Abstract:Large Language Model (LLM) agent systems suffer from failures that occur without external triggers -- no injection, no adversarial input, no resource
exhaustion. These silent failures -- unexpected deviations from intended behavior under normal conditions -- are routinely misattributed to bugs or
configuration errors. Through systematic analysis of over 40,000 controlled trials and long-term production observations spanning 100,000+ agent
interactions, we identify a common structural logic underlying these failures. Building on patterns observed in our experiments, we survey the
global research literature on autonomous agent reliability and synthesize 22 intrinsic properties of LLM agent systems across six lifecycle layers:
foundation semantics, inter-agent transmission, memory persistence, task execution, feedback correction, and systemic evolution. We demonstrate that
whenever a sufficient subset of these properties co-exist, system entropy -- the measurable accumulation of disorder: loss of output consistency,
task accuracy, and cross-session coherence -- increases monotonically with interaction rounds. We formalize this as the Entropy Principle: S(t) = S0
* e^(alpha * t), with alpha measured empirically across multiple architectures. We propose the PIG (Physical Integrity Gate) Engine with the ADE
(Agent Delivery Engineering) protocol suite as an engineering countermeasure to entropy-driven disorder. Our findings establish silent failure not
as a bug to be fixed but as a manifestation of Intelligence Entropy -- a physical constraint to be managed through deterministic governance. We argue
that any engineering effort stabilizing the structure and order of agent systems participates in a unified mission: keeping intelligent systems
reliable as they grow in scale and complexity.
From: Dexing Liu [view email]
[v1]
Sat, 6 Jun 2026 13:22:07 UTC (409 KB)
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