to the callersatisfying your time-constrained requirement.
      case Opcodes.POLL: {
        const taskId = dataStack.pop();
        const buffer = taskRegistry.get(taskId);
        
        // Synchronously reads the latest snapshot without blocking or
awaiting.
        // Returns immediately, even if the worker is still actively
computing.
        const latestState = RingBuffer.read(buffer); 
        
        // Pushes the state object onto the stack for the JSON-Lisp program to
evaluate
        dataStack.push(latestState);
        break;
      }

Why this fits time-constrained execution
 * Zero Event Loop Latency: Because you aren't using .then() or await, your VM
loop isn't at the mercy of Node's V8 microtask queue scheduler. The
instruction timing is highly predictable.
 * Graceful Degradation: If a request must complete in exactly 50ms, the main
VM can spin in its own tight loop, check the clock, hit OP_POLL at 49ms, grab
the partial state, and send the HTTP response.
 * True Parallelism: The worker is actually executing on a different CPU core,
meaning the main API thread remains entirely unblocked and responsive to new
incoming requests.