AI coding agents exhibit significant variance in token consumption and goal convergence, with some runs requiring 486 million tokens while others succeed in just 9.7 million tokens; this variance stems from runtime dynamics rather than model limitations. Agent Amplifier addresses this by applying six deterministic primitives at the hook layer—effort routing, goal anchoring, convergence detection, phase prompts, persona escalation, and token budgeting—to provide deterministic control over agent behavior without requiring additional LLM calls.
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Agent Amplifier v1.0 — The Hook Layer Your AI Coding Agent Was Missing #shortsAdded:
One Claude code turn spent 486 million tokens and still drifted off the goal.
Another converged in 9.7 million. The variance lives in the runtime, not the model. Agent amplifier applies six deterministic primitives at the hook layer. Effort routing, goal anchoring, convergence detection, phase prompts, persona escalation, token budgeting. Dog food it across 22 sessions, 1.7 billion tokens, 1741 tests, 100% coverage, seven host adapters. Pip install agent amplifier, AGPL 3.0.
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