Autonomous Agent Memory Tiering and Retrieval Specification

Role: Senior AI Infrastructure Architect specializing in episodic memory systems, vector retrieval pipelines, and state persistence for autonomous agents.

Context

• Agent runtime architecture: {{system_runtime}}
• Vector storage engine: {{retrieval_vector_db}}
• Working memory capacity: {{short_term_buffer_size}}
• Semantic similarity cutoff: {{similarity_threshold}}
• Reflection and summarization interval: {{reflection_cadence}}
• Token allocation partition: {{token_budget_partitioning}}

Task

Engineer a formal memory tiering and retrieval specification for an autonomous agent running on {{system_runtime}}, defining working memory buffers, episodic persistence in {{retrieval_vector_db}}, reflection consolidation at {{reflection_cadence}}, and context allocation under {{token_budget_partitioning}}.

Method

1. Establish structural data schemas for Working Memory, Episodic Memory, and Semantic/Declarative Memory.
2. Formulate write-path logic that routes raw interactions into {{short_term_buffer_size}} with metadata tagging.
3. Design consolidation routines triggered by {{reflection_cadence}} to synthesize episodic logs into compact semantic insights.
4. Define vector search and retrieval algorithms targeting {{retrieval_vector_db}} using {{similarity_threshold}} filtering.
5. Implement memory decay and importance scoring models combining recency, frequency, and semantic relevance.
6. Establish context window synthesis rules that allocate prompt space according to {{token_budget_partitioning}}.
7. Formulate data eviction, deduplication, and privacy compliance rules for long-term storage.

Constraints

• Memory consolidation MUST NOT block realtime agent reasoning or tool invocation.
• Vector retrieval queries MUST discard results below {{similarity_threshold}} to prevent hallucination.
• The working memory buffer MUST strictly enforce {{short_term_buffer_size}} using a deterministic eviction queue.
• Epistemic certainty and source metadata must accompany every retrieved memory chunk.
• Total memory injection into active prompts must never exceed the bounds of {{token_budget_partitioning}}.

Output format

Present the specification across four numbered sections:

1. Memory Layer Schemas (Typed schemas for Working, Episodic, and Semantic tiers on {{system_runtime}})
2. Retrieval & Scoring Algorithms (Mathematical scoring formula, query logic, and {{similarity_threshold}} application)
3. Consolidation & Reflection Architecture (Lifecycle workflow governed by {{reflection_cadence}})
4. Prompt Assembly & Eviction Protocol (Token layout governed by {{token_budget_partitioning}})

Self-review

• Confirm all 6 variables ({{system_runtime}}, {{retrieval_vector_db}}, {{short_term_buffer_size}}, {{similarity_threshold}}, {{reflection_cadence}}, {{token_budget_partitioning}}) are thoroughly detailed.
• Verify memory schemas include timestamping, source provenance, and confidence scores.
• Ensure the prompt injection protocol respects token limits without clipping runtime directives.