System Components

Amigo System Components

Amigo's system components implement the unified cognitive architecture we've designed. Our architecture is built on a fundamental principle: models are entropy-aware. They understand not just how to solve problems, but the optimal approaches for different problem types, the entropy properties of each approach, and whether any step falls outside their effective operating range.

This entropy awareness, however, depends entirely on context. A perfect context allows models to figure out what the best next steps are. When context degrades, entropy-awareness reasoning degrades, and the degradation accelerates rapidly. This relationship is essential for determining the problem approach and setting up the context for the next quantum of action in any problem's forward progression. Point-in-time accurate problem assessment leads to optimal solution path determination, ensuring that the new context generated for the next step isn't degraded. This preservation means that entropy awareness for the following step isn't operating on faulty foundations, which protects the entire cycle.

This page explains how our six core components—Agent Core, Context Graphs, Dynamic Behaviors, Functional Memory, Evaluations, and Reinforcement Learning—serve as an orchestration framework to create the near-perfect point-in-time context essential for driving perfect entropy stratification.

The Design Philosophy Behind Our Architecture

Perfect entropy stratification within a problem neighborhood means that for all verifiable economic work units in that neighborhood, we have discovered an optimal composition where each quantum of work connects to complete the economic work unit. In this composition, each quantum of work is assigned to the optimal topology—whether that's a specific LLM, tool, search system, algorithm, or other resource that's also been optimized for its role. Here, optimal means we pass the verification threshold while keeping the cost as low as possible.

Problem neighborhoods are human business value domains with shared properties that make the problem space coherent, such as mental health handling or prescription management. Think of it as finding the perfect "cognitive gear" for every quantum of operation down to what would be considered an "atom."

Entropy-aware models can accurately assess whether a problem requires precision or creativity. When they have a unified context, they can automatically select the right cognitive approach for each quantum of action, each step forward in solving the problem. Each quantum of action powers how the context is built for the next quantum.

The power of our approach lies in leveraging a beneficial circular dependency. Entropy awareness and unified context are mutually dependent across problem evolution quanta—each forward step in problem-solving progression. You need entropy awareness to maintain perfect context as problems evolve because you must understand how complexity shifts to manage the right contextual information at each quantum of action forward. Conversely, you need perfect context to sustain good entropy awareness because accurate complexity assessment requires perfect point-in-time context.

This circular dependency creates a powerful intelligence-on-intelligence pattern where each component enhances the other in a virtuous cycle. Our unified entropic framework leverages intelligence-managed context engines to orchestrate cognitive architecture, creating a recursive optimization where intelligence manages the very contextual infrastructure that enables intelligent decision-making. This ensures perfect point-in-time context flows throughout the system, and the entropy awareness applied to each context is perfected to the degree that verification of the complete economic work unit succeeds.

The stable identity foundations, invariant structural blueprints, temporal continuity preservation, and intelligent adaptation mechanisms create a systematic context management framework that maintains this precision across all operational transitions, contributing to deliverable economic work units.

Each component in our system contributes specific capabilities to this systematic context management framework, working together to maintain the precision required for successful economic work unit verification. This manifests operationally through Memory, Knowledge, and Reasoning (M-K-R) as interconnected facets of a single cognitive system, where optimization in any area cascades through the entire system because all components share the same contextual foundation.

Agent Core

Amigo's agent architecture creates stable professional identity foundations through immutable core attributes that persist across all problem domains and context variations. Each agent is defined through unchanging core attributes—identity, background, expertise, and philosophy—that provide stable self-perception anchors. However, these attributes remain latent potential until activated and shaped by context. A doctor at a party is just another person, but becomes a medical professional when the situation demands it.

For detailed technical implementation, see Agent Core Concepts.

Contribution to Perfect Entropy Stratification

The Agent Core provides a stable professional identity foundation that manifests its properties only when composed with context. This context-dependent activation is fundamental to entropy stratification—the same identity exhibits entirely different characteristics based on the problem space it operates within. A doctor's medical expertise remains dormant in casual conversation but activates immediately in health-related contexts, demonstrating how identity properties emerge through contextual composition rather than as fixed traits.

This identity-context composition operates in high-bandwidth integration with all other components during session interactions. When a medical context graph activates the doctor identity, it doesn't just "turn on" medical knowledge—it fundamentally shapes how that identity perceives and approaches problems. The professional identity determines how "this problem requires precision" versus "this problem needs creativity" is assessed, but only when the context calls for that professional lens. The same doctor identity might emphasize supportive guidance over clinical precision in a wellness coaching context.

