Phase 8: Multi-Agent Systems & Platform Architecture · 45 min · Claude Code · Python
Long-Running Agent Systems
A real 22-incident study found 70% of silent agent failures were caught by a human looking at the output, not by tests. Design for failures that are loud, attributable, and boring instead of relying on that.
Real-world application: Knowing that idle periods, not just active execution, are where long-running agent bugs hide — and testing for it deliberately — is the specific practice that separates production agent-runtime experience from theory.
What you will learn
- Explain why idle-time periods, not just active execution, are a distinct source of long-running agent bugs
- Simulate realistic idle-time delays in tests to catch bugs that only manifest after real waiting periods
- Interpret the 22-incident study's finding that most silent failures were caught by human observation, not automated tests
- Design for failures that are loud, attributable, and boring instead of relying primarily on human vigilance
Introduction
Long-Running Agent Systems
Objective
Run a fast test that misses a credential-expiry bug and an idle-simulated test that catches it, then classify the resulting failure against the loud/attributable/boring framework.
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