Items related to AI Agent Memory Systems: Short-Term Context, Long-Term...
AI Agent Memory Systems: Short-Term Context, Long-Term Memory, Retrieval, Summarization, and Persistent State - Softcover
Synopsis
AI Agent Memory Systems is a guide to designing what an agent remembers, for how long, and how it finds what it remembers when it needs it. Memory is the subsystem that lets an agent's behavior improve across interactions rather than starting fresh every time — and it is also where privacy risk, staleness, and quiet corruption most often accumulate.
Eleven chapters cover the full memory stack:
• Memory as an agent subsystem: distinct memory roles, state boundaries, lifecycle, and architectural privacy
• Short-term context management: working context, turn history, context budgets, recency weighting
• Long-term memory architecture: persistent stores, episodic memory, semantic memory, profiles
• Retrieval for agent memory: embedding search, metadata filters, ranking, freshness
• Summarization and compression: memory summaries, fact extraction, context compression, loss control
• Memory write policies: salience, deduplication, confidence, retention
• Memory consistency and updating: conflict resolution, staleness, revision, deletion
• Privacy and access control: sensitive data classification, permissions, retention limits, auditability
• Memory evaluation: retrieval quality, factual persistence, contamination testing, behavior impact
• Production memory operations: monitoring, migration, backups, continuous tuning
• Cross-session personalization: preference modeling, cold start, cross-device continuity, personalization boundaries
Every section pairs the concept with the trade-off that makes it a real engineering decision, the pitfall teams most often hit, and practical checks. Each chapter closes with a worked scenario.
Four appendices cover architectural comparisons, a memory maturity model, a failure-mode reference organized by symptom, and common questions.
Written for engineers building AI agents, copilots, and any system where persistent memory shapes user-facing behavior.
"synopsis" may belong to another edition of this title.
Search results for AI Agent Memory Systems: Short-Term Context, Long-Term...
Stock Image
AI Agent Memory Systems (Paperback)
Print on DemandSeller: Grand Eagle Retail, Bensenville, IL, U.S.A.
Seller rating 5 out of 5 stars
Paperback. Condition: new. Paperback. AI Agent Memory Systems is a guide to designing what an agent remembers, for how long, and how it finds what it remembers when it needs it. Memory is the subsystem that lets an agent's behavior improve across interactions rather than starting fresh every time - and it is also where privacy risk, staleness, and quiet corruption most often accumulate. Eleven chapters cover the full memory stack: - Memory as an agent subsystem: distinct memory roles, state boundaries, lifecycle, and architectural privacy- Short-term context management: working context, turn history, context budgets, recency weighting- Long-term memory architecture: persistent stores, episodic memory, semantic memory, profiles- Retrieval for agent memory: embedding search, metadata filters, ranking, freshness- Summarization and compression: memory summaries, fact extraction, context compression, loss control- Memory write policies: salience, deduplication, confidence, retention- Memory consistency and updating: conflict resolution, staleness, revision, deletion- Privacy and access control: sensitive data classification, permissions, retention limits, auditability- Memory evaluation: retrieval quality, factual persistence, contamination testing, behavior impact- Production memory operations: monitoring, migration, backups, continuous tuning- Cross-session personalization: preference modeling, cold start, cross-device continuity, personalization boundaries Every section pairs the concept with the trade-off that makes it a real engineering decision, the pitfall teams most often hit, and practical checks. Each chapter closes with a worked scenario. Four appendices cover architectural comparisons, a memory maturity model, a failure-mode reference organized by symptom, and common questions. Written for engineers building AI agents, copilots, and any system where persistent memory shapes user-facing behavior. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Seller Inventory # 9798175809849
Stock Image
AI Agent Memory Systems
Print on DemandSeller: PBShop.store US, Wood Dale, IL, U.S.A.
Seller rating 5 out of 5 stars
PAP. Condition: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000. Seller Inventory # L0-9798175809849
Stock Image
AI Agent Memory Systems: Short-Term Context, Long-Term Memory, Retrieval, Summarization, and Persistent State
Print on DemandSeller: California Books, Miami, FL, U.S.A.
