> tpl_air_039
Agent Memory and State Specification
Production architecture and engineering specification defining autonomous agent short-term working memory (scratchpad / conversation context), long-term episodic memory, semantic memory retrieval, state compaction pipelines, and TTL pruning policies.
Agent memory specification standardizing short-term scratchpads, episodic long-term recall, and state compaction.
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Problem Solved
Agents suffer catastrophic context amnesia between sessions or drown in bloated token context windows, repeating previous user mistakes, hallucinating contradictory historical facts, and exploding inference costs.
When to Use
- •Engineering persistent memory systems for customer-facing AI agents requiring cross-session recall and user personalization
- •Designing memory compaction and hierarchical summarization pipelines to manage long-running multi-turn agent tasks
- •Implementing strict GDPR/CCPA personal data deletion and encryption-at-rest policies on stored agent memories
When NOT to Use
- •For simple stateless one-turn LLM completions or standard translation APIs (no persistent memory needed)
- •For overarching corporate enterprise data warehouse retention policies (use TPL-AIM-002)
5 Template Sections & Structural Outline
Partitioning memory across three functional tiers: Tier 1 Working Memory (in-context short-term scratchpad for the current task), Tier 2 Episodic Memory (chronological historical interactions and tool executions), Tier 3 Semantic Memory (distilled facts, user preferences, and entity knowledge).
Designing recursive summarization: When conversation threads approach context window limits (e.g. 75% capacity), an asynchronous worker summarizes past dialogue rounds, updates the rolling memory block, and prunes raw token history.
Extracting durable facts using structured output parsing: Identifying preferences, past decisions, constraints, and organizational context, storing them as key-value pairs or property graphs linked to the user ID.
Querying stored memories: Combining vector similarity search (dense embeddings) with metadata filtering (recency, user ID, task domain) to dynamically retrieve the top-K most relevant memory chunks into the agent context.
Engineering privacy into memory: Tagging memory objects with classification labels (PII, Financial, General). Enforcing automated TTL expiration and cryptographic hard-deletion pipelines for GDPR compliance.
Completion Instructions
Independent Review Checklist
- All mandatory sections completed
- No secrets or passwords included
- Executive sponsor sign-off obtained
Agent Memory and State Specification - Worked Case Study
Fictional Entity: Executive Strategic AI Copilot (Serving 1,200 Enterprise Executives across Multi-Month Engagements)
Real-world production case study demonstrating complete operational adoption for Executive Strategic AI Copilot (Serving 1,200 Enterprise Executives across Multi-Month Engagements).
- •Reduced average prompt token footprint by 73% through hierarchical memory compaction pipelines
- •Achieved 98.6% user personalization accuracy across multi-month strategy projects
- •Implemented automated 24-hour GDPR Article 17 cryptographic memory hard-deletion across all vector shards
Frequently Asked Questions
What is the architectural difference between Working Memory, Episodic Memory, and Semantic Memory?
Working Memory is ephemeral in-context state (scratchpad, immediate variables) held in RAM or prompt context for the active turn. Episodic Memory is an append-only log of historical interactions and tool results stored in persistent databases. Semantic Memory consists of distilled, generalized knowledge, facts, and user preferences extracted from episodic logs and indexed in vector/relational stores.
How does memory compaction prevent context degradation without losing critical details?
Compaction uses structured hierarchical summarization rather than naive text truncation. A specialized background agent extracts key decisions, entity facts, unresolved action items, and tool execution proofs into an immutable JSON summary state, discarding conversational filler while preserving core operational semantics.
How are sensitive corporate credentials and PII prevented from persisting into long-term agent memory?
An automated privacy filter (e.g. Microsoft Presidio) intercepts conversation turns before memory extraction. Passwords, API tokens, credit card numbers, and PII are scrubbed or replaced with encrypted reference hashes, ensuring that long-term semantic stores remain completely compliant with data governance policies.
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Authoritative Sources
- MemGPT / Letta: Towards LLMs as Operating Systems (Memory Hierarchies)UC Berkeley / Letta • OFFICIAL REQUIREMENT
- Generative Agents: Interactive Simulacra of Human Behavior (Stanford)Stanford University • OFFICIAL REQUIREMENT
- Redis for AI: Working Memory and Semantic Caching SpecificationsRedis Ltd. • OFFICIAL REQUIREMENT