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AgentGuard is PRYSM’s spend-control layer. It turns “please don’t let the agent burn $50 on a typo” into a declarative guarantee: per-request cost caps, monthly budgets, and hard blocks on models you never want an agent to reach for.
AgentGuard rules are guardrails — in routing precedence they run after model selection and always win over routing preferences. A cost cap or a compliance block can’t be overridden by a rule or even a hard model lock.

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Per-request cost caps

Set max_cost_per_request in your BRAIN.md (or pass it inline). If the model PRYSM would pick costs more than the cap, PRYSM downgrades the request to a budget model (deepseek-v4-flash) instead of failing.
BRAIN.md
How the estimate works. PRYSM compares a fast pre-flight estimate — (input_price + output_price) × 0.001, i.e. the blended per-MTok price at a ~1K-token reference — against your cap. It’s deliberately conservative and computed before the call, so you’re protected up front rather than billed and apologized to afterward.
Use /route (or client.route(...)) to preview the estimated cost of a prompt before sending it — handy for testing that your cap behaves the way you expect.

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Example

BRAIN.md
A writing prompt matches the rule, but claude-sonnet-4.5’s estimate exceeds the cap, so AgentGuard downgrades it to deepseek-v4-flash. To honor the preference, raise the cap to at least the model’s estimate or remove it.

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Monthly budgets

Set a soft monthly ceiling to get alerted as you approach it:
BRAIN.md
Track actual spend any time via /usage, which returns total_cost_usd plus breakdowns by model, provider, and mode.

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Blocking models

List models an agent must never use — for cost, latency, or compliance reasons. A blocked model is rerouted through your fallback chain:
BRAIN.md
If routing (or a rule, or even a model lock) selects a blocked model, PRYSM walks the fallback list and routes to the first allowed, available model. If you don’t define a fallback, PRYSM uses a sensible default order.

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Putting it together

A typical agent config combines all three: pin good defaults with rules, cap runaway spend per request, and block the models you never want touched.
BRAIN.md

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Why it matters for agents

Autonomous agents make many calls without a human in the loop. One mis-routed batch on a frontier model can dwarf a month of normal usage. AgentGuard makes the cost ceiling a property of your repository — reviewed in pull requests, enforced on every request, and impossible for a prompt to talk its way around.

Configure it in BRAIN.md

Caps, budgets, blocks, and fallback chains — all version-controlled.

Audit spend with /usage

Totals and per-model / per-provider / per-mode breakdowns.