_estimate_batch_cost (atomic_agents/agent.py) looks up the per-model output price with PRICING.get(model, {}), returning 0.0 for any model not in the hardcoded pricing table. _check_batch_reservation then early-returns when the reservation is <= 0, skipping the batch reservation entirely. That reservation is the only defense against the documented fan-out race where every parallel helper/delegate reads the identical pre-batch on-disk cost total and each passes its individual check even though the collective spend overruns the configured cap.
Impact: an operator running any model not in the pricing table (self-hosted/Ollama/vLLM, a new provider SKU) with cost_guardrails + daily_cap_usd set believes the cap protects them, but a single parallel batch can blow past the cap. The parallel-helper model argument can also be steered to an unknown id. The sibling dream._estimate_dream_cost does this correctly (PRICING.get(model, _fallback_pricing())), which makes this a clear defect.
Affected: agent.py (_estimate_batch_cost / _check_batch_reservation), all versions through 1.0.0.
Fix: use PRICING.get(model, _costs._fallback_pricing())['output'] (mirror dream/calc_cost). Add a conformance test asserting an unknown-model batch reserves > 0 and that an over-cap unknown-model batch raises CostGuardrailBlocked.
_estimate_batch_cost(atomic_agents/agent.py) looks up the per-model output price withPRICING.get(model, {}), returning 0.0 for any model not in the hardcoded pricing table._check_batch_reservationthen early-returns when the reservation is <= 0, skipping the batch reservation entirely. That reservation is the only defense against the documented fan-out race where every parallel helper/delegate reads the identical pre-batch on-disk cost total and each passes its individual check even though the collective spend overruns the configured cap.Impact: an operator running any model not in the pricing table (self-hosted/Ollama/vLLM, a new provider SKU) with
cost_guardrails+daily_cap_usdset believes the cap protects them, but a single parallel batch can blow past the cap. The parallel-helpermodelargument can also be steered to an unknown id. The siblingdream._estimate_dream_costdoes this correctly (PRICING.get(model, _fallback_pricing())), which makes this a clear defect.Affected:
agent.py(_estimate_batch_cost/_check_batch_reservation), all versions through 1.0.0.Fix: use
PRICING.get(model, _costs._fallback_pricing())['output'](mirror dream/calc_cost). Add a conformance test asserting an unknown-model batch reserves > 0 and that an over-cap unknown-model batch raisesCostGuardrailBlocked.