Asanify AI

𝐘𝐨𝐮𝐫 𝐀𝐈 𝐚𝐠𝐞𝐧𝐭𝐬 𝐚𝐫𝐞 𝐥𝐞𝐚𝐫𝐧𝐢𝐧𝐠. 𝐓𝐡𝐞𝐲'𝐫𝐞 𝐚𝐥𝐬𝐨 𝐬𝐥𝐨𝐰𝐥𝐲 𝐠𝐞𝐭𝐭𝐢𝐧𝐠 𝐰𝐨𝐫𝐬𝐞. New paper on arXiv this week. MemEvoBench. First benchmark built to measure what happens to an LLM agent after thousands of real interactions. The finding that should stop every founder deploying agents cold, memory doesn't just accumulate. It drifts. Researchers tested frontier agents against three real-world failure modes. Adversarial memory injection. Noisy tool outputs. Biased user feedback. 36 risk types across 7 domains. Every model showed substantial safety degradation once the memory started getting polluted. The uncomfortable part, static prompt-based defenses didn't fix it. Most HR teams I talk to are deploying agents for screening, employee help desks, onboarding. The pitch is always the same, the agent learns your company, gets better over time. What nobody shows you in the demo, the same learning pipe is the attack surface. One biased feedback loop, and your "helpful" screening agent starts rejecting the wrong people. Silently. Here's what we're telling teams at Asanify before they turn on agentic HR: ✔️ Audit the memory writes, not just the outputs. ✔️ Rotate evaluation every 2-4 weeks against a frozen baseline. ✔️ Keep a kill switch that hard-resets agent memory to a known-good state. Agents that learn are the right bet. But "learn" and "drift" look identical from the outside, until a reporter calls you. What's your policy for catching agent memory drift once it's in production? Drop yours in the comments, I'm collecting patterns from HR and ops teams. #artificialintelligence #hrtech #futureofwork