# Compound Error Degradation

> Deploying a long-horizon agent while ignoring that per-step accuracy compounds — a 20-step pipeline of 95%-accurate steps succeeds less than 36% of the time.

- **Category**: Agentic AI
- **Subcategory**: Anti-Patterns
- **Canonical URL**: https://designpattern.fyi/patterns/compound_error_degradation/

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## Description
**Intent**: Treating per-step benchmark accuracy as a forecast for end-to-end pipeline quality.

**Context**: A team measures 95% per-step accuracy and scales to a 20-step pipeline. The math says 0.95^20 ≈ 36% overall success. They learn this in production.

**Solution**: Model end-to-end task success as the product of per-step success rates (after any per-step recovery). Either cap step count so the product clears your quality bar, or raise effective per-step success with verifiers, retries, and intermediate checkpoints. Treat raw benchmark accuracy as a ceiling, not a forecast.



## Use Cases
- Reviewing a long-horizon agent proposal with no step budget and no per-step verifier.
- Per-step benchmarks look healthy but end-to-end success on production traffic does not.
- Naming this failure mode explicitly when it arises in design review.






## Trade-offs


### Advantages

- Naming the failure mode forces explicit step budgets and per-step recovery planning

- Surfaces when you need a stronger model versus a shorter pipeline




### Considerations & Drawbacks

- Per-step success on production-shaped tasks is hard to measure; benchmarks rarely transfer cleanly

- Per-step verifiers add their own error rates that also need to be modeled







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**Reference**: [Original Source](https://www.agentpatternscatalog.org/patterns/compound-error-degradation/)