RPCS-1 – Configure AI agents that don't oscillate, overload, or freeze

RPCS-1, a new configuration framework for AI agents, provides deterministic parameter recommendations based on an agent's specific task and environment to prevent oscillation, overloading, or freezing. The framework maps five receiver primitives to LLM settings like temperature and context window strategy, using the Matching Principle to match attention span to environmental entropy. Free web tuner access is available without sign-up, with paid SDK access starting at $40 per month.

Configure AI agents that don't oscillate, overload, or freeze. Describe your agent's task and environment. Get exact temperature, context strategy, and model recommendation — derived from your agent's operating conditions, not guesswork. Start with a filled example if you just want to see whether the framework clicks. No account, email, or payment required. No sign-up required. Free forever for web tuner access. before / after Same agent. Different operating regime. Without tuning, defaults can loop under pressure. With RPCS-1, the same workload gets bounded thresholds, cleaner tool use, and a stable path to action. Unconfigured agent High stakes, dynamic inputs, guessed defaults Result: oscillation, tool churn, no confident final action. RPCS-1 tuned agent Receiver profile mapped to model settings Result: bounded context, cleaner tool use, final answer delivered. python from rpcs1 import recommend params config = recommend params task description="Customer support agent", environment entropy="dynamic", stakes="high", commitment style="cautious", target platform="anthropic", Grounded in Matching Principle Pred-09-5: TI ~ 1/H print config.platform parameters.temperature 0.52 print config.platform parameters.model recommendation claude-sonnet-4-6 print config.predicted regime stable print config.receiver profile.TI 30 The problem every agent builder has You ship an agent. It works in testing. In production it starts failing in one of three structural ways — and you have no framework for diagnosing why. Agent revisits the same tool calls, refuses to commit. High TI + high SG in a fast-changing environment. Lower SG, shorten context window TI ↓ Agent acts on insufficient information, hallucinates tool calls. High SG + low FT + short integration. Raise FT, lower SG, add retry strategy Agent hedges endlessly, never takes action. Low UE + high FT — stuck in the filter. Lower FT, raise UE, adjust commitment style Five primitives. One structural framework. Every recommendation is driven by five receiver primitives from RPCS-1, each mapping to a specific LLM parameter. All outputs are deterministic and traceable — no black-box recommendations. Temporal Integration How much history to integrate. Maps to context window strategy and max tokens. Signal Gain How strongly to amplify signals. Maps inversely to temperature. Filtering Threshold How conservatively to gate action. Drives tool use strategy. Update Elasticity How readily to revise the model. Sets retry and grounding strategy. Ambiguity Resolution How aggressively to commit when uncertain. Pred-09-5 from IMM Paper 9 The Matching Principle: TI ≈ 1 / H Agents in high-entropy environments need short attention windows. Agents in stable environments benefit from long integration. This single principle drives the core of every parameter recommendation. Read the full explanation → /docs/matching Ready to tune your agent? Free tier: unlimited web tuner. Paid SDK access starts at $40/month. Team plan at $400/month.