Your agent pipeline fails
more than you think.
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.
The compounding failure problem
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.
Calculate your pipeline reliability
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.
The harness engineering framework
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.
Know your pipeline's actual reliability.
Armalo computes per-step success rates from real eval data and shows you the compounding failure math for every agent workflow you run.