The article critiques the 'remember everything' approach for AI memory in companies, advocating for separate current-state and historical views to prevent agents from using outdated information.
I’ve been spending an unreasonable amount of time thinking about company memory lately. At first, the problem looked simple. Important decisions disappear inside Slack threads, meeting notes and random documents. So you collect everything, make it searchable and let the agent retrieve the relevant context. Great. Problem solved. Everyone can go home. Except there’s a slightly terrifying failure mode. Imagine a team decides to use PostgreSQL in January. The decision is documented, along with all the reasoning behind it. Six months later, they migrate to MySQL for equally valid reasons. Now the memory contains two decisions. Both are real. Both are supported by evidence. Both can be retrieved successfully. But only one of them should guide the agent today. The memory system didn’t fail to store the information. Retrieval didn’t technically fail either. The agent found a perfectly relevant answer. It just found the wrong version of reality. And honestly, that feels more dangerous than the agent saying “I don’t know.” An agent with no memory asks questions. An agent with outdated memory confidently resurrects decisions everyone else forgot were dead. So I’m starting to think agents need two different views of memory: A current-state view containing what the team believes now A historical view containing previous decisions, rejected options and the evidence behind them The agent uses the first one to work. Humans use the second one when they need to understand why, investigate a mistake or challenge the current decision. Old decisions shouldn’t be deleted. But they probably shouldn’t compete with current ones every time an agent searches for context either. Full disclosure, this question came out of my work on Verbasil. The deeper I get into it, the less I believe that “more memory” automatically creates a better agent. Sometimes it just creates an AI with excellent recall and terrible judgment. How are you handling this? Do you let the agent see the entire history, or do you maintain a separate current version of the truth?
The article discusses the common failures of current AI memory solutions in production, such as stale facts, summary drift, and vendor lock-in, suggesting that the real bottleneck is memory governance rather than retrieval.
An analysis of the current state of AI memory systems, arguing that the focus has shifted from storing more data to defining how memory should behave—covering governance, observability, lifecycle management, and interoperability.
The author argues that AI agents have a state-integrity problem rather than a memory issue, proposing a State Ledger to distinguish historical facts from current state and track provenance.
The author argues that AI agent memory should focus on pruning data rather than hoarding, drawing parallels to human memory types (sensory, short-term, long-term) and suggesting that modeling after human memory can reduce token usage while maintaining high-quality context.
The author critiques the idea of agents remembering everything and introduces TrueMemory, a system that converts memories into trait claims with confidence and evidence to better calibrate agent behavior.