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A practical guide for modeling source items, model jobs, runs, events, artifacts, approvals, handoffs, and notifications so AI can notice, prepare, draft, and recommend while humans approve directional or irreversible actions.
A workplace assistant becomes useful when it can prepare the next reviewable object: a brief, draft, approval packet, notification, handoff, or recommendation. It becomes risky when those prepared objects skip the human gate and turn into silent action.
The control plane keeps that boundary visible. It gives every source item, model job, run, event, artifact, approval, handoff, and notification a record that can be reviewed, batched, retried, and audited with public-source patterns and synthetic examples.
Jobs, runs, events, actions, artifacts, approvals, handoffs, and human gates form a safe bundle for workplace assistants that prepare work without silently crossing trust boundaries.
The useful assistant does not need broad autonomy first. It needs a control plane that lets AI notice, prepare, draft, and recommend while humans approve directional or irreversible actions.
These modes let the assistant create leverage without pretending it owns the decision.
AI can: Detect a new source item, repeated blocker, stale handoff, changed dashboard row, missing decision, or approaching review date.
Record: Event plus source item pointer, confidence, timestamp, and reason the signal matters.
Stop before: Do not infer motives, assign blame, or message stakeholders directly.
AI can: Gather safe context, summarize public-source or synthetic material, attach related artifacts, and build an approval packet.
Record: Model job, run, artifact, source ids, prompt version, cost, and evidence links.
Stop before: Do not merge private sources or create unsupported claims.
AI can: Write a first version of an update, recommendation, decision brief, meeting follow-up, checklist, or escalation option.
Record: Artifact version with source item links, assumptions, confidence, and review notes.
Stop before: Do not send the draft, publish it, or treat it as the final workplace message.
AI can: Rank options, suggest next actions, identify missing approvals, flag risks, and explain trade-offs for a human decision.
Record: Recommendation artifact with rejected options, rationale, supporting events, and approval requirement.
Stop before: Do not take directional or irreversible action without a human approval record.
A small schema is enough. The important move is separating preparation, evidence, execution, review, approval, handoff, and notification so every action has a traceable path.
Store the public-source inputs, sanitized notes, synthetic examples, files, records, messages, dashboard rows, or manual entries the assistant is allowed to inspect.
source_item_id, source_type, owner, created_at, received_at, title, safe_summary, source_url_or_pointer, sensitivity_level, retention_rule.
A human decides which sources enter the control plane and removes private material before model jobs can read it.
Describe the work the model should perform: classify, summarize, extract, compare, draft, score, route, or prepare an approval packet.
model_job_id, job_type, source_item_ids, prompt_version, model_policy, priority, budget_cents, requested_by, status, due_at.
A human approves new job types, prompt versions, and model policies before they run on recurring workplace material.
Record every execution attempt for a model job so retries, costs, outputs, and failures are inspectable instead of hidden in logs.
run_id, model_job_id, started_at, finished_at, run_status, input_hash, output_hash, token_count, cost_cents, error_code.
A run can prepare artifacts automatically, but it cannot send, publish, delete, purchase, escalate, or modify systems by itself.
Append facts about what happened: source arrived, job queued, run started, artifact created, approval requested, handoff accepted, notification sent.
event_id, entity_type, entity_id, event_type, actor_type, actor_id, occurred_at, payload_json, trace_id.
Events make the system auditable so a reviewer can reconstruct why the assistant made a recommendation.
Save model outputs as reviewable objects: briefs, summaries, drafts, extracted tables, issue lists, decision options, or stakeholder updates.
artifact_id, model_job_id, run_id, artifact_type, title, artifact_status, version, storage_pointer, review_notes.
Artifacts are draft material until a person accepts, edits, rejects, or converts them into an approved action.
Turn risky or directional actions into explicit approval packets with proposed action, evidence, options, risk, cost, and reversible state.
approval_id, artifact_id, action_type, approver, approval_status, requested_at, decided_at, decision_reason, expires_at.
