Memory

Perfect recall and contextualization for critical information

The Fundamental Memory Challenge

In critical enterprises, traditional memory systems fundamentally break down. They treat information with incorrectly opinionated importance and fail to maintain proper domain-specialized context over time. When making high-stakes decisions, this approach is unacceptable.

Critical functions need memory systems optimized for the use cases they serve, not for general performance benchmarks. The only important measure of the quality of a memory system is the statistical confidence the agent can achieve on memory-dependent tasks.

Amigo's Functional Memory System solves this by:

1. Guaranteeing customizable precision and contextualization for critical information

2. Maintaining perfect preservation and retrieval for important information and its proximal data for recontextualization against the current real-time local context

3. Using the custom user dimension definition-driven user model as a blueprint for memory and synthesis operations

4. Detecting and contextualizing information gaps using user model snapshot proactively and efficiently

Perfect Context Preservation

Traditional memory systems fail because they can't determine:

• What information deserves perfect preservation

• How to maintain contextual relationships over time

• When to recontextualize information based on new understanding

Amigo's layered architecture solves this by maintaining perfect associative binding between critical information and its context. When you need vital facts, you get them with their complete context—every time.

Traditional Memory Approach	Amigo Functional Memory
Uniform or unspecialized treatment of all information	Critical information identified and contextualized through custom user dimensions
Context fully/partially lost or incorrectly contextualized over time due to non-domain-specific processing	Perfect context maintained through L0 binding informed by custom user dimensions
Simple metadata filtering for retrieval	Contextualized proximity-based search guided by user model-informed memory topology
No recontextualization capability	Information evolves via continuous recontextualization powered by custom user dimensions and custom information decay

User Model: The Memory Blueprint

The user model is the functional blueprint that guides the entire memory system:

1. Dimensional Framework: Defines what information requires perfect preservation and the preservation methodology.

2. Memory Navigation: Guides and contextualize search to and reasoning over the important information and its proximal data.

3. Contextual Conditioning: Provides critical present snapshot context for interpretation or recontextualization of past information.

4. Information Gap Detection: Intelligently identifies what information is missing for the current real-time context.

Real-World Example:

When a patient reports "feeling stress in their leg after exercising," a generic system might simply search for similar phrases. Amigo's approach:

1. User model consultation: Identifies past leg injury from user dimensions

2. Contextualized search: Generates targeted query focused on injury context

3. L0 drill-down: Retrieves complete contextual information from relevant sessions

4. Information evolution tracking: Distinguishes between temporary pain and chronic condition

This allows the system to provide responses that account for the full context—something generic memory systems fundamentally can't do.

Layered Memory Architecture

Amigo implements a functionally-aligned memory architecture that ensures perfect recall while optimizing resources:

1. L0 Complete Context Layer: Preserves full conversation transcripts with 100% recall of critical information, maintaining all contextual nuances and enabling deep reasoning across historical interactions.

2. L1 Observations & Insights Layer: Extracts structured insights from raw conversations, identifying patterns and relationships along user dimensions. This layer maps insights according to dimensional importance, facilitating efficient search and retrieval of relevant information when needed.

3. L2 User Model Layer: Consolidates these insights into multidimensional understanding for each user, providing a blueprint for identifying critical information and detecting knowledge gaps. This layer guides the contextual interpretation of all information, ensuring the system responds appropriately based on comprehensive user understanding while optimizing memory resources.

Key Features

1. Recent Information Guarantee

Amigo guarantees that recent information (last n sessions based on information decay for use case) is always available for:

• Full reasoning over complete context

• Perfect recall of all details

• Recontextualization based on new understanding

This solves the fundamental problem of information decay that plagues traditional systems.

2. Perfect Search Mechanism

When information is needed, Amigo:

1. Identifies specific information gaps using the user model

2. Conducts targeted searches near known critical information

3. Drills down to L0 when needed for complete context

4. Maintains perfect precision for all critical information

3. Information Evolution Handling

Unlike traditional systems that struggle with changing information (like a patient reporting different moods), Amigo:

1. Uses checkpoint + merge operations for user models

2. Accumulates observations by dimension over time

3. Identifies longer-range patterns beyond individual sessions

4. Properly recontextualizes information as understanding evolves

Example: As a child, you hated that your parents lectured you. At age 30, you are thankful for those lectures. Amigo's memory system understands this evolution rather than treating them as conflicting facts.

4. Enterprise Customizability

Amigo's memory architecture is fully customizable for enterprise-specific needs through a comprehensive implementation process that our Forward Deployed Engineers will work with you on.

1. Critical Function Assessment: Identify functions requiring perfect memory and map critical information types & hierarchy based on your use cases.

2. Memory Design: Configure memory topology and define user dimensions + parameters.

3. Integration & Deployment: Deploy memory system, connect to existing data sources and initialize user models.

4. Verification & Optimization: Validate functional performance, optimize dimensional parameters to increase performance where necessary.

Memory + Knowledge ↔ Reasoning Bandwidth

Solving a tough calculus proof and orchestrating a six‑month product launch both require the same cognitive dance: zoom out to see the large‑scale plan, then zoom in on the next sub‑problem, carry its result upward, and repeat. If the channel between long‑term memory / domain knowledge and the live reasoning engine is narrow, this dance falls apart.

Amigo’s memory stack widens that channel in three ways:

1. Dimensional Granularity The user‑model dimensions decide what slice of information is needed and how coarse or fine it should be—L2 summaries, L1 observations or full L0 transcripts.

2. On‑demand Re‑contextualisation Retrieved facts are instantly re‑embedded inside the current conversational frame so that old information drives the new optimisation problem, not yesterday’s.

3. Bandwidth‑Sensitive Abstraction Control The system surfaces only the relevant context, avoiding token‑window overload while still giving the reasoning engine enough depth to plan multiple steps ahead.

Why Bandwidth Matters (quick view)

Long‑range work—anything from multi‑step maths to end‑to‑end project delivery—demands fast shifts between abstract strategy and granular evidence. Amigo widens this bridge via the dimensional framework, live re‑embedding, and dependency‑aware fetching.

See Advanced Topics › Memory‑Reasoning Bridge for the full pipeline and examples.

For a step‑by‑step view of the bandwidth pipeline—including Outer ↔ Inner World stages and real‑world examples—see Advanced Topics › Memory‑Reasoning Bridge.

Conclusion: Memory That Works When It Must

In critical industries, memory that works "most of the time" is memory that doesn't work at all. Amigo's Functional Memory System delivers:

• Perfect recall of critical information

• Complete preservation of vital context

• Efficient identification of information gaps

• Understanding of information evolution over time

For functions where failure isn't an option, Amigo provides memory that works when it must.