01 DEFINE
Define invariants
Describe the truths your system must preserve: balances reconcile, orders settle once, data stays consistent.
assert(balance >= 0)
Join the waitlist ↗DETERMINISTIC SIMULATION TESTING
Find the bugs that only show up in production.
Replay them exactly.
Run your whole system through seeded faults. Find the failure, replay it on demand, and know when the fix holds.
ILLUSTRATIVE RUN REPLAYABLE
INVARIANT VIOLATED
balance_after_settlement >= 0Replay this exact execution →YOUR SYSTEM, UNDER REALISTIC PRESSURESCROLL TO EXPLORE ↓
THE PRODUCTION GAP
Some bugs need a whole system
to come alive.
A unit test sees a function. Production sees retries, timeouts, clocks, and dependencies colliding at once. SpawnOps brings that complexity into a controlled, repeatable environment.
Timing changes everythingRace conditions hide in the gaps between services.
Time is a dependencyClock skew and expiry expose edge cases.
Failure is not rareDisks, networks, and processes fail mid-flow.
A REPEATABLE VERIFICATION LOOP
From invariant
to verified fix.
Explore the conditions that conventional tests struggle to reproduce, then keep the exact failure close at hand.
02 SIMULATE
Simulate with seeded faults
Explore repeatable combinations of network delays, disk errors, crashes, and clock skew.
seed: 0x7A31C9
03 REPLAY
Replay the failing seed
Return to the same execution, with the same fault timing and system behavior, whenever you need it.
replay --seed 0x7A31C9
04 VERIFY
Fix and verify
Change the code, rerun the scenario, and confirm the invariant now holds.
invariant: PASS
A FEEDBACK LOOP FOR CODING AGENTS
Let agents write code.
Make the system prove it.
Claude Code, Codex, Cursor, or your own workflow can run a scenario, inspect a replayable failure, make a fix, then verify the result against the same conditions.
Bring deterministic checks into your loop ↗$ agent run simulation
→ Exploring seeded scenarios...
→ Injected network delay at ledger.write
× Invariant failed · balance must remain ≥ 0
↳ seed 0x7A31C9 · replay available
$ agent replay 0x7A31C9
→ Fix applied · replaying same execution
✓ All invariants hold
BUILT FOR SYSTEMS WHERE CORRECTNESS MATTERS
Different systems.
Same need for certainty.
When one failure can cascade across services, verifying the whole system matters.
Fintech & trading
Exercise settlement, ledger updates, and order flows through retries and partial failures.
↗Infrastructure
Explore cascading dependency failures, process crashes, and network partitions.
⌁Healthcare
Check that critical workflows remain consistent through delays and service disruptions.
＋E-commerce
Test inventory, payments, and order state when dependencies become unreliable.
◎SYSTEM-LEVEL TESTING, BY DESIGN
Your system.
In a controlled world.
SpawnOps is being built to run an application and its dependencies inside deterministic virtual machines. The goal: explore many seeded conditions without losing the ability to reproduce the one that matters.
DETERMINISTIC ENVIRONMENT
service A↔service B↔database
VIRTUAL CLOCKFAULT INJECTIONCHECKPOINTS
seeded execution · replayable
COMMON QUESTIONS
Good questions.
Early answers.
Is SpawnOps available today?+
SpawnOps is in development. Join the waitlist for product updates and availability news.
What does “deterministic” mean here?+
Given the same system state, scenario, and seed, a run is intended to reproduce the same execution so a failure can be investigated repeatedly.
Does it run my full application and dependencies?+
The product is being designed to run a customer's system, including dependencies, within deterministic virtual machines. Supported environments and setup details are still being developed.
How does it work with coding agents?+
The intended workflow is to let an agent run scenarios, inspect a failing seed, make a change, and rerun that same scenario to verify the fix.
GET THE SIGNAL, NOT THE NOISE
Make the next failure
one you already know.
Join the waitlist for occasional updates as SpawnOps takes shape.