AWS checklist. It reveals what capabilities organisations actually need to move from AI experimentation to productiongrade agentic systems. Consider what happens after a major AI investment: six months later, implementation has stalled. Agents aren’ t reaching production. ROI isn’ t materialising. The organisation is frustrated – not because the models failed, but because it lacked the foundational capabilities to operationalise them.
Agentic AI breaks the traditional deployment model completely: it requires simultaneous capabilities across five dimensions. Access to foundation models isn’ t enough. Organisations need governance, infrastructure, operations, value tracking and culture change.
2.4.1 Governance and accountability Without this, implementations stop. Customers worry:“ What if the AI makes a biased decision?”“ Who’ s liable for AI errors?” These governance gaps halt deployments – not technical issues. Governance development takes time but enables production deployment.
2.4.2 Technical infrastructure Beyond basic compute and storage, agentic AI demands purpose-built infrastructure: agent runtimes for orchestrating multi-step autonomous workflows; vector stores for grounding agents in enterprise knowledge;
“ A SINGLE AI INITIATIVE CAN
SIMULTANEOUSLY ENABLE NEW PRODUCTS, IMPROVE CUSTOMER EXPERIENCE, REDUCE OPERATIONAL COSTS, ENSURE
FAIRER DECISIONS AND CREATE
NEW REVENUE STREAMS”
AWS
AWS’ S STRUCTURED APPROACH FOR ARTICULATING AND MEASURING VALUE CREATION HAS
5
KEY BUSINESS PILLARS