Algorithm Design

Trigger Boundary

• •Use when algorithmic correctness or complexity drives implementation risk.
• •Do not use for persistence-schema decisions; use db-*.
• •Do not use for runtime deployment topology; use deployment-* or kubernetes-*.

Goal

Deliver correct and efficient computational designs with clear tradeoffs.

Inputs

• •Change scope and risk profile
• •Domain evidence for problem decomposition and algorithmic strategy selection
• •Operational, compliance, and rollout constraints

Outputs

• •Algorithm design decision with trade-off matrix
• •Decision log for problem decomposition and algorithmic strategy selection
• •Verification checklist with measurable pass-fail criteria

Workflow

1. •Clarify outcomes and hard constraints for problem decomposition and algorithmic strategy selection.
2. •Produce options and select an approach for problem decomposition and algorithmic strategy selection.
3. •Evaluate trade-offs across security, performance, operability, and maintainability.
4. •Verify decisions using correctness proof sketch and benchmark sanity checks.
5. •Publish decisions, residual risks, and accountable follow-up actions.

Quality Gates

• •Scope and assumptions for problem decomposition and algorithmic strategy selection are explicit and reviewable.
• •Decision rationale is backed by evidence instead of preference.
• •Rollout and rollback criteria are defined when production impact exists.
• •Residual risks have owners, due dates, and verification steps.

Failure Handling

• •Stop when algorithm choice lacks correctness reasoning or trade-off evidence.
• •Escalate when accepted risk exceeds team policy thresholds.