Generative and Agentic AI
Generative AI strategy
Generative AI strategy: a practical guide to focus generative capability on work where language, knowledge, and judgment create value.
Expertise
Practical design of generative and agentic systems that people can trust, operate, and improve.
Library
20 results
Generative and Agentic AI
Generative AI strategy: a practical guide to focus generative capability on work where language, knowledge, and judgment create value.
Generative and Agentic AI
Agentic AI: a practical guide to decide when systems should plan and act, and where autonomy should stop.
Generative and Agentic AI
AI agents for business workflows: a practical guide to redesign end-to-end work around goals, tools, exceptions, and accountable owners.
Generative and Agentic AI
Multi-agent systems from a business perspective: a practical guide to judge whether role specialization adds value beyond complexity and coordination cost.
Generative and Agentic AI
Human-in-the-loop systems: a practical guide to place human judgment at the points where it changes quality, safety, or accountability.
Generative and Agentic AI
AI copilots: a practical guide to embed assistance into the moment of work without creating a parallel destination.
Generative and Agentic AI
Enterprise knowledge assistants: a practical guide to make governed organizational knowledge easier to find, interpret, and apply.
Generative and Agentic AI
Retrieval-augmented generation: a practical guide to connect model responses to relevant sources and observable retrieval behavior.
Generative and Agentic AI
Model customization: a practical guide to choose the lightest customization method that solves the actual performance gap.
Generative and Agentic AI
Fine-tuning decisions: a practical guide to reserve training investment for stable patterns that prompts, tools, or retrieval cannot address.
Generative and Agentic AI
AI workflow redesign: a practical guide to change the workflow rather than placing a chat box on top of existing friction.
Generative and Agentic AI
AI orchestration: a practical guide to coordinate models, tools, data, state, approvals, and recovery as one operating system.
Generative and Agentic AI
AI evaluation: a practical guide to measure task performance, risk, user value, and change over time before trust is assumed.
Generative and Agentic AI
AI reliability: a practical guide to design for variable outputs, external dependencies, degraded modes, and recovery.
Generative and Agentic AI
AI observability: a practical guide to give operators evidence about inputs, decisions, costs, failures, and user outcomes.
Generative and Agentic AI
AI security: a practical guide to protect data, identities, tools, prompts, models, and downstream actions as one attack surface.
Generative and Agentic AI
Guardrails: a practical guide to combine technical constraints with policy, workflow, monitoring, and escalation.
Generative and Agentic AI
Responsible AI: a practical guide to translate principles into product decisions, ownership, evidence, and redress.
Generative and Agentic AI
AI governance: a practical guide to make decision rights and assurance proportionate to material risk.
Generative and Agentic AI
AI risk management: a practical guide to identify, assess, treat, monitor, and communicate risk across the system lifecycle.
A useful first step
Share the opportunity, the current constraint, and what needs to become clearer.