Featured PlaybookEnterprise AI agents: scope, control and operation
A practical guide to enterprise AI agents: define the workflow, action authority, persistent state, evaluation evidence and operating responsibility.
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Engineering perspectives, decision guides and explicitly illustrative scenarios. Use these open resources to define requirements, compare approaches and plan what to validate.
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PlaybookHow to choose an industrial AI partner
Compare industrial AI partners by workflow expertise, integration design, evaluation evidence, human control, and support. Use a practical buyer checklist.
PlaybookRAG, fine-tuning, or workflow automation?
Choose RAG for source-backed knowledge, fine-tuning for evaluated behavior changes, and workflow engineering for authorized actions across existing systems.
PlaybookA readiness checklist for long-running AI agents
Assess long-running AI agents with a practical checklist for task state, permissions, integrations, recovery, evaluation evidence, monitoring, and human takeover.
PlaybookEnterprise AI agents: scope, control and operation
A practical guide to enterprise AI agents: define the workflow, action authority, persistent state, evaluation evidence and operating responsibility.
PerspectiveCloud foundations for operational AI
Assess cloud foundations for AI through identity, source access, deployment, monitoring, cost and recovery, with a scope suited to the intended workload.
- Playbook
Enterprise RAG: sources, permissions and evaluation
Design enterprise RAG around approved sources, permission-aware retrieval, supported answers and ongoing knowledge ownership, with practical evaluation criteria.
PlaybookIllustrative case: a controlled software release workflow
Explore an illustrative platform engineering design for release controls, evidence capture, deployment recovery and operational ownership in a regulated setting.
PerspectiveDesign systems: shared decisions and usable interfaces
Build a design system around shared ownership, accessibility, responsive behavior and real product tasks, with a clear contribution and release process.
- Playbook
AI governance: actions, evidence and human control
Define AI governance through action permissions, evaluation evidence, source access, stopping conditions and accountable operational review for each workflow.
PerspectiveData products: ownership, contracts and useful evidence
Define data products with clear consumers, source ownership, freshness, change handling and operational evidence for analytics and AI applications.
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Illustrative case: connected maintenance decision support
Follow an illustrative maintenance workflow from sensor event to approved evidence and human review, with integration, evaluation and recovery considerations.
PlaybookIllustrative case: phased commerce modernization
Explore an illustrative commerce migration with journey mapping, order reconciliation, checkout recovery and a measured baseline before conversion claims.
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