Memory & State
What goes wrong when systems can't remember or trace — and what it looks like when they can.
Memory is what separates a tool from a partner. A tool does what you tell it, every time, with no recollection of last time. A partner remembers your project, your preferences, your decisions, and your history. Most AI systems today are tools pretending to be partners — they feel conversational, but they forget everything the moment the session ends. These terms name the failures that come from stateless systems and the capabilities that emerge when memory is done right.
Context Anxiety
The dread of re-explaining everything every session because the system has no persistent memory.
Ghost Knowledge
Facts that exist in the system but can't be traced to an author or source.
Context Rot
Deterioration of an agent's understanding over time due to limited memory and compaction.
State Loss
Failure to maintain awareness of prior actions.
Context Continuity
Seamless persistence of knowledge across sessions — the user never re-explains. The cure for Context Anxiety.
Memory Fidelity
The accuracy and completeness of what a system retains over time. The opposite of Context Rot.
Provenance Clarity
Every fact in the system is traceable to its author, source, and reasoning. The cure for Ghost Knowledge.
Recall Precision
The system retrieves exactly the right context at the right time — no noise, no gaps.
State Awareness
The system's ability to maintain and use awareness of prior actions within a session. The opposite of State Loss.
Selective Recall
The system's ability to retrieve only what's relevant rather than everything it knows. The difference between a useful memory and a noisy one.
Memory Bloat
Accumulated stored context that is no longer relevant but still gets retrieved, increasing noise and cost. The memory-layer equivalent of Prompt Bloat.