continuous-agents-deployable-with-defense-in-depth
OUT derived (depth 7)
Created 2026-06-21T11:17:49+00:00
Continuous agents with persistent memory are safely deployable at scale — persistent memory's paradigm-extending capability combined with five independent defense dimensions provides sufficient safeguards for long-horizon autonomous operation.
Justifications
SL — Defense-in-depth could protect continuous agents, but prompt injection as primary concern means the instruction-following boundary is too porous
Antecedents (all must be IN):
- IN persistent-memory-transforms-agentic-from-episodic-to-continuous — Persistent memory (cross-session state consolidation) transforms the agentic paradigm — itself the culmination of the entire NLP evolution — from episodic tool use bounded by context windows into continuous autonomous operation with temporal coherence, enabling agents to pursue long-horizon goals across sessions rather than single-task episodes.
- IN llm-reliability-requires-five-independent-defense-dimensions — LLM reliability requires independent defenses across at least five dimensions — two control layers (training-time alignment, inference-time prompting) and three security surfaces (training data poisoning, prompt injection, architectural vulnerability) — with no single defense sufficient on its own.
Unless (any of these IN defeats this justification):
- IN prompt-injection-primary-security-concern — Prompt injection is the primary security concern for deployed LLM applications