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Defend the system, not one metric

The capstone changes the unit of reasoning from one subsystem to the whole AI system. A locally good choice can become a bad architecture when it conflicts with another layer. Strong retrieval cannot repair ambiguous instruction authority. A safe prompt cannot enforce a runtime permission boundary. A reliable loop can still live inside an unnecessarily complex graph. A good candidate can still be released under weak evidence.

Prompt Engineering defines the behavioral contract: which instructions have authority, how consequential constraints are stated, how retrieved evidence is treated, and what output shape downstream systems can validate. In this challenge, unresolved Prompt conflicts are explicit blockers rather than a small quality penalty that can be averaged away.

Context Engineering combines retrieval and working-context policy. Retrieval decides what evidence is available; compression decides what survives into the model working set. The architecture must retain enough critical information without exceeding the context budget or paying for evidence that does not improve the task.

Harness and Loop Engineering control execution. Validation, approval, retries, timeouts, termination, and local recovery change reliability, operational cost, and risk. More retries can improve completion while making repeated work and long tails worse, so the execution policy must be judged as a production control system rather than a single success-rate number.

Graph Engineering decides how responsibilities connect. A coordinator with many workers may look more advanced while increasing state coupling and failure propagation. A smaller branched workflow can preserve independent evidence, retry only the failed node, verify joins, and place review at the consequential boundary. The graph earns complexity only when that structure creates a measurable advantage.

Evaluation Engineering turns all of those design choices into a release decision. Dataset coverage, evidence width, safety vetoes, and cost gates decide what counts as ready. The same architecture can look acceptable under a demo-biased evaluation and fail a production gate. This is why evaluation is part of system architecture rather than a final dashboard added after implementation.

All values are deterministic educational abstractions composed from the existing AhaFrame Labs. They are not live model benchmarks or universal thresholds. The goal is to practice defending a coherent architecture under explicit constraints, then carry that reasoning into real systems with measured data.