Create 3 outlines of novel and good ways to approach this problem:

The Core Problem: Creating a Proactive, Conversational Memory for a Voice Agent
At its heart, the challenge is to build a memory system for an AI voice agent that makes it feel less like a search engine and more like a human assistant with a shared history. The agent must be able to recall and utilize past conversations to inform the present one, creating an experience that is both continuous and contextually aware.
This broad challenge breaks down into several distinct, difficult sub-problems, all governed by one non-negotiable, hard constraint: extremely low latency. In a voice conversation, any perceptible delay beyond a few hundred milliseconds shatters the illusion of natural interaction. The user must feel that the agent's memory is an instantaneous faculty, not a database being queried.
Here are the specific problems we must solve under this constraint:
1. The Problem of Instantaneous, Relevant Recall.
When a user is speaking, they are constantly referencing concepts, projects, and people mentioned in prior conversations. The agent needs to "remember" this related history in real-time. If the user mentions "Project Titan," the agent should, at that moment, have the relevant context about Project Titan ready, without the user having to explicitly say, "Search for my notes on Project Titan." The system must bridge the gap between the live conversation and the vast archive of past conversations instantly.
2. The Problem of User Intent vs. Literal Words.
Human language is not a database query language. A user will not use precise keywords. They will ask, "How did we do on money last quarter?" The memory system must be capable of understanding that this query is conceptually related to stored information about profits, revenue, and financial reports. It has to retrieve information based on the user's intent, not their literal vocabulary. This semantic gap is a massive challenge when the primary constraint is speed, as deep semantic analysis is computationally expensive.
3. The Problem of Contextual Filtering and Precision.
A human assistant knows not to bring up irrelevant information. If asked about "the marketing budget for Project Titan," they won't bring up notes about "the server budget for Project Phoenix." The memory system must be able to perform highly precise, filtered retrieval. It needs to handle complex logical queries that include concepts, entities, and negative constraints (e.g., "A but not B") to avoid presenting the user with a wall of irrelevant or "loosely similar" information, which erodes trust.
4. The Problem of Proactivity without Interruption.
The ultimate goal is for the agent to be proactive—to offer relevant information before it's even asked for. This creates the most difficult problem: how does the system know when to surface a memory and what memory is appropriate? It must constantly analyze the live conversation to anticipate the user's needs. It has to decide, in real-time, that a piece of historical information is relevant enough to be "recalled" from long-term storage into a "working memory," ready to be used. This entire process of anticipation and background retrieval must be invisible to the user and not interfere with the primary task of listening to them speak.
In summary, the fundamental problem is this:
How do we design a system that can continuously and proactively search a massive, ever-growing archive of a user's entire conversational history, understand the semantic intent of their live speech, retrieve only the most precise and relevant slivers of information, and have that information ready to use in a natural, conversational response, all within the unforgiving time constraints of a live, spoken conversation?