Agent reliability and task length: what breaks in production?

TL;DR: An agent with 95% per-step reliability succeeds on a 20-step task only about 36% of the time, according to Agentic Fabriq, because end-to-end performance is the product of every step and pass@1 hides that compounding failure. The real control problem is reducing steps, making them deterministic, and detecting errors before they propagate.

Editorial analysis by NHI Mgmt Group, based on content published by Agentic Fabriq: “Why Your Agent Works in the Demo and Fails in Production”.

Key questions

Q: How do security teams know if an agent workflow is actually reliable?

A: They measure it with repeatable evaluations, not with launch-time confidence.

Q: Why do multi-step agent workflows fail even when every step seems acceptable?

A: Because each step multiplies the chance of failure.

Q: What signs show that an agent is failing silently rather than obviously?

A: Look for duplicated writes, status updates that do not match the underlying state, partial completions reported as done, and results that vary across repeated runs.

Practitioner guidance

• Model end-to-end success, not just step success Count the steps in each agent workflow and estimate the full-task success rate from per-step reliability before you move into production.
• Instrument per-step outcomes and variance Log each intermediate decision, tool call, write, and verification result so you can see where reliability decays.
• Add external verification for state-changing actions Require schema validation, reconciliation, and idempotent writes for any agent action that changes records, permissions, or financial state.

Bottom line: Long agent workflows can fail even when each individual step looks acceptable, because the end-to-end result is the product of all those steps.

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