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The Agentic AI Engineer's Handbook: Workflow Architectures and Patterns for Next-Generation AI Systems
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ISBN13: 9798274707725
Publisher: Independently Published
Published: Nov 16 2025
Pages: 532
Weight: 1.85
Height: 1.07 Width: 6.69 Depth: 9.61
Language: English
Publisher: Independently Published
Published: Nov 16 2025
Pages: 532
Weight: 1.85
Height: 1.07 Width: 6.69 Depth: 9.61
Language: English
The Agentic AI Engineer's Handbook: Workflow Architectures and Patterns for Next-Generation AI Systems is a practical, code-first guide to designing, building, and running agentic AI systems-single- and multi-agent-at production scale. You'll learn how to structure cognitive loops, coordinate agents, manage memory, enforce safety and governance, and instrument everything for reliability and cost control. Written with a builder's mindset, this book favors working patterns over theory: minimal, complete Python examples; reproducible templates; and measurable outcomes (latency, quality, cost, safety). Each chapter distills field-tested practices-risk gates, capability tokens, staged rollouts, and debugging playbooks-so you can adopt them without guesswork. About the Technology:
Modern AI is shifting from single prompts to agentic workflows: systems that perceive, plan, act, and reflect across tools, APIs, and teams. Success depends on architecture (graphs, orchestrators), memory (vector/graph hybrids), safety (policies-as-code), and operations (observability, autoscaling, budgets). This book shows how these pieces fit and how to evolve them safely. What's Inside:
- Foundations of agentic systems: perception, reasoning, memory, action, and autonomy.
- Workflow patterns: cognitive loops, task decomposition, coordinator-worker, consensus, and blackboard architectures.
- Reasoning & planning: chain-of-thought vs graph-of-thought, prioritization, replanning, reflective self-evaluation.
- Memory & context: short/long/episodic memory; hybrid vector-graph storage; retrieval & fusion.
- Orchestration & integration: event-driven designs, API/database/tool adapters, error handling, recovery.
- Multi-agent engineering: roles, specialization, synchronization, conflict resolution, and case studies.
- Scaling & reliability: metrics, dashboards, failure detection, optimization levers, cost/latency trade-offs.
- Ethics & governance: alignment gates, guardrails, privacy/compliance, meta-reasoning, self-evolution pipelines.
- Appendices: framework comparison matrix, deployment templates, debugging checklists, glossary, and curated resources.
- AI Engineers & MLEs turning prototypes into dependable systems.
- Platform & Infra teams building orchestration, policy, and observability layers.
- Tech leads & founders who need pragmatic patterns for quality, safety, and ROI.
- Researchers & advanced learners seeking applied, testable implementations of agentic ideas.
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