Layered Architecture

Hierarchical Memory & E2E Processing Cycle

Amigo's memory architecture implements a sophisticated hierarchy that optimizes both performance and precision through a complete end-to-end processing cycle:

Layer	Live-Session	Post-Processing
L2 (User Model)	Dimensional understanding Primary live access (90% of queries)	Merged snapshot across all checkpoints with proper information recontextualization + decay
L1 (Observations)	Contextual search index Path to L0	Accumulated into checkpoints to extract long-term patterns to propagate up to L2 (user model)
L0 (Raw Transcripts)	Perfect context preservation Targeted deep reasoning	Source for extracting observations guided by dimensional framework

Live Conversation Processing

During live interactions, the system operates primarily in a top-down fashion:

1. L2 Primary Access (90% of cases)

   • User model provides immediate dimensional context.

   • Critical information is instantly available with high precision.

   • No need for deeper retrieval for most interactions.

   • Ensures low-latency responses while maintaining quality.

2. L1 Index-Based Retrieval (when needed)

   • When user model lacks specific details, L1 observations serve as a contextualized search index.

   • Each observation contains standalone contextualized information linked to source sessions.

   • The L1 index allows us to find the L0 sessions in which the relevant information lives so that we get both the important information itself and its proximal context.

3. L0 Deep Reasoning (when needed)

   • For high-stakes decisions requiring perfect context, the system drills down to L0 through the L1 index.

   • Complete reasoning across raw transcripts with full L2 integration which incorporates both recent data (~20 most recent sessions depending on decay algorithm), the most recent full user model snapshot, the ~10 most relevant sessions most likely to contain important info, and relevant external events.

   • User model provides present complete context for interpreting and re-contextualizing raw information from the past.

   • Ensures perfect precision and contextualization where it matters most.

Post-Processing Memory Management

Amigo implements an efficient post-processing cycle that ensures optimal memory performance:

1. Session Breakpoint Management

   • Each conversation sessionized with clear breakpoints

   • Enables precise tracking of context boundaries

   • Allows efficient batch processing of memory operations

   • Maintains perfect contextual continuity across sessions

2. L0 → L1 Transformation

   • Raw transcripts analyzed using dimensional framework

   • Important observations extracted and contextualized

   • Each observation tagged with dimensional relevance

   • Linkages maintained to source contextual data

3. Checkpoint + Merge Pattern

   • L1 observations checkpointed by information volume and dimensional importance

   • Information decay modeled to prioritize persistence

   • Recent observations weighted more heavily in merge operations

   • Critical observations maintain perfect preservation regardless of age

4. L1 → L2 Synthesis

   • Checkpointed observations synthesized into dimensional understanding

   • Previous user model merged with new observations

   • Evolved understanding captured in updated model

Memory Evolution Handling

The end-to-end memory system uniquely handles information evolution:

1. Progressive Recontextualization

   • As new L1 observations accumulate, older information is recontextualized

   • Long-range patterns emerge through observation accumulation

   • Changing understanding (e.g., opinions evolving over time) properly captured

   • Perfect context maintained for critical information despite evolution

   • Contextual relationships preserved through associative binding mechanisms

2. Decay-Aware Persistence

   • Non-critical information naturally decays according to importance model

   • Critical information maintains perfect persistence regardless of age

   • Decay rates vary by dimensional importance defined in user model blueprints

   • Search efficiency improves as less relevant information naturally fades

   • Dimensional tagging ensures critical information doesn't decay even over long periods

3. Information Gap Processing

   • Dynamic detection of missing information during live sessions

   • Automatic generation of contextually-aware queries to fill gaps efficiently

   • Preservation of information confidence levels to guide retrieval decisions

   • Progressive refinement of understanding through targeted gap filling

This complete cycle ensures that Amigo's memory system delivers both perfect preservation of critical information and efficient operation across all memory access patterns, solving the fundamental challenges that plague traditional approaches.