retrieval-policy

name	retrieval-policy
description	Agent retrieval policy for intelligent memory search. Use when implementing memory queries to detect capabilities, classify intent, route through optimal layers, and handle fallbacks. Provides tier detection, intent classification, fallback chains, and full explainability for all retrieval operations.
license	MIT
metadata	{"version":"1.0.0","author":"SpillwaveSolutions"}

Retrieval Policy Skill

Intelligent retrieval decision-making for agent memory queries. The "brainstem" that decides how to search.

When to Use

Use Case	Best Approach
Detect available search capabilities	retrieval status
Classify query intent	retrieval classify <query>
Route query through optimal layers	retrieval route <query>
Understand why a method was chosen	Check explainability payload
Handle layer failures gracefully	Automatic fallback chains

When Not to Use

• Direct search operations (use memory-query skill)
• Topic exploration (use topic-graph skill)
• BM25 keyword search (use bm25-search skill)
• Vector semantic search (use vector-search skill)

Quick Start

# Check retrieval tier
memory-daemon retrieval status

# Classify query intent
memory-daemon retrieval classify "What JWT issues did we have?"

# Route query through layers
memory-daemon retrieval route "authentication errors last week"

Capability Tiers

The system detects available layers and maps to tiers:

Tier	Name	Layers Available	Description
1	Full	Topics + Hybrid + Agentic	Complete cognitive stack
2	Hybrid	BM25 + Vector + Agentic	Keyword + semantic
3	Semantic	Vector + Agentic	Embeddings only
4	Keyword	BM25 + Agentic	Text matching only
5	Agentic	Agentic only	TOC navigation (always works)

Tier Detection

memory-daemon retrieval status

Output:

Retrieval Capabilities
----------------------------------------
Current Tier:    2 (Hybrid)
Available Layers:
  - bm25:    healthy (2847 docs)
  - vector:  healthy (2103 vectors)
  - agentic: healthy (TOC available)
Unavailable:
  - topics:  disabled (topics.enabled = false)

Query Intent Classification

Queries are classified into four intents:

Intent	Triggers	Optimal Strategy
Explore	"browse", "discover", "what topics"	Topics-first, broad fan-out
Answer	"what did", "how did", "find"	Hybrid, precision-focused
Locate	Identifiers, exact phrases, quotes	BM25-first, exact match
Time-boxed	"yesterday", "last week", dates	Time-filtered, sequential

Classification Command

memory-daemon retrieval classify "What JWT issues did we debug last Tuesday?"

Output:

Query Intent Classification
----------------------------------------
Intent:          Answer
Confidence:      0.87
Time Constraint: 2026-01-28 (last Tuesday)
Keywords:        [JWT, issues, debug]
Suggested Mode:  Hybrid (BM25 + Vector)

Fallback Chains

Each tier has a predefined fallback chain:

Tier 1: Topics → Hybrid → Vector → BM25 → Agentic
Tier 2: Hybrid → Vector → BM25 → Agentic
Tier 3: Vector → BM25 → Agentic
Tier 4: BM25 → Agentic
Tier 5: Agentic (no fallback needed)

Fallback Triggers

Condition	Action
Layer returns 0 results	Try next layer
Layer timeout exceeded	Skip to next layer
Layer health check failed	Skip layer entirely
Min confidence not met	Continue to next layer

Stop Conditions

Control query execution with stop conditions:

Condition	Default	Description
max_depth	3	Maximum drill-down levels
max_nodes	50	Maximum nodes to visit
timeout_ms	5000	Query timeout in milliseconds
beam_width	3	Parallel branches to explore
min_confidence	0.5	Minimum result confidence

Intent-Specific Defaults

Intent	max_nodes	timeout_ms	beam_width
Explore	100	10000	5
Answer	50	5000	3
Locate	20	3000	1
Time-boxed	30	4000	2

Execution Modes

Mode	Description	Best For
Sequential	One layer at a time, stop on success	Locate intent, exact matches
Parallel	All layers simultaneously, merge results	Explore intent, broad discovery
Hybrid	Primary layer + backup, merge with weights	Answer intent, balanced results

Explainability Payload

Every retrieval returns an explanation:

{
  "tier_used": 2,
  "tier_name": "Hybrid",
  "intent": "Answer",
  "method": "bm25_then_vector",
  "layers_tried": ["bm25", "vector"],
  "layers_succeeded": ["bm25", "vector"],
  "fallbacks_used": [],
  "time_constraint": "2026-01-28",
  "stop_reason": "max_results_reached",
  "results_per_layer": {
    "bm25": 5,
    "vector": 3
  },
   
   

Displaying to Users

## Retrieval Report

Method: Hybrid tier (BM25 + Vector reranking)
Layers: bm25 (5 results), vector (3 results)
Fallbacks: 0
Time filter: 2026-01-28
Execution: 234ms
Confidence: 0.87

Skill Contract

When implementing memory queries, follow this contract:

Required Steps

1. Always check tier first:

   memory-daemon retrieval status
   

2. Classify intent before routing:

   memory-daemon retrieval classify "<query>"
   

3. Use tier-appropriate commands:

   • Tier 1-2: teleport hybrid
   • Tier 3: teleport vector
   • Tier 4: teleport search
   • Tier 5: query search

4. Include explainability in response:

   • Report tier used
   • Report layers tried
   • Report fallbacks triggered

Validation Checklist

Before returning results:

• Tier detection completed
• Intent classified
• Appropriate layers used for tier
• Fallbacks handled gracefully
• Explainability payload included
• Stop conditions respected

Configuration

Retrieval policy is configured in ~/.config/agent-memory/config.toml:

[retrieval]
default_timeout_ms = 5000
default_max_nodes = 50
default_max_depth = 3
parallel_fan_out = 3

[retrieval.intent_defaults]
explore_beam_width = 5
answer_beam_width = 3
locate_early_stop = true
timeboxed_max_depth = 2

[retrieval.fallback]
enabled = true
max_fallback_attempts = 3
fallback_timeout_factor = 0.5

Error Handling

Error	Resolution
All layers failed	Return Tier 5 (Agentic) results
Timeout exceeded	Return partial results with explanation
No results found	Broaden query or suggest alternatives
Intent unclear	Default to Answer intent

Integration with Ranking

Results are ranked using Phase 16 signals:

Signal	Weight	Description
Salience score	0.3	Memory importance (Procedure > Observation)
Recency	0.3	Time-decayed scoring
Relevance	0.3	BM25/Vector match score
Usage	0.1	Access frequency (if enabled)

See Command Reference for full CLI options.