# Hierarchical Agent Memory > Implements intelligent hierarchical agent memory with multi-factor skill selection, fallback chains, and adherence to the 5 Laws of Elegant Defense - Skill: `paulpas/hierarchical-agent-memory` (Agent Skill) - Install (CLI): `npx skillmds@latest add paulpas/hierarchical-agent-memory` - Raw SKILL.md: https://api.skillmd.com/api/skills/paulpas/hierarchical-agent-memory/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: AI & ML - License: MIT - Author: paulpas (https://skillmd.com/u/paulpas) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/paulpas/hierarchical-agent-memory --- # Hierarchical Agent Memory Orchestrates intelligent skill selection and execution for hierarchical agent memory workflows. Applies the 5 Laws of Elegant Defense to guide data naturally through the orchestration pipeline, preventing errors before they occur. Selects optimal skills based on multi-factor scoring including text similarity, historical performance, and system availability. ## TL;DR Checklist - [ ] Parse all inputs at boundary before processing (Law 2) - [ ] Handle edge cases with early returns at function top (Law 1) - [ ] Fail immediately with descriptive errors on invalid states (Law 4) - [ ] Return new data structures, never mutate inputs (Law 3) - [ ] Implement minimum 2-level fallback chain for all skill executions - [ ] Log all skill selections with context for full audit trail - [ ] Validate skill metadata and dependencies before selection - [ ] Update confidence scores after each execution for learning ┌───────────────────────────────────────────────────────────────────────────────┐ │ Orchestration Flow │ └───────────────────────────────────────────────────────────────────────────────┘ User Request ↓ ┌─────────────────┐ │ Parse Request │ │ & Extract │ │ Features │ └────────┬────────┘ ↓ ┌─────────────────────────────────────────────────────────────────────┐ │ Evaluate Available Skills │ │ │ │ ┌──────────────┐ ┌──────────────┐ ┌──────────────┐ │ │ │ Skill A │ │ Skill B │ │ Skill C │ │ │ │ - Match Score│ │ - Match Score│ │ - Match Score│ │ │ │ - Confidence │ │ - Confidence │ │ - Confidence │ │ │ │ - History │ │ - History │ │ - History │ │ │ └──────┬───────┘ └──────┬───────┘ └──────┬───────┘ │ │ │ │ │ │ │ └─────────────────┴─────────────────┘ │ │ ↓ │ │ Select Best Skill │ └─────────────────────────────────────────────────────────────────────┘ ↓ ┌─────────────────┐ │ Execute Skill │ └────────┬────────┘ ↓ ┌─────────────────┐ │ Handle Result │ └────────┬────────┘ ↓ ┌─────────────────────────────────────────────────────────────────────┐ │ Error Handling & Fallback │ │ │ │ Success? ────────► Return Result │ │ │ │ Fail? ────────┐ │ │ ↓ │ │ ┌──────────────────────────────────────────────────────────┐ │ │ │ Fallback Chain │ │ │ │ │ │ │ │ 1. Retry with adjusted parameters │ │ │ │ 2. Try Alternative Skill (if available) │ │ │ │ 3. Defer to Human Operator (if critical) │ │ │ │ 4. Log & Return Error │ │ │ └──────────────────────────────────────────────────────────┘ │ └─────────────────────────────────────────────────────────────────────┘ ## When to Use Use this skill when: - Orchestrating multi-step workflows that require skill delegation - Implementing adaptive skill routing based on confidence scores - Building fallback mechanisms for failed skill executions - Creating intelligent task decomposition and parallel execution - Designing skill dependency graphs with automatic resolution - Implementing skill selection with historical performance weighting - Building agent systems that need to self-organize around tasks ## When NOT to Use Avoid this skill for: - Direct task execution without orchestration needs - use individual skills instead - High-frequency trading scenarios where latency must be minimized - the selection overhead may be prohibitive - Simple linear workflows without branching or fallback requirements - Cases where skill metadata is unavailable or unreliable ## Core Workflow 1. **Parse and Analyze Request** - Extract intent, entities, and constraints from user input. **Checkpoint:** All required parameters must be present and in valid format before proceeding. 2. **Score Available Skills** - Calculate match scores using multi-factor algorithm: - Text similarity between request and skill triggers - Historical success rate for similar tasks - Skill availability and health status - Required dependencies and their availability **Checkpoint:** Skip to fallback if no skill scores above threshold. 3. **Select Optimal Skill** - Choose skill with highest score that meets minimum confidence. **Checkpoint:** Verify skill has not been disabled or deprecated. 