| name | agent-economics |
| description | Plan, track, and reconcile AI agent token budgets across a project lifecycle. Covers per-phase token budget allocation, model tier selection trade-offs, cost-per-task estimation, retry loop economics, context compaction strategy, and phase-end reconciliation reporting. |
| compatibility | Any Markdown editor; Python 3.10+ for budget tracking scripts |
| last_verified | 2026-05-30 |
Agent Economics Skill
Anti-Hallucination Rule (MANDATORY)
- NEVER report token counts or costs without reading actual usage data. Estimates must be labelled as estimates; actuals must come from telemetry or provider billing.
- NEVER recommend a model tier without stating the trade-off. Every tier recommendation must include a cost-vs-capability rationale.
- NEVER carry over unspent budget silently. Document carry-forward explicitly in the phase report.
What This Skill Covers
Agent economics is the cost governance layer for AI agents operating within a project. It is distinct from Azure infrastructure cost (see finops and cost-optimization) - it covers the cost of the agents themselves: the tokens they consume, the models they call, and the loops they run.
Budget Baseline Format
Initialized at Phase 0, maintained at .sisyphus/knowledge/agent-economics/budget-baseline.md:
# Agent Economics Budget Baseline
## Phase Budgets
| Phase | Token Budget | Model Tier Ceiling | Max Retry Loops | Max Human Interventions | Est. Cost |
|-------|-------------|-------------------|-----------------|------------------------|-----------|
| Phase 0 | 20k | Sonnet | 3 | 2 | ~$0.10 |
| Phase 1 | 80k | Opus (lead), Sonnet (members) | 5 | 3 | ~$4.00 |
| Phase 2 | 200k | Opus (lead), mixed members | 5 | 3 | ~$8.00 |
| Phase 3 | 100k | Sonnet (most), Opus (security) | 5 | 3 | ~$3.50 |
| Phase 4 | 110k | Sonnet (most), Haiku (writing) | 5 | 3 | ~$2.50 |
| **Total** | **510k** | | | | **~$18.10** |
## Model Tier Reference (2026 pricing guide — verify against current rates)
| Tier | Model | Input $/1M | Output $/1M | Use when |
|------|-------|-----------|------------|---------|
| Haiku | claude-haiku-4-5 | $0.80 | $4.00 | Writing, formatting, quick tasks |
| Sonnet | claude-sonnet-4-6 | $3.00 | $15.00 | Most technical members |
| Opus | claude-opus-4-6 | $15.00 | $75.00 | Lead, ultrabrain, critical reasoning |
## Retry Loop Economics
Each retry loop iteration costs ~(average_task_tokens × model_rate).
Default: 5 loops max per phase. At loop 5 without resolution: escalate to human.
Human intervention cost: ~30 min engineer time (~$25 equivalent). Budget 3 per phase.
Per-Phase Report Format
At the end of each phase, write to .sisyphus/knowledge/agent-economics/phase{N}-report.md:
# Agent Economics: Phase N Report
## Budget vs Actual
| Metric | Budget | Actual | Variance |
|--------|--------|--------|---------|
| Total tokens | 80k | 73k | -9% (under) |
| Estimated cost | $4.00 | $3.65 | -$0.35 |
| Retry loops used | ≤5 | 3 | OK |
| Human interventions | ≤3 | 1 | OK |
## Model Breakdown
| Member | Model | Tokens | Cost |
|--------|-------|--------|------|
| backend-arch | claude-sonnet-4-6 | 22k | $0.88 |
| infra-arch | claude-opus-4-6 | 18k | $2.07 |
| ... | | | |
## Carry-Forward
Remaining budget from this phase: 7k tokens ($0.35).
Carry-forward to Phase N+1: [yes/no — state policy].
## Observations
- [Any model tier that was over/under selected for the task]
- [Any retry storm patterns]
- [Recommendations for next phase]
Model Tier Selection Guide
| Task type | Recommended tier | Rationale |
|---|
| Governance, reasoning, cross-cutting decisions | Opus | Needs deepest reasoning |
| Technical implementation (architecture, code) | Sonnet | Strong capability, 5× cheaper than Opus |
| Writing, formatting, documentation | Haiku or Sonnet | Writing doesn't need Opus-level reasoning |
| Quick lookups, search, summarization | Haiku | Cheapest for low-complexity tasks |
| Security review, threat model | Opus or Sonnet | High stakes; don't cut corners |
| Agent evaluation, test running | Haiku | Mechanical execution, not reasoning |
Context Compaction Economics
Long conversations accumulate tokens. Compaction (summarizing earlier turns) reduces cost:
- Use
durable-work skill to offload tasks that survive session boundaries rather than holding all context in one session.
- Target: compaction when conversation exceeds 50k tokens in a single session.
- Compaction saves ~40–60% on re-read costs when context is dense.
Budget Overrun Protocol
If a phase is tracking to exceed budget by >20%:
- Stop non-critical work immediately.
- Report to lead with current spend and projection.
- Lead approves budget extension or descopes work.
- Document in phase report with rationale.
Never silently exceed budget. Token overruns compound: Phase 2 overruns leave less headroom for Phase 3 debugging loops.
Gate Integration
At each ARB gate, cloud-economics voter checks:
- Phase token spend vs budget-baseline allocation.
- Phase report exists and is reconciled.
- Total cumulative spend vs total project budget.
A phase where the report is missing or shows >20% overrun without documented approval is a gate condition.
