The Economics of Intelligence
Source-grounded pack with Audit Intelligence Harness, Engineer Intelligence Routing, and Intelligence Allocation Plan, plus 1 more.
Get this packGrounded in the complete The Economics of Intelligence series.
Open the pack, choose a workflow, and follow its setup and input instructions. You can also open each workflow below.
10 source essays · 4 usable skills · reviewed Jul 2026
Use this pack when
Choose this pack when the work touches The Economics of Intelligence and you need a bounded workflow with explicit inputs, a defined output, and human review gates.
Choose the job in front of you.
4 source-grounded skills. Load one workflow at a time.
Audit Intelligence Harness
Use this workflow when an AI workflow or agent must be replayed and audited for ownership, verified inputs, state, permissions, model routing, human review, exceptions, fallbacks, logging, learning, and cost per accepted outcome before scaling.
Output specification
Return an intelligence harness audit with replay_sample, execution_trace, ownership_and_state, permissions_and_review, rule_vs_run_findings, accepted_output_economics, learning_loop, risk_priorities, and scale_decision.
Engineer Intelligence Routing
Use this workflow when an operator must tier AI tasks by value and risk and define default models, prompts, context, tools, quality bars, latency budgets, review, escalation, fallback, logging, and learning rules.
Output specification
Return an intelligence routing specification with task_tiers, quality_and_latency_bars, default_routes, prompt_context_and_tools, permissions_and_review, fallback_and_stop_rules, logging_schema, and learning_cadence.
Intelligence Allocation Plan
Use this workflow when leaders must allocate premium models, commodity models, deterministic systems, human expertise, review capacity, and infrastructure across an AI workflow portfolio to improve economics and strategic learning.
Output specification
Return an intelligence allocation plan with portfolio_inventory, premium_and_commodity_routes, human_expertise_allocation, binding_constraints, scenarios, capacity_moves, strategic_learning, decision_rights, and review_thresholds.
Intelligence Cost Curve Map
Use this workflow when an AI workflow needs task-level economics across model quality, latency, accepted outputs, human review, retries, downstream correction, scale effects, and physical capacity constraints rather than blended token or vendor cost.
Output specification
Return an intelligence cost curve with economic_unit, task_segments, route_costs, quality_latency_tradeoffs, cost_per_accepted_outcome, volume_sensitivity, review_and_correction_cost, physical_constraints, and decision_thresholds.
Keep consequential decisions explicit.
These workflows structure analysis and artifacts; they do not authorize autonomous external, personnel, financial, legal, security, or other high-impact action.