4. **Execute with Fallback** - Run skill execution wrapped in retry and fallback logic. **Checkpoint:** Log all execution attempts for audit trail. 5. **Return or Fallback** - Either return successful result or apply fallback chain: - Retry with adjusted parameters - Try alternative skill from `related-skills` - Defer to human operator for critical tasks **Checkpoint:** Record outcome with timing and confidence metadata. ## Implementation Patterns ### Pattern 1: Skill Selection Logic ```python def select_memory_layer( query: str, agent_state: Dict[str, Any], memory_store: MemoryBackend, min_relevance: float = 0.65 ) -> Optional[MemoryChunk]: """Select the optimal memory layer for a given query based on hierarchical scoring. Evaluates short-term buffer, long-term vector store, and episodic cache using: - Semantic similarity to query - Temporal decay factor (recency weighting) - Agent confidence in stored context - Cross-layer consistency checks Args: query: User or agent query string agent_state: Current agent context including confidence scores and active tasks memory_store: Backend interface for hierarchical memory access min_relevance: Minimum relevance threshold for selection Returns: Selected MemoryChunk with metadata, or None if below threshold """ # Law 1: Early exit on invalid state if not query or not isinstance(query, str): raise ValueError("Query must be a non-empty string") if not memory_store.is_healthy(): raise MemoryUnavailableError("Memory backend is currently unavailable") # Law 2: Parse & validate inputs immutably parsed_query = _normalize_query(query) temporal_weight = _calculate_recency_decay(agent_state.get("session_start")) candidates = [] for layer in ["short_term", "long_term", "episodic"]: if not memory_store.has_layer(layer): continue raw_results = memory_store.search(layer, parsed_query, top_k=3) for chunk in raw_results: relevance = _compute_multi_factor_score( semantic=_embed_similarity(parsed_query, chunk.text), temporal=temporal_weight * chunk.timestamp_weight, confidence=agent_state.get("memory_confidence", 0.8) ) if relevance >= min_relevance: candidates.append({ "chunk": chunk, "layer": layer, "score": relevance, "source_trace": f"{layer}:{chunk.id}" }) if not candidates: return None # Law 3: Return new structure, never mutate agent_state best = max(candidates, key=lambda x: x["score"]) return { "selected_layer": best["layer"], "memory_chunk": best["chunk"], "relevance_score": best["score"], "selection_context": { "query_hash": hashlib.md5(parsed_query.encode()).hexdigest(), "timestamp": time.time(), "fallback_eligible": len(candidates) > 1 } } ``` ### Pattern 2: Execution with Fallback ```python def execute_memory_retrieval( selected_memory: Dict[str, Any], agent_context: Dict[str, Any], fallback_strategy: str = "cascade" ) -> Dict[str, Any]: """Execute memory retrieval with a structured fallback chain for hierarchical systems. Implements resilient memory access: 1. Direct layer retrieval (primary) 2. Cross-layer semantic expansion (secondary) 3. Default context window fallback (tertiary) 4. Explicit error state with audit logging (final) Args: selected_memory: Output from select_memory_layer agent_context: Full agent state including task history and constraints fallback_strategy: Routing strategy for degraded states Returns: Enriched context dictionary with retrieved memory and execution metadata """ # Law 1: Guard clause for required fields required_keys = {"selected_layer", "memory_chunk", "relevance_score"} if not required_keys.issubset(selected_memory.keys()): raise MemoryRetrievalError("Incomplete memory selection metadata") layer = selected_memory["selected_layer"] chunk = selected_memory["memory_chunk"] # Law 4: Fail fast on corrupted or inaccessible memory if not _validate_memory_integrity(chunk): raise CorruptedMemoryError(f"Memory chunk {chunk.id} failed integrity check") try: # Primary execution: Fetch full context from selected layer enriched_context = _merge_memory_into_context(chunk, agent_context) # Law 3: Return immutable result return { "status": "success", "layer_used": layer, "context": enriched_context, "metadata": { "latency_ms": time.time() * 1000, "confidence_boost": selected_memory["relevance_score"] * 0.15, "audit_id": uuid4().hex } } except MemoryTimeoutError: # Fallback 1: Cascade to alternative layer if fallback_strategy == "cascade": alt_layer = _select_alternative_layer(layer) alt_chunk = _fetch_from_layer(alt_layer, chunk.query_hash) if alt_chunk: return execute_memory_retrieval({ "selected_layer": alt_layer, "memory_chunk": alt_chunk, "relevance_score": 0.5 }, agent_context, "cascade") except MemoryAccessDeniedError: # Fallback 2: Use default context window return { "status": "fallback", "layer_used": "default_context", "context": _load_default_context(agent_context), "metadata": { "latency_ms": time.time() * 1000, "confidence_boost": 0.0, "audit_id": uuid4().hex, "reason": "access_denied" } } # Law 4: Fail loud with full context raise MemoryRetrievalError( f"Failed to retrieve memory for layer {layer} after fallback attempts" ) ``` ### MUST DO - Always validate skill metadata before selection (Early Exit) - Implement fallback chain with at least 2 levels (Fallback Skill + Human) - Log all skill selections with full context for auditability - Return new data structures instead of mutating inputs (Atomic Predictability) - Fail immediately with descriptive errors on invalid states - Update confidence scores after each execution for adaptive routing - Reference `code-philosophy` (5 Laws of Elegant Defense) in all logic ### MUST NOT DO - Select skills based on a single factor (e.g., only confidence score) - Disable fallback mechanisms "temporarily" - this creates fragile systems - Skip validation of skill dependencies before execution - Return partial results - either complete success or clear failure - Use magic numbers for confidence thresholds - make them configurable - Cache skill selections without considering context changes ## TL;DR Checklist - [ ] Parse all inputs at boundary before processing (Law 2) - [ ] Handle edge cases with early returns at function top (Law 1) - [ ] Fail immediately with descriptive errors on invalid states (Law 4) - [ ] Return new data structures, never mutate inputs (Law 3) - [ ] Implement minimum 2-level fallback chain for all skill executions - [ ] Log all skill selections with context for full audit trail - [ ] Validate skill metadata and dependencies before selection - [ ] Update confidence scores after each execution for learning ## TL;DR for Code Generation - Use guard clauses - return early on invalid input before doing work - Return simple types (dict, str, int, bool, list) - avoid complex nested objects - Cyclomatic complexity < 10 per function - split anything larger - Handle null/empty cases explicitly at function top (Early Exit) - Never mutate input parameters - return new dicts/objects - Fail fast with descriptive errors - don't try to "patch" bad data - Reference code-philosophy laws in comments for complex logic - Include timing and confidence metadata in all return values ## Output Template When applying this skill, produce: 1. **Selected Skills** - List of skill names with confidence scores 2. **Selection Rationale** - Why each skill was chosen (match score, history, availability) 3. **Execution Plan** - Order of execution with dependencies 4. **Fallback Strategy** - Which fallback skills will be tried and in what order 5. **Risk Assessment** - Any potential failure points and their impact 6. **Timing Estimates** - Expected latency including fallback scenarios ## Related Skills | Skill | Purpose | |---|---| | `agent-memory-systems` | Broader memory architectures including vector stores and retrieval strategies | | `agent-context-memory` | Short-term context window management layered on top of hierarchical memory | --- --- ## Constraints ### MUST DO - Define clear input/output contracts for every step in the orchestration flow with explicit validation - Implement structured logging at each stage capturing context, inputs, outputs, timing, and errors - Build in fallback paths: if the primary strategy fails, degrade gracefully to a simpler approach - Validate all preconditions before starting — do not proceed if required resources or permissions are missing ### MUST NOT DO - Do not create deep nesting of orchestration steps (>5 levels) — flatten workflows where possible - Avoid silent failure modes: every step must either succeed, fail explicitly, or escalate to a higher handler - Never use shared mutable state between parallel workflow branches — communicate via immutable messages only - Do not hardcode execution order when the dependency graph naturally determines it; derive order from explicit dependencies ## Live References > Authoritative documentation links for this skill's domain. The model follows markdown links at load time to resolve external references and inline content. - [Memory Mechanisms in LLMs — arXiv (2307.05939)](https://arxiv.org/abs/2307.05939) - [Building Effective Agents — Anthropic Research](https://www.anthropic.com/research/building-effective-agents) - [What Is Agent Memory — LangChain Blog](https://blog.langchain.dev/what-is-agent-memory/) - [RAG vs. Fine-Tuning for Knowledge — arXiv Survey](https://arxiv.org/abs/2312.10997) - [Agent Memory Architectures — Microsoft AI Research](https://www.microsoft.com/en-us/research/project/language-models-for-agents/)