The agent's immutable core attributes serve as potential capabilities that become actual behaviors only through contextual activation. This latent-to-active transformation is what enables coherent complexity assessment across problem spaces. The professional identity acts not as a fixed lens but as an adaptive interpretive framework that manifests different properties based on contextual requirements. This ensures that entropy awareness remains appropriately calibrated—high precision when medical accuracy is needed, creative flexibility when emotional support is primary, casual relatability when building rapport. The identity provides the potential; the context determines the manifestation.

Context Graphs

Context graphs fundamentally define the problem—nothing in the system makes sense without this minimal problem structure. They represent the invariant parts and blueprint structure of one coherent problem, capturing the service purpose, structural topology, and local intricacies that make a problem space navigable. These partial frameworks become complete only when composed of an agent's identity and dynamic behaviors.

Context graphs offer tremendous flexibility in their design approach. They can range from extremely open, reactive support states that rely entirely on agent intelligence and dynamic behaviors to handle any situation, to strict workflows where each step is necessary and precisely defined. This spectrum allows organizations to match the graph's constraint level to their business requirements, using open graphs where agent expertise should drive the interaction, and structured workflows where compliance or safety requires specific procedural steps.

For detailed technical implementation, see Context Graph Concepts.

Contribution to Perfect Entropy Stratification

Context graphs enable perfect entropy stratification by defining the problem space itself at three levels of granularity, each serving a distinct role in entropy management.

The Conceptual Level defines the problem fundamentally through the service description—the session's purpose and the value being delivered. This conceptual grounding enables agents to understand why they're navigating this particular problem space, informing their assessment of when precision versus creativity is appropriate. Without this problem definition, entropy awareness has no meaningful reference frame.

The Structural Level captures the entire invariant structure of the problem through abstract topology—all possible states and transitions that define how this problem can be navigated. This bird's-eye view shows agents the complete problem landscape, enabling strategic planning and optimal path selection. The topology represents the problem's inherent structure to whatever degree invariance exists, providing the stable scaffolding for entropy assessment.

The Operational Level defines the intricacies at each invariant part of the problem through local guidelines—specific constraints, tool interpretations, data access patterns, and local objectives. These details make the abstract problem structure actionable, providing the concrete guidance needed for optimal execution at each quantum of action.

The context graph's primary contribution is establishing the problem definition that makes all other components meaningful. Agent identity only manifests relevant properties when operating within a defined problem space. Dynamic behaviors can only modify what the context graph has established. Memory can only be relevant in relation to the problem being solved. This foundational role ensures that entropy stratification has a coherent reference frame—you cannot optimize cognitive resources without first defining what problem those resources are addressing.

By defining problems at multiple granularities while maintaining compositional flexibility, context graphs create the essential substrate upon which perfect entropy stratification operates. They establish not just how to navigate, but fundamentally what is being navigated, ensuring that all system components can coordinate effectively to deliver economic work units.

Functional Memory

Functional Memory serves as the operational blueprint that aligns memory organization with the specific functions each agent performs, ensuring that all function-critical information is available at the right interpretation and granularity for live reasoning. The system centers around user models derived from custom dimensional frameworks that organizations design to interpret raw information through interactions.

For detailed technical implementation, see Memory Concepts.

Contribution to Perfect Entropy Stratification

Functional Memory ensures context for users is always available at the right interpretation and depth where it matters, allowing for smart expansion of that interpretation as needed, each quantum of action forward. Unlike traditional approaches that treat all information equally, our dimensional framework organizes memory according to functional importance, determining what information deserves perfect preservation, how contextual relationships should be maintained over time, and when information should be recontextualized based on new understanding. This organization enables perfect context through real-time recontextualization that can occur at each quantum of action as agents traverse through quantum patterns of interaction, ensuring adaptive depth and breadth expansion as well as reinterpretations as needed to match the cognitive approach required for each operational quantum.

The system operates through episodic extraction, where each session generates observations along with their contributing context, building user models that evolve over time while maintaining perfect awareness of what information matters for each specific enterprise function. This temporal continuity supports the beneficial circular dependency by providing a stable memory foundation that enables both entropy awareness and unified context to operate effectively.

The memory system operates through three distinct layers. L0 (Ground Truth) preserves complete raw transcripts with all unprocessed information. L1 (Observations) contains contextualized insights extracted and indexed from raw data. L2 (User Model) provides a synthesized dimensional understanding with structured context.

This layered architecture maintains perfect recall for critical information while optimizing computational resources. L2 provides immediate dimensional context for 90% of interactions, L1 enables contextual search when deeper information is needed, and L0 preserves complete context for high-stakes reasoning. This functional alignment ensures agents have all the context they need for optimal entropy assessment and decision-making without constant information retrieval. The layered approach directly supports entropy stratification by providing the right level of contextual detail for each complexity stratum—quick access for low-entropy decisions, deep retrieval for high-entropy challenges.

