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.
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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.
production proof
Benchmarked against unstable agent traces.
A 1,000-trace IMM eigenvalue/spectral-gap simulation compares baseline agent behavior against RPCS-1 calibrated settings across enterprise RAG, multi-tool, coding, research, support, medical, financial, legal, and safety agents.
Completion rate
100%
vs 79.6% baseline
Token savings
71.5%
3,861 fewer tokens per trace
Oscillation reduction
81.8%
2,623 to 478 oscillations
Synthetic traces
1,000
500 baseline, 500 RPCS-1
Largest token savings
competitive context
RPCS-1 does not replace your stack. It tells the stack how to behave.
Most AI engineering tools help you build, trace, evaluate, or iterate. RPCS-1 sits one layer lower: it converts operating conditions into a receiver profile and runtime posture.
Benchmark data is synthetic simulation output. Use it as directional evidence, then validate against your own production traces.
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) # 30The 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 →Ready to tune your agent?
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