[Advanced] Behavior Chaining

Beyond simple behavior selection, the Amigo system enables sophisticated behavior chaining. This architecture creates a powerful meta-control capability where the agent can influence its own trajectory through behavior spaces, affecting future behavior selection probabilities through its own outputs.

Behaviors are organized into clusters representing different domains, tasks, or conversational modes. This creates a navigable terrain that the agent can traverse by hopping between semantically-adjacent clusters without the need for explicit metadata or mode changes.

As a result, the agent is able to proactively shape conversational direction. By creating predictable shifts in behavior rankings, the agent can transition between different clusters of behaviors while maintaining conversational coherence. For example, when discussing a user's exercise routine, the agent might deliberately introduce nutrition concepts that gradually shift the conversation toward dietary behaviors without an abrupt topic change. This enables the design of multi-step conversational journeys that feel natural rather than rigidly programmed.

This implicit logical mesh—powered by associative proximity and the behavior-selection reasoning model—offers several key advantages. First, it provides reasoned safety since each transition is decided by the reasoning agent, avoiding brittle jumps while upholding context graph constraints. Second, it enables broad coverage where even unanticipated domain overlaps can trigger valid transitions, reducing blind spots without requiring hand-coded connections. Third, as the selector model evolves, chaining adapts automatically, unlocking richer multi-domain traversal paths. Finally, the system requires lower maintenance as designers can focus on creating effective triggers and clusters, with new behaviors integrating seamlessly without manual flow editing.

For experience designers, behavior chaining offers a powerful middle ground between completely unstructured conversations and rigid decision trees. Designers can create structured experiences that follow intended pathways while still adapting to individual user preferences and inputs. This approach enables predictable progression through information spaces with an appearance of freedom alongside subtle structural guidance, resulting in context-aware conversational pathways based on both immediate inputs and emerging patterns that achieve conversational goals without sacrificing naturalness.

Behavior Chaining with Side-Effects: Orchestrating Beyond Conversation

When integrated with side-effects, behavior chaining extends beyond conversation to create a unified orchestration layer for both dialogue and external actions. This integration enables agents to navigate conceptual spaces while sequencing and coordinating system interactions with precision. The agent can guide conversational trajectories to create conditions for specific side-effects to trigger at appropriate moments, combining conversation and action into an integrated experience.

This integration transforms behavior chaining from conversational navigation to comprehensive experience orchestration. An agent can direct the conversation toward conditions that warrant specific tool invocations, data integrations, or external system interactions, then transition back to conversation, carrying relevant context forward. For example, in a healthcare scenario, the agent might navigate through symptom assessment behaviors before triggering diagnostic tool engagement, followed by transition to treatment discussion behaviors—all while maintaining conversational context.

The combination creates an action-perception loop where side-effects generate data that influences subsequent behavior selection. This feedback mechanism allows for adaptive workflows where each side-effect potentially modifies the behavior selection for subsequent turns, creating pathways that respond to emerging information. For instance, a financial advisory agent might transition through risk assessment behaviors, trigger portfolio analysis tools, and then navigate to different recommendation behaviors based on the analysis results—all appearing as a continuous conversation to the user.

This integration enables multi-turn, multi-modal experiences that maintain coherence across complex workflows. The agent can coordinate sequences combining information gathering, external processing, data visualization, and explanatory dialogue without requiring explicit programming of each transition. This allows for applications like guided diagnostics, advisory services, or multi-step collaborations that adapt to user inputs while following coherent process frameworks.

Knowledge Activation Through Behavior Chaining

Behavior chaining provides an approach to knowledge activation that enables agents to direct their navigation through knowledge spaces. Through this meta-control mechanism, agents can guide conversation flows across different domains of expertise, creating knowledge activation pathways that adapt to context through cluster-leaping between adjacent knowledge domains. Rather than only responding to prompts, agents can shape their trajectory through knowledge spaces, creating structured conversational journeys that activate relevant knowledge clusters when needed.

Each activated knowledge cluster functions as a node in a semantic mesh, with the agent dynamically hopping between them (e.g., "exercise physiology" → "sleep recovery") based on real-time context signals. This cluster-leaping mechanism weaves multi-domain expertise without rigid hand-coded flows, relying instead on soft-association edges validated against context graph constraints.

When integrated with side-effects, behavior chaining creates additional knowledge application capabilities. Agents can execute sequences where conversational knowledge activation is combined with external data retrieval, tool utilization, and system integrations. This creates a knowledge-action cycle where the agent navigates from conceptual understanding to practical application and back, maintaining context throughout the process. For example, an agent might activate medical diagnostic knowledge frameworks, transition to laboratory test analysis tools, and then apply treatment protocol knowledge—all as part of a coherent process that functions as a unified interaction for the user.

This capability changes how knowledge is accessed and applied in conversation, enabling transitions between specialized domains while maintaining dialogue coherence.

Even within today's architecture it is the bandwidth between knowledge activation (Knowledge) and live reasoning (Reasoning) that determines whether an agent merely recites information or can apply it across multi‑step plans. This interplay is further enriched by Functional Memory, which provides the necessary context (Memory) for both effective knowledge activation and relevant reasoning. The Amigo Functional Memory System, as part of this unified M-K-R cognitive challenge, widens that channel so the right knowledge, influenced by and influencing memory, at the right granularity, arrives exactly when the reasoning engine needs it, facilitating a cyclical and deeply integrated optimization process.