How to Set Memory Boundaries for AI Agents - Typed Categories for Context Retention
Setting Memory Boundaries for AI Agents
The question of what an AI agent should remember - and for how long - is harder than it looks. Without clear boundaries, agent memory becomes a dump of everything that happened, and the useful signal gets buried in noise.
The Flat Memory Problem
Most agent memory implementations store everything in a single list or vector database. Past conversations, user corrections, task outcomes, and preferences all live in the same space. When the agent retrieves context, it pulls a mix of relevant and irrelevant memories that confuse the model and waste tokens.
The result is an agent that sometimes remembers helpful details and sometimes surfaces completely unrelated context from weeks ago. It feels random because, functionally, it is.
Typed Memory Categories
The approach that works is separating memory into distinct typed categories with different retention rules.
User preferences are long-lived. The user prefers dark mode, uses VS Code, deploys to Vercel. These rarely change and should persist indefinitely.
Project context is medium-lived. The current branch name, the tech stack, the deployment target. These are relevant for days or weeks and should be scoped to a specific project.
Feedback and corrections are the most valuable category. When the user says "no, do it this way instead," that correction should be stored with high priority and surfaced whenever a similar situation arises.
Task history is short-lived. What the agent did in previous sessions matters for continuity but becomes noise after a few days.
Why Categories Matter
Each category gets its own retrieval logic. When the agent starts a new task, it loads user preferences and project context automatically. Feedback is retrieved based on task similarity. Task history is only loaded when the user explicitly references a previous session.
This prevents the common failure where an agent applies a correction from one project to a completely different project, or where stale task context overrides current instructions.
Fazm is an open source macOS AI agent. Open source on GitHub.