Directional or irreversible actions wait here until an accountable human approves, rejects, requests changes, or defers.
Track work that moves between assistant, operator, manager, reviewer, or another system without losing context.
handoff_id, from_owner, to_owner, entity_type, entity_id, handoff_reason, required_next_action, accepted_at, due_at.
The receiving person or team accepts the handoff before the system treats ownership as transferred.
Notify the right person about queued approvals, stale handoffs, failed runs, budget stops, review-ready artifacts, and decisions due.
notification_id, recipient, channel, notification_type, entity_type, entity_id, sent_at, acknowledged_at, escalation_after.
Notifications ask for review or attention; they do not imply approval and should not perform the action they describe.
Approval packets should make the proposed action, evidence, owner, risk, cost, and default decision obvious before anything leaves the review lane.
The assistant drafts email, chat, meeting follow-up, stakeholder update, public page copy, or any message that leaves the private workspace.
Draft, intended audience, source items, facts versus asks, risk note, editable version, and approve or request-changes controls.
Hold until approved by the accountable sender.
The proposed action edits a tracker, issue, roadmap, decision log, dashboard, ticket, account setting, billing state, or access control.
Before state, proposed after state, reason, owner, rollback path, affected records, and whether the change is reversible.
Require explicit approval and log the final state.
The recommendation changes ownership, escalates risk, assigns a handoff, or asks another person to act.
Handoff reason, evidence, urgency, alternatives, proposed recipient, expected next action, and date needed.
Ask a human owner to accept the route before notification.
A run can consume paid model budget, API quota, credits, compute time, vendor calls, or operator review time beyond a threshold.
Estimated cost, remaining budget, cheaper fallback, retry count, priority, and the artifact expected from the spend.
Stop at the budget cap and request approval before continuing.
The assistant proposes deleting, overwriting, archiving, suppressing, or hiding source material, artifacts, events, or notifications.
Target item, reason, retention rule, recovery path, affected views, and an export of the prior state.
Prefer archive or mark-as-superseded unless deletion is approved.
Use events to make assistant behavior reconstructable.
A sanitized source item becomes eligible for model jobs.
Creates an audit trail for what the assistant could see.
A control-plane rule or human request schedules model work.
Makes cost, prompt version, priority, and source scope reviewable before execution.
A model job finishes and produces an output or a structured failure.
Connects result quality, cost, retries, and errors to a specific execution.
A draft, brief, table, or recommendation is ready for human review.
Separates prepared work from approved work.
A proposed action crosses a directional or irreversible gate.
Makes the trust boundary explicit before action.
A person or team accepts ownership of the next action.
Prevents silent ownership transfer and stale follow-through.
A reviewer sees or confirms a notification.
Distinguishes delivery from actual attention.
Ask for attention without creating noisy autonomy.
Start with the review lane, then add durability, batch model work, and a dashboard that prioritizes approvals over magic.
Start with a spreadsheet, SQLite table, or markdown queue for source items, artifacts, approvals, and handoffs.
Append events for source creation, model jobs, runs, artifacts, approvals, handoffs, notifications, and human decisions.
Run summarization, classification, extraction, and recommendation jobs in batches with prompt versions, cost caps, and retry budgets.
Serve pending approvals, stale handoffs, failed runs, review-ready artifacts, and notifications from small materialized tables.
It is the operating layer that records source items, model jobs, runs, events, artifacts, approvals, handoffs, and notifications so an AI assistant can prepare work while humans keep approval authority.
They can notice signals, prepare safe context, draft artifacts, and recommend options. Sending, publishing, deleting, escalating, spending, routing, or changing systems of record should require human approval.
No. Start with a spreadsheet, SQLite table, DuckDB file, markdown queue, or lightweight local dashboard that runs on a normal office laptop.
Approvals are first-class records that connect a proposed action to an artifact, source items, evidence, risk, cost, approver, decision, and audit trail.
The control plane is the difference between a helpful assistant and opaque automation. Let AI prepare the next object; let humans approve the action.
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