Agentic AI Systems Engineering : Planning, Tool Use, Memory, Verification, and Reliable Autonomous Workflows
Language: English
Published by Independently Published Sep 2026, 2026
- Softcover
- New
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
AbeBooks seller since August 14, 2006
Condition: New
US$ 32.39
Quantity: 2 available
Add to basketItem description from seller
Neuware - Agentic AI Systems Engineering is a guide to building systems where a language model plans, calls tools, maintains state, and acts over many steps without a human approving each one. An agent is a control system with a model inside it. The model supplies judgment; the surrounding system supplies bounds, verification, permissions, and the ability to stop. Most production agent failures trace to that surrounding system rather than to the model's reasoning - which is why this book spends far more time on execution loops, tool schemas, and permission scoping than on prompting. Fourteen chapters cover the full agentic stack: - Agentic systems as software architectures: goals, state, policies, and loop design- Planning and task decomposition: explicit task graphs, verifiable subgoals, bounded replanning- Tool use and external actions: call boundaries, strict schemas, scoped permissions, untrusted results- Memory and persistent state: working memory, durable facts, measured retrieval, bounded summarization- Verification and self-checking: output validation, genuinely independent cross-checks, domain constraints- Workflow reliability: classified retries, measured timeouts, idempotent writes, designed recovery- Human oversight and escalation: meaningful approval gates, checkable exception criteria, informative handoffs- Security and permission boundaries: phase-scoped privilege, tool-layer credentials, sandboxing, audit logs- Observability and evaluation: decision-level traces, outcome-based success, actionable failure taxonomies- Cost and latency management: per-run attribution, per-step model selection, caching, latency budgets- Multi-agent coordination: justified decomposition, message contracts, partitioned state, global termination- Testing agentic systems: deterministic harnesses, adversarial inputs, failure-mode coverage- Production agentic platforms: bundled deployment, model version pinning, layered cost limits>Every section pairs the concept with the trade-off that makes it a real engineering decision, the pitfall teams most often hit, and a short set of practical checks. Each chapter closes with a worked scenario showing how these decisions interact under real pressure. Five appendices cover architectural comparisons, a four-level agent maturity model, a failure-mode reference organized by symptom, adoption sequencing, and common questions. Written for engineers building AI agents, copilots, and autonomous workflows in production.…
Seller Inventory # 9798175707923
- Title
- Agentic AI Systems Engineering : Planning, Tool Use, Memory, Verification, and Reliable Autonomous Workflows
- Author
- Yousf Alrukh
- Publisher
- Independently Published Sep 2026
- Publication year
- 2026
- Condition
- Neu
- Binding
- Taschenbuch
- Language
- English
- ISBN 13
- 9798175707923
- Item weight
- 131 grams
- Dimensions
- 229x152x5 mm
An agent is a control system with a model inside it. The model supplies judgment; the surrounding system supplies bounds, verification, permissions, and the ability to stop. Most production agent failures trace to that surrounding system rather than to the model's reasoning — which is why this book spends far more time on execution loops, tool schemas, and permission scoping than on prompting.
Fourteen chapters cover the full agentic stack:
• Agentic systems as software architectures: goals, state, policies, and loop design
• Planning and task decomposition: explicit task graphs, verifiable subgoals, bounded replanning
• Tool use and external actions: call boundaries, strict schemas, scoped permissions, untrusted results
• Memory and persistent state: working memory, durable facts, measured retrieval, bounded summarization
• Verification and self-checking: output validation, genuinely independent cross-checks, domain constraints
• Workflow reliability: classified retries, measured timeouts, idempotent writes, designed recovery
• Human oversight and escalation: meaningful approval gates, checkable exception criteria, informative handoffs
• Security and permission boundaries: phase-scoped privilege, tool-layer credentials, sandboxing, audit logs
• Observability and evaluation: decision-level traces, outcome-based success, actionable failure taxonomies
• Cost and latency management: per-run attribution, per-step model selection, caching, latency budgets
• Multi-agent coordination: justified decomposition, message contracts, partitioned state, global termination
• Testing agentic systems: deterministic harnesses, adversarial inputs, failure-mode coverage
• Production agentic platforms: bundled deployment, model version pinning, layered cost limits
• Interaction and progress design: plan-derived progress, streaming, interruption, scope communication
Every section pairs the concept with the trade-off that makes it a real engineering decision, the pitfall teams most often hit, and a short set of practical checks. Each chapter closes with a worked scenario showing how these decisions interact under real pressure.
Five appendices cover architectural comparisons, a four-level agent maturity model, a failure-mode reference organized by symptom, adoption sequencing, and common questions.
Written for engineers building AI agents, copilots, and autonomous workflows in production.
"Synopsis" may belong to another edition of this title.
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