Current autonomous systems operate in a state of resource-blindness. They treat API rate limits, token budgets, and execution costs as external "weather" that happens to them, rather than internal constraints they must navigate. This leads to brittle systems that fail catastrophically when quotas are hit, or inefficient systems that under-utilize available resources out of fear.
ratelord is the missing control plane for constrained execution. It provides agents with a "sensory organ" for resource availability and a "prefrontal cortex" for budget planning.
By modeling constraints as a hierarchical graph, ratelord allows systems to:
- Negotiate: Agents ask for permission based on intended impact.
- Forecast: The system predicts exhaustion before it happens.
- Govern: Policies enforce system-wide safety without manual intervention.
- Adapt: Load is dynamically routed across identities and protocols (REST vs. GraphQL) to maximize utility.
- Zero-Ops Resilience: A local daemon that "just works," providing immediate protection for local agent development.
- Provider Agnostic: While starting with GitHub, the core logic must be applicable to LLM tokens (OpenAI/Anthropic), cloud spend (AWS/GCP), and internal system throughput.
- Attribution as a First-Class Citizen: Every millisecond of latency or unit of quota must be traceable back to an agent, an identity, and a specific intent.
In a world of ubiquitous AI agents, the primary bottleneck is no longer compute power, but the rate-limited interfaces of the world. ratelord aims to be the standard protocol for how software systems negotiate their right to act within these limits.
"Constraints are not blockers; they are the contours of the solution space."