Each theme separates the problem, architecture position, evidence or hypothesis, status and next intended output.
AI-generated explanations can appear credible without sufficient SAP evidence or control.
Users cannot trust an agent when they cannot see what it is doing, waiting for or failing on.
Prompts change over time while outputs and decisions are compared as if the instruction remained stable.
Enterprise applications often load heavy optional capabilities before they are needed.
Cloud and on-premise SAP evidence must be exposed safely to agent tools without bypassing platform controls.
Autonomous write-back can outpace evidence, policy and organisational accountability.
Enterprise AI designs can add retrieval and orchestration layers before proving that approved evidence, access boundaries and operational value justify them.
Agent observability is often reduced to token and model latency while user, UI, API, database, connector and policy failures remain invisible.
A generic approval button does not define who is accountable, what evidence is reviewed or how a decision is reversed.
Independent agents can fragment identity, policy, evidence, model configuration, release control and operations.
