Agent Core

The Amigo platform is used to develop advanced conversational agents to address complex problems by navigating dynamically-structured contexts. Traditional conversational AI either relies on rigid scripts or lacks structured guidance entirely; Amigo Agents are instead built to adaptively navigate across context graphs to achieve a balance between strong control over behavior and situational flexibility. This design mimics how human experts deliver services.

An Amigo agent is made up of two key components (Core Persona and Global Directives as detailed in the following subsections) that dictate how it behaves in a vacuum. However, problem solvers do not exist in a vacuum - this is why our agent architecture is unified with the other key components of the Amigo system: context graphs, functional memory, and dynamic behaviors. These components are not treated as isolated silos but as deeply intertwined facets of a single cognitive challenge, enabling a holistic approach to Memory, Knowledge, and Reasoning. The effectiveness of an Amigo agent hinges on the high-bandwidth integration and cyclical optimization of these elements.

Domain Specialization and Architectural Constraints

Amigo's approach to agent architecture emphasizes domain specialization as a response to the fundamental "token bottleneck" limitation of current AI models. Domain-specialized agents measurably outperform generalists in specific fields, even with identical knowledge access, through:

Optimized Domain Reasoning: Each specialized agent develops more efficient externalization patterns for its specific domain, creating effective workarounds for token bottleneck constraints.
Latent Space Efficiency: Domain focus enables consistent activation of the most relevant regions of the model's latent space without interference from competing domains.
External Scaffolding Integration: Specialized agents work seamlessly with context graphs and memory systems to compensate for the model's inability to maintain rich internal representations across reasoning steps.

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Domain Specialization and Architectural Constraints

Optimized Domain Reasoning: Each specialized agent develops more efficient externalization patterns for its specific domain, creating effective workarounds for token bottleneck constraints.

Latent Space Efficiency: Domain focus enables consistent activation of the most relevant regions of the model's latent space without interference from competing domains.

External Scaffolding Integration: Specialized agents work seamlessly with context graphs and memory systems to compensate for the model's inability to maintain rich internal representations across reasoning steps.