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A 10-step agent workflow at 90% per-step reliability succeeds only 34.9% of the time. Reliability compounds failure. This is the central insight behind harness engineering.
Each step in your pipeline multiplies the failure probability of the previous one. By step 10, even a "good" 90% step rate collapses to catastrophic overall failure.
Most agent pipelines run at 85–92% per-step reliability. At 10 steps, that means the workflow fails roughly half the time. The fix isn't better prompts — it's deterministic software rails: state machines, validation loops, and retry-on-failure gating.
Enter the number of steps in your agent workflow and your estimated per-step success rate. See the real overall reliability — and what per-step rate you need to reach production-grade nines.
Five interlocking mechanisms that turn unreliable AI calls into production-grade pipelines. Armalo implements all five and exposes them as SDK primitives for builders.
Fixed-phase enforcement. Agents can only move to valid next states — invalid transitions are blocked at the DB level, not caught after the fact.
Programmatic output checking with forced iteration on failure. Not just detecting bad output — retrying until it passes or hitting a defined retry cap.
Per-step reliability tracking with compounding math. Surfaces the real overall success rate of your pipeline, not the best-case single-step number.
Supervisor agents spawning isolated sub-agents with scoped context, model routing, and inherited permissions. Divide-and-validate at every layer.
One-line trust gates, retry wrappers, and pipeline reliability queries. Builders get harness-ready primitives without reinventing reliability infrastructure.
Armalo computes per-step success rates from real eval data and shows you the compounding failure math for every agent workflow you run.