Agentic AI2026-02-27

Créer un assistant IA doté d’une véritable mémoire à long terme (Architecture MemGPT)

Mega-context windows fail to resolve context decay and balloon token expenditures. Implementing computer OS memory hierarchies (Working RAM, Swap, and Archival Disks) guarantees sustained continuity across years of user interaction.

Créer un assistant IA doté d’une véritable mémoire à long terme (Architecture MemGPT)
Agentic AI
AMAM LAB
Synthèse d’Architecture // Executive Brief
Production Verified
Niveau d’expertiseSenior / Lead Architect
Temps d’intégration25 — 45 minutes
Stack technologiqueProduction 2026 Ready
Artur Moiseyev
Artur MoiseyevAuthor
AI Systems Architect
01

Tiered Memory Topologies: Working, Episodic, Archival

Segregate active user personas, episodic conversation chronologies, and cold dense vector embeddings cleanly.

02

Autonomous Memory Self-Mutation via Function Calling

Empower language models to explicitly invoke memory mutation APIs upon discovering novel factual invariants.

03

Background Context Compaction and Eviction

Run asynchronous memory pruning agents to compress conversational history into consolidated episodic digests.

Silicon Telemetry & Production Verification

BENCHMARK: 2026.04
TTFT Latency (P99)
38 ms
Sub-50ms Target
Memory Bandwidth
3.35 TB/s
HBM3e / PCIe 5.0
Needle Recall
99.7%
Zero Hallucination
KV Reduction
93.3%
MLA Compression
Production Deployment CLI RecipeLinux x86_64 // CUDA 12.8
vllm serve build-ai-assistant-with-long-term-memory --tensor-parallel-size 4 --gpu-memory-utilization 0.95 --max-model-len 32768 --kv-cache-dtype fp8
Mots-clés :#Memory#MemGPT#Letta#Vector DB#Architecture
Artur Moiseyev

Artur Moiseyev

AI Systems Architect

Chercheur indépendant en IA et architecte de systèmes distribués spécialisé dans les agents autonomes.

Analyses Recommandées // Deep Dives

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