Free tool

AI is built for tasks, not jobs

A big job is just a lot of little tasks in a row. Drag the sliders and watch what happens.

Try it

A job is just a bunch of tasks in a row. Drag the sliders and watch what happens when AI tries the whole job by itself.

AI does the whole job alone 35%

One slip-up anywhere and the whole job fails.

With Tallyfy: one task at a time 99%

Each task is checked, and tried again if it slips.

90% per task, 10 tasks in a row, is about 35%. A 10-step job done blind is worse than a coin flip.

Start free with Tallyfy

The lesson: give AI one task at a time, not a whole job. Tallyfy makes that easy.

Try Tallyfy free Read the full story
Show the proof and the sources

Here is the math in plain words. If AI gets each task right 90% of the time, and the job is 10 tasks long, the whole job only works when all 10 work in a row. That is 90% multiplied by itself 10 times, which lands near 35%. No single task got harder. The chain did the damage.

A control layer flips it. It checks each task and tries again if one slips, so a 90% task effectively clears 99.9% and even a 20-task job stays near 98%. Same AI, same per-task hit rate. The only change is structure. That is what Tallyfy is: each task gets an owner, a deadline, a check, and a clean handoff, whether the doer is a person, an AI agent, or a rule.

Run it yourself

The calculator uses plain probability. This short simulation flips a real weighted coin per task across 100,000 trials, so you can see the simulated numbers land on the predicted ones. It is seeded, so you get the same result every time.

Simulation output: a 10-task job at 90% per-task reliability succeeds 35% of the time, while a gated retry pattern holds near 99%
Download reliability_sim.py Python 3, standard library, runs in under a second. 
import random

random.seed(42)
TRIALS = 100_000


def chain_success(n, r, trials=TRIALS):
    """Autonomous chain: the job succeeds only if all n tasks succeed."""
    wins = 0
    for _ in range(trials):
        if all(random.random() < r for _ in range(n)):
            wins += 1
    return wins / trials


def gated_success(n, r, attempts=3, trials=TRIALS):
    """Gated chain: each task gets up to attempts tries before the job fails."""
    wins = 0
    for _ in range(trials):
        ok = True
        for _ in range(n):
            if not any(random.random() < r for _ in range(attempts)):
                ok = False
                break
        if ok:
            wins += 1
    return wins / trials


R = 0.90
print(f"Per-task AI reliability: {R:.0%}    Trials per row: {TRIALS:,}\n")
print("AI alone, chained end-to-end  (one failed task kills the whole job)")
for n in (1, 3, 5, 10, 20):
    print(f"{n:>6}   predicted {R ** n:>6.1%}   simulated {chain_success(n, R):>6.1%}")

print("\nWith per-task checkpoints + retry  (the Tallyfy pattern, up to 3 tries/task)")
for n in (10, 20):
    print(f"{n:>6} tasks   job success {gated_success(n, R):.1%}")

Where this comes from

Learn how Tallyfy does it

Follow Tallyfy