Amigo Overview

Amigo is a comprehensive platform that provides the cognitive architecture and orchestration framework enterprises need to build, deploy, and manage safe and reliable AI agents. We've designed it specifically for demanding industries like healthcare, legal, and finance, where performance, safety, reliability, and observability aren't just features—they're requirements.

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.

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.

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.

The Partnership Model

Amigo's architecture operates on a clear division of responsibilities that leverages the strengths of both parties. You 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.

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 continuously improve over time.

Unified Cognitive Architecture

Our system's uniqueness lies in a fundamental understanding: intelligence emerges from optimal entropy management. This isn't just a technical insight—it's the foundation of effective problem-solving.

Our architecture is built on the principle that models are entropy-. They understand not just how to solve problems but also the entropy properties of different solution approaches. This enables them to select the right cognitive method based on complexity assessment. However, entropy awareness alone isn't enough. It only works with unified —perfect point-in-time context that's essential for accurately assessing problem complexity and determining the optimal solution path for each quantum of action.

This creates a powerful optimization mechanism that operates through a beneficial circular dependency. When entropy-aware models operate with a unified context, they can discover optimal entropy stratification. They identify the correct topology for each complexity stratum and composition patterns that maximize system efficiency and performance for specific problem neighborhoods.

The verification evolutionary serves as the discovery engine where different system configurations compete under verification pressure, enabling the emergence of optimal architectures. Meanwhile, continuous verification of economic work units prevents entropic , ensuring the system maintains alignment with reality as it evolves.

Memory, Knowledge, and Reasoning as One System

This entropy-aware foundation manifests operationally through memory, knowledge, and reasoning (M-K-R), which function as interconnected facets of a single cognitive rather than separate components. The unified entropic enables high-bandwidth integration between these elements, where optimization in any area cascades through the entire system because they share the same contextual foundation.

The power emerges from recognizing that these components form a unified system with complex interdependencies. Memory influences how knowledge is applied and reasoning is framed, such as when memory of a user's previous interactions changes how domain knowledge is applied and which reasoning paths are prioritized. Knowledge and new reasoning, in turn, impact how memory is recontextualized, as when a critical piece of information causes all previous context stored in memory to be reevaluated in a new light. Reasoning, while dependent on knowledge and memory as direct inputs, also affects how they're utilized—different reasoning frameworks lead to different interpretations even with identical knowledge and memory bases.

This unified entropic approach creates a virtuous optimization cycle where the system continuously discovers better entropy stratification patterns. As the verification evolutionary chamber tests different configurations, successful patterns propagate throughout the M-K-R system. Improved memory organization enhances knowledge utilization and reasoning capabilities. Refined knowledge structures improve memory contextualization and reasoning paths. Strengthened reasoning processes lead to better memory utilization and knowledge application.

The intelligence-managed context ensures that perfect contextual information flows between all components. It prevents the missing context that would cause suboptimal problem evolution while protecting against entropic drift through continuous verification.

Agent Forge: Enabling Recursive System Evolution

Agent Forge provides the foundational infrastructure that enables this entire unified cognitive architecture to evolve and optimize itself. Through automated configuration management, coding agents can recursively optimize all components of the system—from context graphs and dynamic behaviors to evaluation frameworks and memory systems—transforming what was previously manual system evolution into a data-driven optimization process that scales with deployment complexity. This represents the next evolution in AI systems: not just agents that solve problems, but systems that can improve themselves while maintaining human oversight and safety boundaries.