Agentic AI Platform Engineering: Building Reliable Infrastructure for Autonomous AI Workflows, Tool Orchestration, and Multi-Agent Systems in Production (Production AI Engineering Series) - Softcover
Buch 8 von 11: Production AI Engineering Series
Inhaltsangabe
Build the Production Platform for Reliable Autonomous AI Agents
Agentic AI is transitioning from experimental prototypes to mission-critical enterprise systems. But while building a single chatbot is easy, engineering the reliable infrastructure required to run autonomous multi-agent workflows at scale is incredibly difficult. Agentic AI Platform Engineering is the definitive, infrastructure-first guide to building the platform layer beneath the agents.
Written specifically for platform engineers, ML developers, and cloud architects, this book bypasses high-level hype to deliver concrete, production-ready architectural patterns. Learn how to design robust, deterministic foundations that handle the unpredictable nature of Large Language Models (LLMs).
What you will master inside:
- Durable Workflows: Implement long-running agent loops using Temporal, Inngest, and Dapr.
- Agent State Stores: Design schema patterns using Redis and PostgreSQL for low latency and transactional integrity.
- Tool Registries: Build dynamic capability discovery, semantic versioning, and secure execution sandboxes.
- Guardrails & Security: Deploy multi-layered defenses against prompt injections and establish Human-in-the-Loop workflows.
- Observability & Tracing: Instrument complex agentic systems with distributed tracing and evaluations.
- Cost Management: Implement granular cost attribution per agent run, team, and customer.
Stop treating agent infrastructure as an afterthought. Equip your organization with the scalable tool orchestration, robust coordination patterns, and safety frameworks needed to support the next generation of autonomous AI. Scroll up and secure your copy today!
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Agentic AI Platform Engineering (Paperback)
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Paperback. Zustand: new. Paperback. Build the Production Platform for Reliable Autonomous AI AgentsAgentic AI is transitioning from experimental prototypes to mission-critical enterprise systems. But while building a single chatbot is easy, engineering the reliable infrastructure required to run autonomous multi-agent workflows at scale is incredibly difficult. Agentic AI Platform Engineering is the definitive, infrastructure-first guide to building the platform layer beneath the agents.Written specifically for platform engineers, ML developers, and cloud architects, this book bypasses high-level hype to deliver concrete, production-ready architectural patterns. Learn how to design robust, deterministic foundations that handle the unpredictable nature of Large Language Models (LLMs).What you will master inside: Durable Workflows: Implement long-running agent loops using Temporal, Inngest, and Dapr.Agent State Stores: Design schema patterns using Redis and PostgreSQL for low latency and transactional integrity.Tool Registries: Build dynamic capability discovery, semantic versioning, and secure execution sandboxes.Guardrails & Security: Deploy multi-layered defenses against prompt injections and establish Human-in-the-Loop workflows.Observability & Tracing: Instrument complex agentic systems with distributed tracing and evaluations.Cost Management: Implement granular cost attribution per agent run, team, and customer.Stop treating agent infrastructure as an afterthought. Equip your organization with the scalable tool orchestration, robust coordination patterns, and safety frameworks needed to support the next generation of autonomous AI. Scroll up and secure your copy today! This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Bestandsnummer des Verkäufers 9798180241269
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Agentic AI Platform Engineering: Building Reliable Infrastructure for Autonomous AI Workflows, Tool Orchestration, and Multi-Agent Systems in Production (Production AI Engineering Series)
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Agentic AI Platform Engineering (Paperback)
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Paperback. Zustand: new. Paperback. Build the Production Platform for Reliable Autonomous AI AgentsAgentic AI is transitioning from experimental prototypes to mission-critical enterprise systems. But while building a single chatbot is easy, engineering the reliable infrastructure required to run autonomous multi-agent workflows at scale is incredibly difficult. Agentic AI Platform Engineering is the definitive, infrastructure-first guide to building the platform layer beneath the agents.Written specifically for platform engineers, ML developers, and cloud architects, this book bypasses high-level hype to deliver concrete, production-ready architectural patterns. Learn how to design robust, deterministic foundations that handle the unpredictable nature of Large Language Models (LLMs).What you will master inside: Durable Workflows: Implement long-running agent loops using Temporal, Inngest, and Dapr.Agent State Stores: Design schema patterns using Redis and PostgreSQL for low latency and transactional integrity.Tool Registries: Build dynamic capability discovery, semantic versioning, and secure execution sandboxes.Guardrails & Security: Deploy multi-layered defenses against prompt injections and establish Human-in-the-Loop workflows.Observability & Tracing: Instrument complex agentic systems with distributed tracing and evaluations.Cost Management: Implement granular cost attribution per agent run, team, and customer.Stop treating agent infrastructure as an afterthought. Equip your organization with the scalable tool orchestration, robust coordination patterns, and safety frameworks needed to support the next generation of autonomous AI. Scroll up and secure your copy today! This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability. Bestandsnummer des Verkäufers 9798180241269
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Agentic AI Platform Engineering : Building Reliable Infrastructure for Autonomous AI Workflows, Tool Orchestration, and Multi-Agent Systems in Production
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