System Components
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Our architecture is built on a clear division of responsibilities: your domain experts are primarily responsible for building the world/problem models and judges that drive evolutionary pressure, while Amigo focuses on building an efficient, recursively improving system that evolves under that pressure. Following the Waymo approach to reliability, we prioritize being reliable in well-known domains first before expanding, rather than pursuing a high-risk "just throw it out there and see if it works" approach.
Fundamentally, we view memory, knowledge, and reasoning not as separate silos, but as deeply intertwined facets of a single cognitive challenge. Their effective integration, characterized by high-bandwidth pathways and cyclical optimization, is paramount to building truly intelligent and reliable agents. To build the optimal Agent, we tackle these interconnected areas through the following vehicles:
To build the Judge, we use a comprehensive Evaluations system and strengthen with .
Please find an overview of each of these concepts below. They are also covered in detail in their respective sections in our Documentation.
Amigo's agent architecture enables the creation of specialized AI experts with distinct personas and global directives that adapt to real-time interactions. Each agent is defined through a carefully crafted set of background, role, expertise, tone, and communication style—creating a consistent identity that delivers specialized expertise.
Powered by a customized memory system for personalized interactions and dynamic knowledge retrieval that surfaces the right information at the right time, these agents balance structured guidance with adaptive flexibility. This approach addresses fundamental AI limitations through domain specialization, allowing agents to develop more efficient reasoning patterns within specific fields.
Like human experts who intuitively adjust their approach based on each situation, Amigo agents deliver consistent quality that scales across specialized domains while maintaining regulatory compliance and preserving the personalized feel of human expertise.
Amigo's context graph system provides the essential blueprint that guides agent behavior through complex problem spaces. These 'topological fields' create structure and consistency while preserving the flexibility needed for natural interactions. By simulating high-dimensional thought spaces, context graphs compensate for current LLM limitations by establishing synthetic 'footholds' that help agents navigate complex scenarios.
When delivering specialized services—such as an AI doctor conducting an initial patient consultation—the agent traverses its custom-built context graph to maintain a rich environmental awareness, access memories appropriately, and retrieve knowledge at precisely the right moments, all while operating within carefully defined safety boundaries. This approach ensures interactions remain consistent, reliable, and perfectly aligned with organizational objectives across every conversation.
Guarantees perfect recall and contextualization for critical information, overcoming limitations of traditional systems. Unlike traditional approaches that treat all information equally, our architecture uses multiple layers to ensure perfect preservation of vital information while maintaining its complete context.
Built around a user model that serves as a blueprint for memory operations, the system organizes information in layers that balance comprehensive storage with efficient retrieval. This approach guarantees recent information is always available, conducts precise searches when needed, and properly tracks how information evolves over time. For high-stakes conversations where memory failures aren't acceptable, Amigo delivers a system that understands what's important, remembers it completely, and applies it appropriately based on the current context.
Amigo's dynamic behavior system solves a core AI challenge: applying the right knowledge at the right moment. Just as human experts must focus their attention properly to be effective, our system ensures AI responses draw on relevant expertise for each situation. Dynamic behaviors work by recognizing subtle conversation patterns and using specialized instructions to guide the agent's next action through a unified response framework - this can include knowledge retrieval, tool calling, deep reflection, real-time context graph modification, data integrations, and others.
For example, this approach can transform interactions by providing evidence-based context, personalizing recommendations, and offering practical alternatives—all while maintaining natural conversation flow as topics change. The system balances flexibility in casual discussions with necessary structure in critical situations, creating more helpful and contextually aware responses.
Our programmatic evaluation framework transforms AI assessment from subjective judgment to systematic measurement, serving as both a precision evaluation tool and a strategic bridge toward superhuman performance. Built on custom metrics that quantify success criteria, the system uses sophisticated persona simulations to intelligently test against thousands of predefined scenarios to fully saturate complex problem spaces. These adversarial simulations leverage stronger domain-specific models to apply the pressure necessary for advancement, while targeted unit tests verify critical behaviors and built-in safeguards (like red-lining critical functions) ensure regulatory compliance.
This comprehensive approach eliminates testing bottlenecks while providing complete accountability through a traceable, reproducible process. By establishing stable evaluation criteria that transcend underlying technological changes, the framework enables evidence-based decision making that helps organizations progress from baseline capabilities to optimized performance. Rather than relying on theoretical assumptions, this metrics-first methodology ensures that real, measurable improvements guide strategic development choices regardless of evolving AI architectures.
Amigo's RL framework transforms AI alignment from a one-time training event into a continuous improvement process that strategically targets enhancement opportunities. Our system implements iterative training cycles that incorporate real-world interaction data and targeted simulations to enhance performance. This continuous loop detects and corrects misalignments before they cause problems, deepens the system's understanding through every edge case, and establishes a clear path from baseline capabilities to superhuman performance.
Rather than applying RL generally, the key to our approach is focusing investments where they matter most—our dynamic multi-step context engine handles routine control functions while our evaluation system provides visibility into performance, allowing us to concentrate RL exclusively on high-leverage capabilities that directly impact business outcomes. By balancing the exploration of novel strategies with the exploitation of proven approaches, the framework optimizes agent behaviors while maintaining reliability. This comprehensive approach ensures sustained alignment with business objectives while positioning organizations to benefit from each new technological breakthrough in AI.