Autonomous agents—LLMs, robotic controllers, trading systems, navigation agents—increasingly operate in environments where actions are irreversible and context is bounded. Beyond a complexity threshold, these agents lose track of accumulated constraints and undergo a phase transition from convergent to non-convergent operation.
This is not a performance degradation. It is a structural regime change: the agent begins making decisions that are locally reasonable but globally incoherent. The failure is silent, unrecoverable, and currently unaddressed.
A bounded external channel (<10% of context capacity) stores only the constraint-relevant state descriptors generated by the agent itself. At each decision cycle, these descriptors are reinjected into the agent’s input. No modification is made to the agent’s parameters, architecture, or environment.
The invention prevents phase transitions through purely exogenous reinjection of self-generated constraint state. The agent, the environment, and the model remain identical with and without the mechanism.
Channel capacity is not a tuned hyperparameter—it is structurally derivable from the system’s intrinsic forgetting distribution and constraint contraction rate.
reinjection
(worst arch.)
validated
Validated across three heterogeneous agent architectures (Opus, GPT, Gemini). The effect is binary at the regime level: 0% structural drift with reinjection versus 25–44% without. Per-step decision quality is indistinguishable between conditions—the mechanism does not improve the agent, it prevents its regime collapse.
The bridge utility is conditional: it activates only when constraint-induced information demand exceeds the agent’s local reconstructibility. When agents can self-reconstruct, the mechanism correctly has no effect.
The mechanism applies to any system satisfying three structural conditions: irreversible action space, bounded decision context, and constraint-relevant selectivity. This includes:
The inventive step is domain-independent: the bridge prevents regime transitions in any bounded-context decision system operating under irreversible constraints, whether the irreversibility is informational, physical, financial, or temporal.
Executive summary and technical brief available on request.
Davide Lugli · Independent researcher · Carpi, Italy