🚨 This paper is why most agent demos won’t survive real users. Once you add tools, memory, and autonomy, every “prompt defense” breaks. This one doesn’t. Most “prompt injection defenses” break the moment you add agents, tools, or memory. The paper proposes a three-agent security pipeline plus a fourth evaluator agent. Each agent has a fixed role: generation, sanitization, policy enforcement. No debates. No vibes. Just layered defense. Every intermediate output is inspected, scored, and logged. The real breakthrough is Nested Learning. Instead of treating prompts as stateless events, each agent has a Continuum Memory System with medium-term and long-term memory. Prompts are embedded and matched semantically. If a new attack looks like something seen before, the agent reuses a verified response instead of regenerating one. This does three things at once. 1, security. Across 301 real prompt injection attacks spanning 10 attack families, the system achieved zero high-risk breaches. Not reduced risk. Zero. 2, observability. They introduce a new metric called OSR that measures how much security reasoning is exposed for audits. Counterintuitively, the configuration with the most transparency performed best overall. 3, sustainability. Semantic caching cut LLM calls by 41.6%, dropping latency from ~9 seconds to 150ms for cached paths. Fewer calls means lower cost, lower energy use, and lower carbon emissions without touching model weights. The takeaway is uncomfortable for most agent builders. Security doesn’t come from clever prompts. It comes from architecture, memory, and evaluation discipline. This is one of the clearest blueprints yet for production-grade, secure, and sustainable agentic AI. Read the full paper here: