system-prompts-and-models-of-ai-tools/docs/ai-operating-model.md

AI operating model — decision, execution, control, data, trust

This repository follows the Master Operating Prompt (MASTER_OPERATING_PROMPT.md): a governed hybrid stack, not “agents only.”

Planes (summary)

Plane	Owns	Must not
Decision	Analysis, memos, structured recommendations, scenarios	Durable external commitments
Execution	Workflows, retries, idempotency, compensation, side effects	Unstructured “trust me” narration
Control	Policy, approvals, RBAC, secrets, promotion, audit	Ad-hoc rules in prompts
Data	Operational truth, contracts, metrics definitions, lineage	Duplicate conflicting metric meanings
Trust	Evidence packs, tool verification, security gate, evals	Claims without proof

Dealix implementation pointers

• Agents / routing / pipeline: salesflow-saas/backend/app/services/agents/ — router.py, executor.py, autonomous_pipeline.py.
• Prompts (runtime path): salesflow-saas/ai-agents/prompts/ — loaded by AgentExecutor (PROMPTS_DIR); policy stays in code/services, not inside markdown prompts.
• Core OS (memos / governance direction): salesflow-saas/backend/app/services/core_os/ — e.g. decision memo and related structures where present.
• Launch & evidence discipline: salesflow-saas/docs/LAUNCH_CHECKLIST.md, salesflow-saas/verify-launch.ps1.

Operating sequence for any major change

1. Repository discovery (architecture + capability + gap + risk + trust).
2. Smallest phase that proves value with tests and rollback.
3. Evidence: tests, logs, or contract checks — as defined in the phase.
4. Only then expand scope.

See also: approval-policy.md.