Solving the Compound Error Problem

The Mathematics of Compounding Error

The compound error problem is the single biggest obstacle to reliable multi-step AI workflows. The mathematics are unforgiving: if each step in a workflow has accuracy p, the probability that an N-step workflow completes without error is p^N.

The implications are stark: A 10-step workflow with 95% per-step accuracy (which sounds excellent) succeeds only 59.9% of the time. A 100-step workflow at the same accuracy fails 99.4% of the time. This is why multi-agent AI systems feel unreliable in production even when individual agents perform well in testing.

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The Agency-Reliability Tradeoff

The industry's current response to this problem is to constrain autonomy. If agents make errors, give them less to do. Reduce the number of steps. Add human checkpoints. Simplify the workflow.

This defeats the purpose of agentic AI. An agent that requires human approval at every step is not autonomous; it is an expensive UI for a human decision-maker.

Corvair's approach does not constrain autonomy. It improves quality from the foundation upward:

1. Start with Data Sigma: Clean, validated input data eliminates the largest source of errors.
2. Improve Process Sigma: Structured prompting, deterministic tool use, and validation checkpoints.
3. Address Agent Sigma: Consensus voting and coordination protocols for multi-agent workflows.

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Consensus Voting: Six Sigma from Imperfect Agents

Consensus voting applies a well-understood statistical principle to multi-agent AI: independent errors in multiple agents cancel out when you take the majority vote.

This works because the probability that a majority of independent agents make the same error is dramatically lower than the probability that any single agent makes an error. The key requirement is independence: the agents must fail in different ways, not make correlated errors.

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When Consensus Voting Is Most Valuable

Consensus voting is not free; it multiplies compute and latency by the number of voting agents. It is most valuable for:

• High-risk decisions: where the cost of error significantly exceeds the cost of redundant computation.
• Long workflows: where compound error would otherwise make completion unreliable.
• Regulated processes: where quality evidence must be demonstrated to auditors.
• Critical path actions: where a single failure cascades through downstream systems.

For low-risk, high-frequency actions, a single well-governed agent with strong Data Sigma and Process Sigma may be sufficient. The governance engine's risk scoring determines when consensus voting is warranted.