AI Agents at Work - Burk, Scott; Dutta, Kinshuk; Kaur, Harman
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Supercharge your organization with a practical, battle-tested playbook for deploying agentic AI, which are multi-agent systems that boost ROI, harden compliance, and turn everyday workflows into resilient, data-driven automation. From strategy to sprint, AI Agents at Work shows you how enterprises move beyond demos to durable outcomes: an end-to-end blueprint starting from planning and readiness, progressing to agentic architecture patterns, and finally to the orchestration of multi-agent systems (MAS). Learn from actual case studies spanning finance, healthcare, retail, supply chain, and…mehr

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Supercharge your organization with a practical, battle-tested playbook for deploying agentic AI, which are multi-agent systems that boost ROI, harden compliance, and turn everyday workflows into resilient, data-driven automation. From strategy to sprint, AI Agents at Work shows you how enterprises move beyond demos to durable outcomes: an end-to-end blueprint starting from planning and readiness, progressing to agentic architecture patterns, and finally to the orchestration of multi-agent systems (MAS). Learn from actual case studies spanning finance, healthcare, retail, supply chain, and cybersecurity, along with executive decision trees and implementation checklists from actual production deployments. The authors map the full agentic stack: agent taxonomy (reactive, deliberative, utility-based, learning, and generative), reasoning frameworks (BDI, chain-of-thought, and optimization), and integration patterns for agent-to-system and agent-to-agent coordination. Learn how to operationalize graph-based workflows with DAGs, stand up resilient orchestration, and build an integrated memory fabric across vector databases and knowledge graphs. The result is not another LLM app, but adaptive agents that plan, negotiate, and execute across ERP, CRM, EMR, and IoT systems. Since enterprise trust is non-negotiable, you'll get deep guidance on Agent Governance and Performance Management: Master Agent Management (MAM), AgentOps quality gates, SLOs, immutable audit trails, XAI/Explainability, privacy and data sovereignty in federated architectures, and cost visibility with FinOps dashboards. Clear deployment playbooks compare cloud, on-prem, and hybrid models; security patterns (RBAC, API gateways, and ledgered logging) meet regulatory needs while preserving velocity. Practical matrices and maturity checklists help teams pick the right framework (LangGraph, CrewAI, and AutoGen), keep costs in check, and avoid the failure modes that sink pilots. If your charter includes cutting process backlogs, accelerating time-to-decision, and shipping production-grade multi-agent systems with measurable ROI, AI Agents at Work is your launchpad.