AI agents should be aware of the resources constraining their current work.
The useful abstraction is broader than subscription quota.
A future agent may be constrained by:
- account quota,
- API rate limit,
- token budget,
- context remaining,
- prepaid credit,
- dollar budget,
- time budget,
- tool-call budget,
- provider concurrency,
- client-specific limits.
FluxGuard converts these heterogeneous constraints into an execution signal.
Without resource awareness:
Agent
-> broad exploration
-> spawn several subagents
-> large refactor
-> full tests
-> hard quota limit
-> incomplete result
With resource awareness:
Agent
-> check resource pressure
-> detect weekly quota at 9%
-> avoid optional exploration
-> keep one execution path
-> use targeted tests
-> checkpoint
-> complete user objective
FluxGuard should become a small local control plane.
It should work with different agent clients through MCP and optional native lifecycle hooks.
It should not become an LLM proxy unless a separate future design explicitly chooses that direction.
A successful implementation:
- works without changing model providers,
- returns useful signals quickly,
- fails safely when source information is unavailable,
- does not require private API reverse engineering,
- supports partial information,
- keeps agent-facing responses concise,
- can add a provider without changing core policy types.