RPCS-1 Agent Tuner — Configure AI agents that don't oscillate, overload, or freeze
Questions?Built on RPCS-1 receiver dynamics · Pred-09-5 validatedConfigure AI agents that don't oscillate, overload, or freeze.<br>Describe your agent's task and environment. Get exact temperature, context strategy, and model recommendation — derived from your agent's operating conditions, not guesswork.<br>Start with a filled example if you just want to see whether the framework clicks. No account, email, or payment required.<br>temperature 0.52max_tokens 4096regime stablecontext rolling_summary<br>Show me a live exampleTune my own agent<br>No sign-up required. Free forever for web tuner access.<br>Popular starts:support agentcoding agentresearch agent
before / after<br>Same agent. Different operating regime.<br>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<br>High stakes, dynamic inputs, guessed defaults
looping
Search docsretry<br>Search docs againretry<br>Retry tool callretry<br>Need more contextretry<br>Search docs againretry
Result: oscillation, tool churn, no confident final action.
RPCS-1 tuned agent<br>Receiver profile mapped to model settings
stable
temp 0.52<br>FT raised<br>context rolling<br>regime stable
Classify taskok<br>Measure entropyok<br>Set thresholdsok<br>Pick context planok<br>Commit safelyok
Result: bounded context, cleaner tool use, final answer delivered.
python<br>from rpcs1 import recommend_params
config = recommend_params(<br>task_description="Customer support agent",<br>environment_entropy="dynamic",<br>stakes="high",<br>commitment_style="cautious",<br>target_platform="anthropic",
# Grounded in Matching Principle (Pred-09-5: TI ~ 1/H)<br>print(config.platform_parameters.temperature) # 0.52<br>print(config.platform_parameters.model_recommendation) # claude-sonnet-4-6<br>print(config.predicted_regime) # stable<br>print(config.receiver_profile.TI) # 30
The problem every agent builder has<br>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.
OscillationAgent revisits the same tool calls, refuses to commit. High TI + high SG in a fast-changing environment.<br>Lower SG, shorten context window (TI ↓)
OverloadAgent acts on insufficient information, hallucinates tool calls. High SG + low FT + short integration.<br>Raise FT, lower SG, add retry strategy
FreezeAgent hedges endlessly, never takes action. Low UE + high FT — stuck in the filter.<br>Lower FT, raise UE, adjust commitment style
Five primitives. One structural framework.<br>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.
TI<br>Temporal Integration<br>How much history to integrate. Maps to context window strategy and max_tokens.
SG<br>Signal Gain<br>How strongly to amplify signals. Maps inversely to temperature.
FT<br>Filtering Threshold<br>How conservatively to gate action. Drives tool use strategy.
UE<br>Update Elasticity<br>How readily to revise the model. Sets retry and grounding strategy.
AR<br>Ambiguity Resolution<br>How aggressively to commit when uncertain.
Pred-09-5 from IMM Paper 9<br>The Matching Principle: TI ≈ 1 / H<br>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.<br>Read the full explanation →
Ready to tune your agent?<br>Free tier: unlimited web tuner. Paid SDK access starts at $40/month. Team plan at $400/month.<br>Open the tuner →View pricing
© 2026 Travis Bergen. MIT License.