Dynamic Behaviors

Dynamic behaviors function as intelligent adaptation mechanisms that complete perfect entropy stratification by serving as general problem space modifiers. They enable real-time cognitive gear shifting based on contextual signals during user engagements and agent thoughts, actions, and events, providing the flexibility needed to handle the full spectrum of variations that static context graphs alone cannot capture.

For detailed technical implementation, see Dynamic Behavior Concepts.

Contribution to Perfect Entropy Stratification

Dynamic behaviors are general problem space modifiers that provide real-time adaptations to the problem context. They can overwrite or enrich context graph local states, connect different graphs, trigger deeper reflection processes, reframe problems entirely, expose external systems through tools, and perform many other adaptations needed for optimal problem-solving. This comprehensive modification capability operates through constrained optimization rather than rigid command execution, using cognitive selection processes that mirror human decision-making: semantic association brings relevant behaviors to mind, logical analysis selects the most appropriate, and constraint solving applies them effectively.

Dynamic behaviors implement entropy control through contextual optimization. Replacement mode provides high constraint when situations require complete optimization focus, offering singular focus for safety-critical scenarios that demand immediate entropy collapse to deterministic responses. Enrichment mode offers adaptive constraints when situations benefit from enhanced capabilities while maintaining the existing problem structure and cognitive flexibility. Semantic association brings relevant behaviors "to mind" based on conversational context and user model, persisting through conversational transitions via associative relevance rather than rigid trigger matching.

Through this comprehensive modification capability, dynamic behaviors enable the same context graph to handle the complete spectrum of variations within its problem domain—from routine interactions to edge cases—while maintaining optimal entropy stratification across all operational transitions. They act as the real-time entropy management system, detecting when the base optimization needs adjustment and providing exactly the right modifications to maintain perfect context at each quantum of action forward.

Evaluations

Our programmatic evaluation framework serves as The Judge from our three-layer framework (Problem Model, Judge, Agent) that answers the fundamental question: "What does successfully solving the problem look like?" The system provides verification that economic work units are delivered acceptably across all problem neighborhoods—the entire customer problem space. This verification framework embodies the strategic objectives that define when problems are considered solved, with evaluation criteria that evolve as market conditions and problem definitions shift.

For detailed technical implementation, see Metrics and Simulations.

Contribution to Perfect Entropy Stratification

Evaluations set a north star for end-to-end optimization of these configurations for the real world. The framework operates as a verification evolutionary chamber where different system configurations, entropy stratification patterns, and solution approaches compete under verification pressure. Through sophisticated persona simulations and adversarial testing across thousands of predefined scenarios, the system enables the discovery of optimal architectures that balance efficiency, performance, and accuracy for specific problem neighborhoods.

Verification of economic work units is multi-dimensional, requiring assessment from multiple angles—both verifying sub-components are correct (calculations, data accuracy, logical steps) and a holistic evaluation of whether the overall deliverable meets intended business value. This continuous verification prevents entropic drift, ensuring the system maintains alignment with reality as it evolves and that successful configurations naturally emerge through selective pressure from economic work unit verification.

The evaluation framework creates the evolutionary pressure that drives the discovery of optimal entropy stratification patterns. It identifies which combinations of agent identities, context graph structures, dynamic behavior sets, and memory strategies create the most effective cognitive architectures for each problem neighborhood. This discovery process is what enables the system to achieve perfect entropy stratification, not through predetermined rules, but through empirical validation of what actually works.

Reinforcement Learning (RL)

Amigo's RL framework provides continuous optimization where reinforcement learning fine-tunes system topologies within their entropy bands for particular use-cases while maintaining perfect contextual awareness to guide each problem-solving quantum of action. The agent learns in an evolutionary pressured environment of verification in simulated worlds powered by problem models, operating under the productive tension between Problem Model requirements and Judge expectations from our three-layer framework.

For detailed technical implementation, see Reinforcement Learning Concepts.

Contribution to Perfect Entropy Stratification

Reinforcement Learning closes foundational reasoning gaps through continuous optimization. Rather than applying RL broadly, the system focuses investments where they matter most—concentrating RL exclusively on high-leverage capabilities that directly impact business outcomes while the systematic context management framework handles routine control functions. This targeted approach detects and corrects misalignments before they cause problems, deepens system understanding through every edge case, and establishes a clear path from baseline capabilities to optimal performance.

The RL framework operates within the verification evolutionary chamber, where successful entropy stratification patterns propagate throughout the system as improved configurations emerge through competitive selection pressure. This ensures that optimization in any area cascades through the entire system because all components share the same contextual foundation, preventing entropic drift while enabling continuous discovery of better entropy stratification patterns that maximize both efficiency and performance for specific problem neighborhoods.

