@yoheinakajima: i said 2025 was about agent memory (red) 2026 will be stateful agents (blue)
Summary
Yohei Nakajima predicts that 2025 will focus on agent memory and 2026 on stateful agents, arguing that better memory is the key unlock for truly improved AI agents.
View Cached Full Text
Cached at: 06/18/26, 10:05 AM
i said 2025 was about agent memory (red)
2026 will be stateful agents (blue) https://t.co/gZKgtvJtA5
Yohei (@yoheinakajima): i’ve said this in private convos so I’ll say it here…
“better memory” is the final unlock we need to get truly better agents, and 2025 is when we’ll see more of this
we have strong reasoning, tools for tools, plenty of frameworks, but memory/context management needs
Similar Articles
@yoheinakajima: okay i’m finally starting to get my mind wrapped around this whole “stateful” agent thing
Yohei Nakajima shares his growing understanding of stateful AI agents.
@yoheinakajima: https://x.com/yoheinakajima/status/2081741659260477666
This thread explores how the brain's dual memory systems (hippocampus and neocortex) offer lessons for building long-running AI agents, arguing that agents need a fast episodic capture and slow consolidation mechanism to avoid catastrophic interference, rather than relying solely on frozen models with temporary scaffolding.
@0xCodez: https://x.com/0xCodez/status/2089393338977829278
This article outlines a 12-step roadmap for AI Agent Engineers in 2026, focusing on seven interconnected pillars like context, tools, and memory, with Claude-based workflows to build reliable production agents.
AI agents have great recall. Zero memory hygiene. And nobody is talking about what that looks like at month six.
Discusses the overlooked problem of memory hygiene in AI agents, where long-term storage leads to stale and unreliable context, and questions whether the industry is ignoring a looming global issue.
Oracle Agent Memory as an Enterprise Memory Substrate for Long-Horizon AI Agents
Oracle introduces Agent Memory, a database-native memory substrate for long-horizon AI agents, achieving 93.8% accuracy with 10.7x fewer tokens compared to flat-history baselines.