Problem
Agentic invocations are fundamentally different from traditional API calls — a single request can trigger multi-step reasoning, tool chains, and sub-agent loops that compound token usage unpredictably. In a federated ecosystem, this creates a cost governance gap:
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Unbounded cost exposure for publishers. A federated partner's workload can impose uncapped inference cost on the publisher's infrastructure with no protocol-level limit.
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No feedback loop for consumers. Consumers have no way to know they're approaching a usage limit until invocations start failing.
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Hard failures break workflows. Without a degradation model, quota exhaustion rejects requests mid-execution with no recovery path for the consumer's orchestration chain.
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Rate limiting is insufficient. Requests/second doesn't capture the real cost driver: token consumption. A simple query and a complex reasoning task may both be one request but differ by 100x in cost.
These gaps are acute in B2B federation where usage is unpredictable, SLAs are contractual, and both parties need governed visibility.
Proposed Solution (summary)
An optional usagePolicy object in the execution contract that defines:
| Component |
What it does |
tokenQuota |
Per-consumer limit + reset period (e.g., 100K tokens / 24h) |
thresholds |
Ordered actions at consumption percentages — notify → degrade → block |
degradedMode |
Named fallback behaviors (e.g., informational-only: cached/static responses, no LLM reasoning) so consumers get a response instead of a hard failure |
| Response headers |
X-Quota-Remaining / X-Quota-Resets on every response for client-side awareness |
Key design principle: Quota exhaustion ≠ trust revocation. A consumer at quota is still trusted — they're simply rate-governed. Trust and usage are independent axes.
Dependencies
Open Questions
- Should the spec define a normative consumer-side pre-check endpoint, or is quota enforcement publisher-side only?
- Should quota support alternative cost units beyond tokens (requests, compute-seconds, monetary)?
- How should quota work across multi-agent chains? If Agent A → Agent B → Agent C crosses federation boundaries, whose quota is consumed?
Happy to elaborate on enforcement semantics, degradation models, or the quota/trust interaction matrix in follow-up comments as consensus forms.
Problem
Agentic invocations are fundamentally different from traditional API calls — a single request can trigger multi-step reasoning, tool chains, and sub-agent loops that compound token usage unpredictably. In a federated ecosystem, this creates a cost governance gap:
Unbounded cost exposure for publishers. A federated partner's workload can impose uncapped inference cost on the publisher's infrastructure with no protocol-level limit.
No feedback loop for consumers. Consumers have no way to know they're approaching a usage limit until invocations start failing.
Hard failures break workflows. Without a degradation model, quota exhaustion rejects requests mid-execution with no recovery path for the consumer's orchestration chain.
Rate limiting is insufficient. Requests/second doesn't capture the real cost driver: token consumption. A simple query and a complex reasoning task may both be one request but differ by 100x in cost.
These gaps are acute in B2B federation where usage is unpredictable, SLAs are contractual, and both parties need governed visibility.
Proposed Solution (summary)
An optional
usagePolicyobject in the execution contract that defines:tokenQuotathresholdsdegradedModeinformational-only: cached/static responses, no LLM reasoning) so consumers get a response instead of a hard failureX-Quota-Remaining/X-Quota-Resetson every response for client-side awarenessKey design principle: Quota exhaustion ≠ trust revocation. A consumer at quota is still trusted — they're simply rate-governed. Trust and usage are independent axes.
Dependencies
usagePolicylives inside the execution contract negotiated during the mutual trust handshakeOpen Questions
Happy to elaborate on enforcement semantics, degradation models, or the quota/trust interaction matrix in follow-up comments as consensus forms.