@dair_ai
New research on agent memory. Agent memory is evaluated on chatbot-style dialogues. But real agents don't chat. They interact with databases, code executors, and web interfaces, generating machine-readable trajectories, not conversational text. The key to better memory is to preserve causal dependencies. Existing memory benchmarks don't actually measure what matters for agentic applications. This new research introduces AMA-Bench, the first benchmark built for evaluating long-horizon memory in real agentic tasks. It spans six domains including web, text-to-SQL, software engineering, gaming, and embodied AI, with both real-world trajectories and synthetic ones that scale to arbitrary lengths. The findings are interesting. Many existing agent memory systems that outperform baselines on dialogue benchmarks actually underperform simple long-context LLMs on agentic tasks. Even GPT 5.2 only achieves 72.26% accuracy. To address this, they propose AMA-Agent with a causality graph and tool-augmented retrieval, achieving 57.22% average accuracy and surpassing the strongest baselines by 11.16%. Why it matters? Agent memory needs to preserve causal dependencies and objective information, not just similarity-based retrieval. This benchmark exposes where current memory systems actually break. Paper: https://t.co/GX0GaHsijN Learn to build effective AI agents in our academy: https://t.co/LRnpZN7L4c
