I know everybody seems to want the agent to remember every conversation they've ever had with it, but I just don't see the value in that. In fact, it seems to hurt productivity to have the agent second guessing me based on something I said yesterday. Every time I've used any memory system, the agent gets distracted from the current tasks based on previous conversations and branches of development...often comingling unrelated projects (I work on code for work, open source projects, a bunch of unrelated side projects, etc.) and trying to satisfy requirements that don't make sense.

I've stopped trying to achieve general "memory". I just ask the agent to thoroughly, but concisely, document each project. If it writes developer documentation and a development plan/roadmap, as though a person was going to have to get up to speed and start working on the project, it provides all the information the agent needs tomorrow or next week to pick up where we left off.

The agent is not my friend. I don't need it to remember my birthday or the nasty thing I said about React last week. I need it to document what anyone, agent or human, would need to know to get productive in a particular repo, with no previous knowledge of the project.

Good, concise, developer and user documentation and a plan with checklists solves every problem people seem to think "memory" will solve: It tells the agent what tech stack to use (we hashed it out in planning), it tells it what commands it needs to run and test the app, it covers the static analysis tools in use (which formalizes code style, etc. in a way a vague comment I made a month ago cannot), and it is cheap. Markdown files are the native tongue of agents. No MCP, no skills, no API needed. Just read the file. It works for any agent, any model, and any human just getting started with the project.

Basically, I think memory makes agents dumber and less useful. I want it to focus on the task at hand.

You're right but I think you're describing flat memory. The agent gets distracted because every old fact has the same weight as the current one. That's a salience problem.

What works in production for me is typed memory with very different decay curves. Personality and relationships are essentially permanent. Preferences fade in months. Stated intent fades in weeks. Emotion and events fade in days. Reinforcement (repeated recall) keeps things alive regardless of type.

Cross-project co-mingling stops because project-specific stuff actually decays out of relevance while who the user is persists. There's also a filter on what even gets written, which scopes between globally and locally-relevant information and writes accordingly (if at all). Most of the noise you're describing comes from systems that store everything they observe.

Flat memory failing is real. Memory failing in general is a stronger claim than that.

I appreciate your comment, and can relate. I tested a couple of "memory" systems, doing some heavy lifting or seemingly implementation of theories (layering, hot memory, etc), I can't really tell if they improve performance, quality or reliability on a task. But they do increase the overhead, for the LLM and for me, that's for sure.

One problem I have is that now CLAUDE.md or skills tend to get version controlled within projects, I suspect they could get in the way sometimes.

There is already so much fatigue induced by these systems, adding another one willingly does sound crazy.

Yeah it's that lack of perfect recall, imo, that gives rise to intelligence and progress.

If we humans just did exactly what we did yesterday, what progress?

It's baked into the immutable constants of the universe for us; entropy, signal attenuation over distances... information breaks down over time.

Because of this all human social statistics trend towards zero with intentional conservatism. Progress is or collapse is all the universe affords. It doesn't seem interested in conservatism at all.

You still have to worry about handing off state into the next session, but you don’t want it loading (“just naive-read the files”) your stack of documents at every turn . It goes against the idea of progressive disclosure. Progressive disclosure scales.

I can't see any value in having a global memory either, but can see the value for a local memory of a specific line of work. I.e. when implementing several features in a row that are related, you want the agent to remember what it did in the last chat.

I prefer ticketing systems for AI. I dont care that it forgets what I did last week, I just need it to be able to compact its own memory and grab the next task once done.

I'm just thinking of youtube or amazon type algorithms applying here.

me: "Hi AI, can you debug this SQL Statement?"

ai: "Well,based on your passion for garden hoses and extensive research of refrigerators, I'm going to guess you really want to discuss that"

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