Accumulating unhandled execution errors degrades an agent's capacity because unresolved task friction triggers context bloat, creating a compounding feedback loop that accelerates token depletion nonlinearly over time. In Play, illustrative values show that Remaining Token Budget falls from 310 to 60 at step 15 when the daily workload doubles, demonstrating how persistent failures rapidly drain resources compared to stable baselines.
Key takeaways
- Raising Verification Thoroughness from 2 to 4 leaves Remaining Token Budget at 1150 at step 15 instead of 310.
- Increasing Daily Errand Target from 10 to 20 reduces Remaining Token Budget to 60 by step 8.
- Unresolved Task Errors compound future token burn by expanding prompt context across daily cycles in this model.
- Operators evaluating agent capacity in this model should prioritize verification depth before scaling task targets.
Does fixing errors protect your token budget more than scaling work?
Teams deploying autonomous agents face a constant balance between scheduling more daily errands and investing compute in immediate verification to avoid lingering task friction. In this playable model, raising verification thoroughness leaves Remaining Token Budget at 1150 at step 15 instead of the baseline value of 310, whereas doubling the daily errand target drops that stock to 60. All numerical figures presented here are illustrative scenario values rather than real-world operational measurements.
Unchecked execution errors lead to context bloat across subsequent operational cycles. When early glitches go unaddressed, prompts expand and drain limited daily allocations faster than expected.
How do errand targets and verification thoroughness shift the budget?
The first lever, Daily Errand Target, sets the scheduled daily workload. At its baseline setting of 10, the stock reaches 310 at step 15. Raising this setting to 20 accelerates burn, leaving the stock at 60.
At step 1 with target 20, the stock begins at 960. By step 5 it drops to 520, reaching 60 by step 8, showing rapid early depletion.
The second lever, Verification Thoroughness, sets the depth of checks applied to each task. Its baseline setting is 2. Doubling this lever to 4 keeps the stock growing, reaching 1150 at step 15 instead of 310.
Under baseline settings, the stock peaks at 1012 at step 2 before falling. With thoroughness set to 4, it climbs to 1050 by step 5 and reaches 1080 at step 8.
The card also has two more sliders. Refill sets how many tokens flow from Daily Token Refill into Remaining Token Budget each step: 60 in the baseline, adjustable from 10 to 200. Remaining Token Budget sets the tokens available for task execution before step 1: 1000 in the baseline, adjustable from 0 to 5000.
How does the agent economy model work?
A regular source supplies Daily Token Refill to the main pool, Remaining Token Budget, at each step. Outflow occurs through Errand Token Burn, which drains tokens based on active errand load and context overhead.
Meanwhile, Error Inflow deposits glitches into Unresolved Task Errors. As this pool expands, it enlarges the prompt context and accelerates the token drain in subsequent daily turns.
Verification Thoroughness dampens Error Inflow. Daily Errand Target scales up both the immediate consumption and the rate of new task friction entering the system.
What does this result mean for operational agent budgets?
In this model, task volume and verification thoroughness create asymmetric effects on resource longevity. Adding workload without increasing oversight causes prompt context bloat that compounds drain over time.
Operators reviewing agent economics in this model should look at error accumulation rates before raising task quotas. High verification preserves headroom, while high task targets accelerate budget exhaustion.
What does the model leave out?
This model relies on several simplifying assumptions. It operates on a fixed time step where one step equals one operational day, using constant refill rates and deterministic error accumulation rules.
The diagram contains two pools, two sources, one sink, and four levers. Real systems are more complicated than any model, omitting many structural elements present in larger environments.
Where does the topic come from?
The structure of this playable model was inspired by Show HN: An AI agent runs ten web errands a day, every transcript published, published by Hacker News on 6 October 2026. This essay and its numbers are the model's own illustrative scenario.
How can you test different settings yourself?
You can adjust Daily Errand Target and Verification Thoroughness in the playable model to observe how token pools evolve under varied configurations. Each Play highlights the trade-off between throughput and context growth.
The parts of the model
The question it asks: How does accumulating unhandled execution errors degrade an autonomous web agent's effective token budget over daily operational cycles?
- Daily Token Refill · Source
- Supplies new computational tokens each operational day.
- Remaining Token Budget · Pool
- Tracks available agent token balance measured in thousand-token units.
- Errand Token Burn · Sink
- Discards tokens consumed by daily errand runs and debugging.
- Error Inflow · Source
- Generates execution glitches from unverified web actions each day.
- Unresolved Task Errors · Pool
- Accumulates unresolved web friction and context bloat across daily plays.
- Daily Errand Target · Lever
- Sets the scheduled daily errand workload in tasks per day.
- Verification Thoroughness · Lever
- Sets the depth of automated verification passes per task.
Assumptions and limits
- One step of Play represents one operational day.
- Unresolved Task Errors directly increase the rate of Errand Token Burn in future steps via larger prompt contexts.
- All numerical values and parameters are illustrative.
A teaching model with illustrative values; each Play is a scenario, not a forecast.
Sources
- Hacker News (news.ycombinator.com)
About this model
Machinations makes these models with AI from a public source, and each one is a teaching scenario: its numbers are illustrative values chosen for the model, not measurements or forecasts. You can open this diagram in the Community to remix it and change it.