[{"_1":2,"_3":-5,"_4":-5},"loaderData",{"_5":6,"_7":8},"actionData","errors","root",{"_28":-5,"_38":-5,"_39":40,"_41":42,"_43":44,"_45":46,"_47":48,"_49":50,"_51":52,"_53":54,"_55":56,"_57":58},"routes/_store+/_readers+/email_author.$slug/route",{"_9":10,"_11":-5},"book",{"_12":13,"_14":15,"_16":17,"_18":19,"_20":21,"_22":23},"currentUser","id","Z2lkOi8vbGVhbnB1Yi9TdG9yZTo6Qm9va1Byb2R1Y3QvNzkzNzQ","title","Hyperautomation with Camunda Agentic-AI and Cloud","state","published","contactAuthorMethod","email","aboutText","

Hyperautomation with Camunda, Flowable, Agentic AI, and the Cloud is a practical guide for technologists, architects, and business leaders navigating the shift from traditional workflow automation to intelligent, self-directing systems. The book frames hyperautomation not as a single tool but as a layered discipline — combining business process management (BPM) engines like Camunda and Flowable with the elasticity of cloud-native infrastructure and the judgment of AI agents. Readers are introduced to the foundational concepts of BPMN, DMN, and CMMN, then shown how those standards become the connective tissue between deterministic workflows and probabilistic AI components.

The core of the book walks through how to design, deploy, and operate orchestration ecosystems that scale. Camunda and Flowable are compared side by side — their engines, modelers, runtimes, and deployment patterns — with concrete guidance on choosing between them based on team skills, licensing, and architectural fit. From there, the focus shifts to agentic AI: how to embed LLM-powered agents into business processes so they can reason about exceptions, summarize unstructured data, call external tools, and hand control back to deterministic steps when human approval or auditability is required. Cloud-native patterns — Kubernetes, event-driven messaging, serverless tasks, observability, and zero-trust security — are treated as first-class concerns rather than afterthoughts, with architectural blueprints for AWS, Azure, and GCP.

The closing chapters tie everything together with implementation strategy, governance, and measurable outcomes. Readers will find guidance on rolling out hyperautomation incrementally, building centers of excellence, managing model and process drift, and quantifying ROI in terms of cycle time, error rates, and cost-to-serve. Case-style examples span finance, insurance, healthcare, and supply chain, illustrating how the same orchestration backbone supports very different domains. By the final pages, the reader should have both a mental model and a concrete playbook for assembling intelligent, resilient automation ecosystems that can evolve as standards, models, and cloud platforms continue to change.

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Kick off your book project, get started with GhostAI, get better at marketing, or spend the day doing all three! Free live workshops on Zoom. Saturday, June 27, 2026.

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