[Advanced] Behavior Chaining
Beyond simple behavior selection, the Amigo system enables sophisticated behavior chaining.
The Semantic Mesh Architecture
Dynamic behaviors form a semantic mesh—a fluid, interconnected network where behaviors are linked through soft semantic edges rather than hard-coded transitions. This is fundamentally different from traditional state machines:
Explicit transitions between states
Semantic adjacency through trigger design
Fixed, enumerable pathways
Emergent pathways based on context
Brittle to unexpected inputs
Adaptive to novel combinations
Requires manual flow editing
Self-organizing through semantic proximity
Complexity grows linearly with states
Complexity scales through semantic relationships
The semantic mesh emerges from the interaction between conversational triggers, agent outputs, and the multi-vector broadcast system. Rather than defining explicit edges between behaviors, designers create semantic adjacencies that allow behaviors to naturally chain based on conversational evolution.
Why "Semantic Mesh" Instead of "State Machine"?
A traditional state machine requires you to enumerate every possible transition. With hundreds of behaviors, this becomes unmanageable. The semantic mesh allows behaviors to connect through meaning rather than explicit rules—if a behavior's output naturally produces content that triggers adjacent behaviors, the system self-organizes without requiring hand-coded connections.
Self-Reinforcing Behavior Design
A key property of the semantic mesh is its self-reinforcing nature. Dynamic behaviors influence:
Agent inner thoughts: What the agent reasons about internally
Agent output: What the agent says to the user
Tool calls: Which tools the agent invokes
Actions: What external effects the agent triggers
Because all of these elements feed back into the multi-vector broadcast system, a behavior can influence the ranking of subsequent behaviors through its effects on the agent. This creates intentional chaining opportunities:
This self-reinforcement means behaviors should not be designed in isolation. When creating a new behavior, consider:
What will the agent likely think about when this behavior is active?
What will the agent say that might trigger related behaviors?
What tool calls might create conditions for follow-up behaviors?
Core Concepts
Behavior clusters. Behaviors are grouped by domain or intent so transitions feel natural and auditable.
Selector model. A reasoning layer ranks candidates; chaining intentionally shifts those rankings.
Transition rationale. Each hop records the evidence that justified it, preserving post-hoc explainability.
Context graph guardrails. Chains respect state topology and safety constraints—you are orchestrating within the graph, not bypassing it.
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.
Topical Mapping at Multiple Granularities
A sophisticated aspect of trigger design for behavior chaining is topical mapping at multiple granularities. Conversational triggers can operate at different levels of abstraction:
Abstract Triggers (General Coverage)
Abstract triggers cast a wide semantic net, activating behaviors across a broad topical range:
"exercise"- Activates for any fitness-related discussion"mental health"- Activates across the emotional wellness spectrum"nutrition"- Activates for any food/diet conversation
These triggers ensure behaviors remain available across the general distribution of relevant conversations.
Concrete Triggers (Specific Scenarios)
Concrete triggers target precise scenarios with high semantic specificity:
"user wants to exercise with a sprained ankle"- Activates for injury-modified workouts"user is experiencing panic attack symptoms"- Activates for acute anxiety responses"user has diabetes and asks about carbohydrate intake"- Activates for condition-specific nutrition
These triggers ensure behaviors activate for specific edge cases that require specialized handling.
Combining Granularities
Effective behavior design often combines both levels:
The abstract triggers ensure general relevance, while concrete triggers boost ranking for specific high-priority scenarios. This topical mapping across the full distribution ensures the behavior activates appropriately for both common and edge cases.
Retrieval Theory Insight
The retrieval conditions for content should not be purely based on the semantic meaning of the content itself. A behavior about "managing exercise with chronic conditions" should have triggers that match how users describe their situation, not just the clinical terminology in the behavior's instructions.
Design triggers based on how users express needs, not how experts categorize solutions.
Designing for Infinite Complexity
The semantic mesh architecture enables arbitrarily complex behavior networks without proportional increases in maintenance burden. Consider:
With 2 behaviors linked to 2 others each, you have ~4 potential pathways
With 100 behaviors each linked to 2-3 semantically adjacent behaviors, you have thousands of potential pathways
With 500+ behaviors (as in production deployments), the pathway space becomes effectively infinite
This complexity emerges from semantic relationships, not explicit configuration. When you add a new behavior:
Define its triggers based on desired activation contexts
Consider which existing behaviors might naturally lead to or from it
The system automatically integrates it into the mesh
No manual wiring required—the semantic proximity handles integration automatically.
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 user inputs, 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.
Chaining Pattern
A typical sequence uses four roles:
Anchor behavior addresses the presenting need and primes data collection.
Bridge behavior broadens or narrows focus while keeping the user experience organic.
Target behavior performs the intended follow-on task (safety check, plan creation, escalation prep).
Stabilizer behavior verifies outcomes and prepares the next state or exit.
Each link outputs structured signals that bias the selector toward the next desired behavior, making the chain deliberate rather than coincidental.
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.
Implementation Checklist
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.
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