RL serves as the fine-tuning mechanism that takes discovered entropy stratification patterns and optimizes them to their theoretical limits. While evaluations discover which broad configurations work, RL perfects the precise parameters within those configurations—the exact thresholds for cognitive gear shifting, the optimal timing for memory expansion, the perfect balance between agent autonomy and structural guidance. This continuous refinement ensures that entropy stratification not only meets verification thresholds but also approaches optimal efficiency.

How Components Work Together

These six components form a unified cognitive architecture that manifests operationally through Memory, Knowledge, and Reasoning (M-K-R) as interconnected facets of a single cognitive system rather than separate components. This integration operates at two distinct levels: session-level quantum interactions and system-level optimization.

Session-Level Component Integration: The High-Bandwidth Unified Cognitive System

During session interactions, all components operate through quantum patterns of state transitions. An agent can take an arbitrary number of steps before responding to a user, with each interaction composed of quantum patterns such as [A] → [A] (direct action-to-action), [A] → [D] → [R] → [A] (action to decision to reflection to action), [A] → [C] → [D] → [A] (memory-informed decision making), or any valid combination of state transitions.

Each state itself can contain smaller quanta of operations—tool calls, memory queries, internal computations. The only guarantee is that the agent responds to users in an action state, but the path to that response can involve complex internal processing invisible to the user.

This quantum-based traversal operates through complete multi-directional influence, where every component affects every other component in sophisticated ways. Memory influences dynamic behavior selection as user preferences and history affect which behaviors activate. It shapes agent thinking patterns as past interactions guide reasoning approaches. It affects context graph navigation as memory of previous paths and outcomes guides future traversal decisions, providing the historical foundation that makes current decisions contextually aware.

Context Graphs influence memory recontextualization, with specific states, like recall states, triggering targeted memory operations. They determine how memories are interpreted as the problem structure establishes which memories are relevant. They shape agent identity manifestation as different graph structures elicit different aspects of the same identity. They determine dynamic behavior applicability as the graph structure defines which behaviors can meaningfully apply.

Agent Identity influences memory formation and interpretation as the professional lens shapes what's remembered and how. It drives navigation preferences as identity determines choices between reflection and direct action. It affects dynamic behavior expression as the same behavior manifests differently based on agent identity. It provides the interpretive framework through which all other components are understood.

Dynamic Behaviors influence memory recontextualization as behaviors can trigger new interpretations of past events. They modify graph states by enriching or replacing local contexts. They alter agent expression modes by modifying how identity manifests. They create new pathways through the problem space that didn't exist in the base graph.

Unified Context emerges from all components operating as one system. Memory, identity, graph structure, and behaviors don't just influence each other bilaterally but create a unified contextual field where every element simultaneously shapes and is shaped by every other element. This unified context itself becomes a force that influences all components, creating recursive optimization where better context leads to better component coordination, which generates even better context.

This creates an intelligence-on-intelligence pattern with extreme bandwidth. The path from user input to agent response involves sophisticated multi-step reasoning where memory informs behavior selection while behaviors reshape memory interpretation, graphs guide navigation while navigation experiences update memory, identity shapes thinking while thinking reveals new facets of identity—all happening simultaneously to maintain perfect point-in-time context and optimal entropy stratification for each problem quantum.

System-Level Optimization Framework

Beyond session interactions, the broader system uses verification results to continuously optimize the orchestration framework.

Evaluations operate as a complex verification system that reasons about failure modes against orchestration configurations across entire session evolution cycles. The system analyzes verification failures across entire neighborhoods to gain a deep understanding of failure mechanics, proposing intelligent variations with potential to succeed while balancing concerns like overfitting, creating intelligent improvement cycles through sophisticated reasoning about why failures occurred and how configurations should evolve.

Reinforcement Learning operates within this verification evolutionary chamber to fine-tune system topologies within their entropy bands, optimizing how the orchestration framework components integrate and how successful context management patterns propagate throughout the system.

The Emergent Intelligence

This 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 architecture ensures that perfect contextual information flows between all components during sessions, preventing the missing context that would cause suboptimal problem evolution, while the broader verification-driven optimization prevents entropic drift through continuous evolutionary pressure that enables discovery of better context management strategies, maximizing both efficiency and performance for specific problem neighborhoods.

The ultimate result is a system where entropy awareness and unified context reinforce each other through the coordinated action of all six components, creating an orchestration framework that not only maintains perfect point-in-time context but continuously improves its ability to match cognitive resources to problem complexity, achieving true perfect entropy stratification.