Seller rating 5 out of 5 stars
Condition: New. Print on Demand. Seller Inventory # I-9798175809849
Stock Image
AI Agent Memory Systems
Print on DemandSeller: PBShop.store UK, Fairford, GLOS, United Kingdom
Seller rating 5 out of 5 stars
PAP. Condition: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000. Seller Inventory # L0-9798175809849
Buy New
US$ 17.87
US$ 4.41 shipping
Ships from United Kingdom to U.S.A.
Ships from United Kingdom to U.S.A.
Quantity: Over 20 available
Stock Image
AI Agent Memory Systems : Short-Term Context, Long-Term Memory, Retrieval, Summarization, and Persistent State
Seller: AHA-BUCH GmbH, Einbeck, Germany
Seller rating 5 out of 5 stars
Taschenbuch. Condition: Neu. Neuware - AI Agent Memory Systems is a guide to designing what an agent remembers, for how long, and how it finds what it remembers when it needs it. Memory is the subsystem that lets an agent's behavior improve across interactions rather than starting fresh every time - and it is also where privacy risk, staleness, and quiet corruption most often accumulate. Eleven chapters cover the full memory stack: - Memory as an agent subsystem: distinct memory roles, state boundaries, lifecycle, and architectural privacy- Short-term context management: working context, turn history, context budgets, recency weighting- Long-term memory architecture: persistent stores, episodic memory, semantic memory, profiles- Retrieval for agent memory: embedding search, metadata filters, ranking, freshness- Summarization and compression: memory summaries, fact extraction, context compression, loss control- Memory write policies: salience, deduplication, confidence, retention- Memory consistency and updating: conflict resolution, staleness, revision, deletion- Privacy and access control: sensitive data classification, permissions, retention limits, auditability- Memory evaluation: retrieval quality, factual persistence, contamination testing, behavior impact- Production memory operations: monitoring, migration, backups, continuous tuning>Every section pairs the concept with the trade-off that makes it a real engineering decision, the pitfall teams most often hit, and practical checks. Each chapter closes with a worked scenario. Four appendices cover architectural comparisons, a memory maturity model, a failure-mode reference organized by symptom, and common questions. Written for engineers building AI agents, copilots, and any system where persistent memory shapes user-facing behavior. Seller Inventory # 9798175809849
Buy New
US$ 32.36
US$ 39.17 shipping
Ships from Germany to U.S.A.
Ships from Germany to U.S.A.
Quantity: 2 available
Stock Image
AI Agent Memory Systems (Paperback)
Print on DemandSeller: CitiRetail, Stevenage, United Kingdom
Seller rating 5 out of 5 stars
Paperback. Condition: new. Paperback. AI Agent Memory Systems is a guide to designing what an agent remembers, for how long, and how it finds what it remembers when it needs it. Memory is the subsystem that lets an agent's behavior improve across interactions rather than starting fresh every time - and it is also where privacy risk, staleness, and quiet corruption most often accumulate. Eleven chapters cover the full memory stack: - Memory as an agent subsystem: distinct memory roles, state boundaries, lifecycle, and architectural privacy- Short-term context management: working context, turn history, context budgets, recency weighting- Long-term memory architecture: persistent stores, episodic memory, semantic memory, profiles- Retrieval for agent memory: embedding search, metadata filters, ranking, freshness- Summarization and compression: memory summaries, fact extraction, context compression, loss control- Memory write policies: salience, deduplication, confidence, retention- Memory consistency and updating: conflict resolution, staleness, revision, deletion- Privacy and access control: sensitive data classification, permissions, retention limits, auditability- Memory evaluation: retrieval quality, factual persistence, contamination testing, behavior impact- Production memory operations: monitoring, migration, backups, continuous tuning- Cross-session personalization: preference modeling, cold start, cross-device continuity, personalization boundaries Every section pairs the concept with the trade-off that makes it a real engineering decision, the pitfall teams most often hit, and practical checks. Each chapter closes with a worked scenario. Four appendices cover architectural comparisons, a memory maturity model, a failure-mode reference organized by symptom, and common questions. Written for engineers building AI agents, copilots, and any system where persistent memory shapes user-facing behavior. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Seller Inventory # 9798175809849
Buy New
US$ 22.08
US$ 49.61 shipping
Ships from United Kingdom to U.S.A.
Ships from United Kingdom to U.S.A.
Quantity: 1 available