Did the Turn provide an explanation, generate a validated draft, request a registered action or complete a canonical side effect—and what exact record supports that conclusion?
Operational guide · Controlled Intelligence
Use Intelligence without confusing an answer with an action.
Use this workflow when natural-language assistance must remain bound to the current actor, tenant, Project, exact object, registered tools and canonical execution evidence.
Intelligence combines owner-private state with server-rebuilt context and a bounded model/tool/model engine.
Ask one concrete question and verify each transition before authorising the next one.
A safe failure preserves the session and goal without manufacturing a result or widening scope.
The session and Turn can retain profile, prompt/context version, usage, selected skill, provider call identity, tool and argument hash, validation result, confirmation or approval state, attempt metadata and final Action/canonical reference according to the configured contract.
Intelligence does not scrape arbitrary screens, browse the web, run a general autonomous workflow graph or inherit permissions the actor lacks. ANpy code is validated but not executed by AI; Drive/Sandbox remain response-only without a registered adapter; Pipeline execution, retry/cancel and Data Quality rule execution are not current AI actions.
Decision question
Did the Turn provide an explanation, generate a validated draft, request a registered action or complete a canonical side effect—and what exact record supports that conclusion?
Objects and controls used
Intelligence combines owner-private state with server-rebuilt context and a bounded model/tool/model engine.
Session, goal, message and Turn lifecycle in global, project or exact-object scope
Actor, tenant, active Project, permission, RLS and selected-object context rebuilt server-side
Versioned skill and server-owned Luna, Terra or Sol policy profile
Registered tool, argument hash, confirmation/approval binding, Action and canonical result reference
Controlled-use flow
Ask one concrete question and verify each transition before authorising the next one.
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State the goal in the active context
Name the selected Source, Dataset, report, run or Assurance object when relevant; do not assume the model can infer hidden browser state.
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Check the grounded object
Confirm the response uses the current authorised Source/schema or exact object and clearly separates facts, assumptions and missing context.
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Review generated SQL or code as a draft
Safe SQL or ANpy AST validation rejects unsupported structure, but validation alone does not execute SQL, save an asset or run Python.
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Inspect the proposed tool and target
Verify the registered capability, arguments, object/version identity and expected output before confirmation.
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Complete confirmation or approval
Preview and write actions follow their exact binding; configured high-risk actions require independent approval and a renewed requester confirmation.
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Follow durable progress
Use SSE replay or polling of database-authoritative Turn events; a queued message or streamed answer is not side-effect evidence.
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Verify the canonical outcome
Open the completed Action/tool result and referenced SQL, Dataset, Analytics, Assurance or Service Desk object created by the responsible module.
Failure and partial paths
A safe failure preserves the session and goal without manufacturing a result or widening scope.
Context is insufficient or mismatched
Ask for the missing authorised object or select the correct Source; do not accept SQL for an unselected or different database.
Model output fails the final contract
No action is applied. The Turn may finish partially with a safe explanation and correlation reference.
Tool dependency is unavailable
Remove only that capability from the model's allowed tools; unrelated response-only help can remain available.
Worker or stream disconnects
Resume from durable events or polling. Cancellation and late-worker handling preserve the fenced Turn state.
Evidence produced
The session and Turn can retain profile, prompt/context version, usage, selected skill, provider call identity, tool and argument hash, validation result, confirmation or approval state, attempt metadata and final Action/canonical reference according to the configured contract.
Current boundary
Intelligence does not scrape arbitrary screens, browse the web, run a general autonomous workflow graph or inherit permissions the actor lacks. ANpy code is validated but not executed by AI; Drive/Sandbox remain response-only without a registered adapter; Pipeline execution, retry/cancel and Data Quality rule execution are not current AI actions.
Next step
Review the responsible-AI control chain.
Understand how context, validation, confirmation, approval and canonical evidence constrain model behaviour.
Explore advanexus IntelligenceIn brief
How does advanexus control AI-assisted actions?
A model may explain permitted context or propose a registered action. Deterministic services validate, policy and people authorise state-changing work, and the canonical service records what actually happened. advanexus is not an unrestricted autonomous agent or a second authorisation system.
Does advanexus guarantee regulatory compliance?
No. The product can support controlled execution, human oversight, traceability, investigation and evidence obligations. Applicability, legal interpretation, retention, signing, certification and regulator acceptance remain specific to the organisation and implemented control environment.