> ## Documentation Index
> Fetch the complete documentation index at: https://docs.sense-lab.ai/llms.txt
> Use this file to discover all available pages before exploring further.

# Intelligence Layer

> LLM-driven extraction, memory critic, distiller, safety validation, and multi-strategy retrieval.

# Intelligence Layer

The Intelligence Layer uses LLMs and advanced algorithms to keep your memory store healthy, your retrieval sharp, and your agents onboarded faster.

## Extraction

Turns raw text (conversations, logs, documents) into structured memory operations using LLMs. The extractor classifies each piece of information as an **ADD**, **UPDATE**, **DELETE**, or **NOOP** operation.

## Auto Entity & Relationship Extraction

Automatically extracts entities (services, people, tools, infrastructure) and relationships (depends\_on, uses, manages, monitors) from decision traces and memory entries using LLMs. Extracted data is stored in dedicated tables with confidence scores, temporal validity, and trace provenance. Enable with `AMFS_AUTO_EXTRACT=true`.

## Memory Critic

Automated quality analyzer that scans the memory store and detects five issue classes:

* **Toxic** — repeated negative correlations
* **Stale** — entries not referenced in a long time
* **Contradictory** — conflicting entries for the same key
* **Uncalibrated** — confidence scores misaligned with outcome history
* **Orphaned** — entries with no links to any outcome or other entries

## Memory Distiller

Compacts large stores into smaller, higher-quality sets via pruning, consolidation, and bootstrap set generation for agent onboarding.

## Memory Safety Validator

Pre-write guardrails: contradiction detection, temporal consistency, confidence thresholds, and causal chain integrity.

## Multi-Strategy Retrieval

Combines semantic, BM25, temporal, and confidence signals via Reciprocal Rank Fusion (RRF). When a learned ranking model is trained, it automatically receives 30% weight in the fusion pipeline.

## Pro MCP Tools

| Tool                        | Description                                                                   |
| :-------------------------- | :---------------------------------------------------------------------------- |
| `amfs_critique`             | Run the Memory Critic and get a quality report                                |
| `amfs_distill`              | Trigger distillation (prune, consolidate, or generate bootstrap set)          |
| `amfs_validate`             | Validate a candidate memory before writing                                    |
| `amfs_retrain`              | Train the learned ranking model from outcome data                             |
| `amfs_calibrate`            | Learn optimal confidence multipliers from outcome history                     |
| `amfs_export_training_data` | Export decision traces as SFT/DPO/reward model datasets                       |
| `amfs_record_llm_call`      | Record an LLM call with model, tokens, cost, and latency                      |
| `amfs_graph_path`           | Find shortest trust-weighted path between two entities in the knowledge graph |
| `amfs_graph_query`          | Flexible graph edge search by relation, entity type, or confidence range      |