Overcoming Drift

To understand why we built Amigo the way we did, we must start with a fundamental challenge: how do we prevent AI systems from drifting away from optimal performance and alignment over time?

The Drift Problem

At its core, every AI system faces the relentless challenge of drift—the tendency for performance, behavior, and alignment to gradually degrade without proper architectural safeguards. This drift manifests in multiple interconnected ways:

Performance Drift occurs when systems lose the ability to maintain optimal cognitive resource allocation. Without proper entropy stratification, systems either over-apply precision to creative tasks (reducing innovation) or under-apply rigor to critical decisions (creating safety risks).

Alignment Drift happens when systems gradually deviate from organizational values and operational requirements. As real-world usage patterns evolve, the gap between verification scenarios and actual conversations can widen, potentially compromising safety and effectiveness.

Context Drift emerges from the token bottleneck, where information loss at each reasoning step causes progressive degradation of the circular dependency between entropy awareness and unified context that enables intelligent decision-making.

Behavioral Drift results when systems lose coherent identity and consistent response patterns across interactions, leading to unpredictable or inappropriate behaviors that erode user trust and organizational value.

The fundamental insight driving Amigo's architecture is that drift isn't an operational problem to be managed—it's an architectural challenge to be prevented. Every component, every design decision, and every verification mechanism exists to maintain system coherence while enabling continuous improvement.

Iterative Agent Evolution: The Drift Prevention Framework

Effective agent training rests on a three-layer framework that creates a coherent approach to preventing drift while enabling continuous improvement. Each layer serves as a safeguard against different forms of drift, yet they work together to create something greater than their individual parts—a system that maintains alignment while adapting to evolving requirements.

The Problem Model: Preventing Alignment Drift

The foundation begins with the Problem Model—a comprehensive representation of the problem space that serves as the primary safeguard against alignment drift. While foundational models already provide substantial world modeling capabilities for human-centric , defining the problem model requires more domain expertise and specialized data foundations that evolve over time.

Amigo's architecture operates on a clear division of responsibilities that leverages the strengths of both parties. Our partners are primarily responsible for defining the problem models and judges that drive evolutionary pressure and track competitive market changes. Meanwhile, Amigo focuses on building an efficient, recursively improving system that evolves under that pressure.

Organizations shape this layer by articulating not just what problem needs solving, but also by establishing the boundaries and characteristics of specific problem neighborhoods. This explicit definition prevents the system from gradually drifting away from organizational values and operational requirements by maintaining clear anchors for what constitutes appropriate behavior within each domain.

This partnership is fundamental to creating AI that works in theory and practice. As markets evolve and problem definitions shift, our partners continuously sharpen these inputs through specialized data, refined problem scopes, or updated success metrics. Domain expertise from our partners combines with technical innovation from our team to create agents that stay aligned and continuously improve over time.

The Judge: Preventing Performance Drift

Next comes the Judge, which exists to answer a deceptively simple question: "What does successfully solving the problem look like?" This component serves as the primary mechanism for preventing performance drift by continuously verifying that the system maintains optimal cognitive resource allocation across all operational contexts.

While we provide programmatic and search-based verifiers out of the , the real work lies in defining the critical evaluation framework that embodies your strategic objectives. This framework determines when a problem is solved, verifying that economic work units are delivered , and most importantly, detecting when performance begins to drift from established baselines.

Organizations focus primarily on defining subjective and domain-specific verifiers for high-entropy —those complex situations where success isn't black and white. As market conditions shift and problem definitions evolve, so must these evaluation criteria. The Judge prevents drift by ensuring that changes to the system are validated against actual performance requirements rather than theoretical improvements.

The Agent: Preventing Context and Behavioral Drift

At the center of this framework sits the Agent—the dynamic problem-solver that operates within the bounds of the problem model while optimizing toward the success measures determined by the Judge. The agent serves as the primary mechanism for preventing both context drift and behavioral drift by maintaining the circular dependency between entropy awareness and unified context that enables intelligent decision-making.

Rather than simply throwing computational resources at problems, the agent's primary responsibility involves orchestrating optimal entropy stratification across operational layers while preventing the progressive degradation that leads to drift. This means discovering the correct topology for each stratum and composition patterns that deliver maximum efficiency and performance for specific problem , while maintaining contextual coherence across all interactions.

The agent learns through evolutionary pressure in simulated worlds powered by problem , existing in productive tension between what the Problem Model requires and what the Judge expects. This controlled evolution prevents behavioral drift by ensuring that adaptations enhance rather than compromise system coherence.

The verification framework protects against drift as the system evolves, ensuring the agent remains grounded in reality while continuously adapting to meet evolving challenges. Most critically, the agent maintains the beneficial circular dependency between entropy awareness and unified context, preventing the context drift that would otherwise compound over extended interactions.