How do AI agents remember context across a long project?

A context window is not memory

The window is capped, re-filled each session, and has no notion of a fact versus a one-off versus a standing rule. Over a long project an agent has to recall a decision from months ago without re-reading everything — a storage-and-retrieval problem the window doesn't solve.

Give agents a source of truth to navigate

Most agent failures come from losing the thread of your codebase and re-deriving it wrong. Serving the canonical answer on demand (this is trovex's job) keeps them fast and correct instead of expensive and confidently wrong.

Layer memory for long relationships

For an agent that holds a relationship over months, memory is an architecture: a rules store for what's always true, a decision journal for why, a history log — and a deliberate path to promote a repeated decision into a rule.

Straight answers.

Won't a larger context window fix this?

It helps a little and gets expensive fast. The window is working space, not durable memory; recall over months is a retrieval problem, not a size one.

Why do agents 'forget' mid-task?

They don't remember, they re-derive — and re-derivation drifts. Holding the canonical state outside the model (in a source of truth it queries) is the fix.

Is this just RAG?

Retrieval is one layer. Real agent memory separates facts, decisions, and history, with a promotion path RAG alone doesn't have.