Hypothesis, evidence and status — kept separate from finished product claims.
Can AI reproduce how an enterprise architect actually decides?
View experiment →Why it matters: Final-answer agreement is not enough; evidence, principles, constraints, unknowns, counterfactuals and challenge response must be inspectable.
Next output: Grow the human-gold denominator without changing the public runtime contract.
AI-generated explanations can appear credible without sufficient SAP evidence or control.
Open theme →Why it matters: Enterprise decisions need traceable evidence, policy boundaries and accountable review.
Next output: Publish a reusable trust and execution reference model.
Users cannot trust an agent when they cannot see what it is doing, waiting for or failing on.
Open theme →Why it matters: Live trace improves diagnosis, governance and user confidence.
Next output: Define a vendor-neutral trace schema.
Prompts change over time while outputs and decisions are compared as if the instruction remained stable.
Open theme →Why it matters: Uncontrolled drift weakens reproducibility and auditability.
Next output: Create a prompt-governance control model.
Enterprise applications often load heavy optional capabilities before they are needed.
Open theme →Why it matters: Slow startup and repeated calls reduce trust and operational usability.
Next output: Publish a thin-loading and telemetry pattern catalogue.
Cloud and on-premise SAP evidence must be exposed safely to agent tools without bypassing platform controls.
Open theme →Why it matters: Stable tools and wrappers reduce coupling between AI systems and SAP implementations.
Next output: Document a secure wrapper and tool-registration blueprint.
Autonomous write-back can outpace evidence, policy and organisational accountability.
Open theme →Why it matters: Enterprise AI must preserve segregation of duties and human ownership.
Next output: Define autonomy levels and promotion gates.
Enterprise AI designs can add retrieval and orchestration layers before proving that approved evidence, access boundaries and operational value justify them.
Open theme →Why it matters: Architecture must separate useful grounding from unnecessary complexity, cost and data exposure.
Next output: Publish a decision framework for when deterministic search, RAG or vector retrieval is appropriate.
Agent observability is often reduced to token and model latency while user, UI, API, database, connector and policy failures remain invisible.
Open theme →Why it matters: Operational reliability requires a complete transaction trace from login to logout and from user intent to evidence and outcome.
Next output: Define a lightweight cross-layer telemetry schema and release comparison model.
A generic approval button does not define who is accountable, what evidence is reviewed or how a decision is reversed.
Open theme →Why it matters: Enterprise control requires clear decision rights, segregation of duties, evidence and rollback.
Next output: Publish reusable control patterns for advisory, approval and controlled-action agents.
Independent agents can fragment identity, policy, evidence, model configuration, release control and operations.
Open theme →Why it matters: Enterprise adoption needs consistent governance without forcing every capability into one monolith.
Next output: Publish the boundary between agent workplace, execution plane and engineering control plane.
