Dynamic Behavior

Dynamic Behavior is a unified system that enables conversational agents to manage knowledge, intuition, side-effects, actions, and data integration. Effectively, it allows the selection agent to examine available options to modify its own local context graph topology and cause side-effects. This powerful mechanism enables agents to respond adaptively to subtle conversational cues, seamlessly integrating foundational knowledge with real-time data through a unified framework.

Core Mechanism

Context Detection

• Identifies conversational patterns, emotional states, user intent, and situational contexts

• Evaluates both explicit statements and implicit expressions of user needs

• Considers the full conversation history while prioritizing recent user inputs

• Recognizes when users pivot to new topics or express different needs

• Adapts dynamically to shifting conversation flows

Advanced Ranking Algorithm

• Utilizes a sophisticated multidimensional approach to behavior ranking

• Separately evaluates user context and conversation history

• Excels at handling topic pivots by prioritizing recent user inputs when appropriate

• Ensures relevant behaviors rise to the top of candidate selection regardless of conversation shifts

• Balances immediate context with conversation continuity for natural transitions

• Adapts ranking weights dynamically based on conversation patterns

• Performs relative ranking rather than exact trigger matching, making the system highly associative

• Conducts logical assessment of behavior applicability against current session context conditioned on user model

• Enables behaviors to be ranked and selected even when triggers are only conceptually related to conversation content

• Triggers function purely as relative ranking mechanisms, not requiring exact or close matches

• Can be completely associative tags (e.g., "exercise," "anger," "protein") or specific identifiers (e.g., a specific drug or food like "chicken")

Behavior Selection Process

1. Candidate Evaluation

   • Presents candidate instructions to the behavior selection agent

   • Selection agent conditions against interaction logs and user model

   • Evaluates each candidate within the current conversation context

2. Selection Decision

   • The selection agent has three options:

     • Continue using the previous dynamic behavior (if any) to maintain thread consistency

     • Select a new candidate behavior that better fits the current context

     • Select nothing if no behavior is appropriate

3. Context Graph Integration

   • If a behavior is selected, it is injected into the action guidelines of the current context graph state

   • This modifies the problem topology, directly influencing agent behavior

   • The selected behavior becomes part of the agent's decision-making framework

   • Merges with the local context graph state rather than providing rigid instructions

   • Enriches the topological field with more nuanced action guidelines

4. Adaptive Application

   • Matches conversation patterns to relevant behavioral guidelines

   • Prioritizes behaviors most applicable to the current context

   • Conditions behavior selection on user model information (see Memory section for more details)

   • Maintains consistency when conversation continues on the same topic

   • Smoothly transitions to new behaviors when conversation dynamics change

   • Intelligently selects behaviors that complement the agent's core capabilities

Instruction Flexibility and Contextual Application

• Instruction Flexibility Spectrum

  • Instructions can range from open-ended guidance allowing significant agent discretion

  • To extremely rigid and strict protocols when precise behavior is needed

  • The chosen approach depends on the use case and required outcomes

• Contextual Application Process

  • When a dynamic behavior is selected, it doesn't guarantee 100% enactment by the agent

  • Instead, it merges with the local context graph state

  • This enriches the topological field (action guidelines)

  • Provides more guidance by creating a more constrained, guided, or contextually rich environment

  • The evaluation of applicability is logically assessed against the current session context, conditioned on the user model

Autonomy Spectrum Through Trigger and Context Design

• Vague Triggers with Open Context: Creates more autonomous agents

  • Functions like an associative knowledge cluster that the agent can freely draw from as context evolves

  • Enables agent to intelligently determine behavior based on user model and interaction context

  • Gives agent freedom to select most relevant knowledge in dynamic situations

  • Suited for creative, exploratory, or coaching conversations where flexibility is valued

• Strict Triggers with Precise Instructions: Simulates protocol overrides

  • Creates highly constrained decision spaces for predictable behavior

  • Ensures regulatory compliance and consistent handling of sensitive topics

  • Functions similar to "override protocols" in critical situations

  • Essential for medical, financial, legal, and safety-critical contexts

• Balanced Implementation: Most deployments use a strategic mix across the spectrum

  • Critical areas use strict triggers and instructions

  • Everyday conversational areas use vaguer triggers and more open context

  • Creates systems that balance compliance with adaptability

Adaptive Response Generation

• Integrates selected behaviors into the agent's response strategy

• Balances adhering to guidelines with maintaining natural conversation

• Enriches responses with contextual awareness and understanding

• Enables more human-like, intuitive interaction patterns

• Updates response approach as the conversation evolves

Example: Dynamic Behavior Implementation

Here is how a typical dynamic behavior is structured and implemented:

{
  "generated_at": {
    "$date": "2025-03-21T01:14:18.308Z"
  },
  "conversational_trigger": "User expresses intention to adopt an extreme exercise routine or overtraining pattern",
  "conversational_trigger_embedding": {
    "$binary": {
      "base64": "...",
      "subType": "09"
    }
  },
  "instructions": "As the fitness companion chat assistant, your role is to promote balanced and sustainable exercise approaches. Focus on helping users develop realistic fitness habits that are maintainable long-term. Research consistently shows that gradual progression leads to better outcomes than extreme training approaches. When a user mentions plans to exercise excessively or adopt an extreme workout schedule, inquire about their fitness goals and previous exercise history. Ask what has worked for them in the past and whether they've experienced burnout before. Explain that sustainable progress typically comes from balanced training that includes adequate rest and recovery periods. Suggest starting with a moderate routine that can be consistently maintained rather than an intense program that might lead to injury or abandonment. Offer to help create a progressive plan that gradually increases intensity as their fitness improves. Recommend integrating rest days and emphasize that improvement happens during recovery, not just during workouts. Ask which approach feels most realistic for their current lifestyle, and emphasize that consistency over time yields better results than short periods of extreme effort.",
  "is_active": true,
  "region_name": "region-1"
}

Dynamic Behavior In Action

Here's how this dynamic behavior transforms a conversation:

Without Dynamic Behavior:

User: "I want to get serious about fitness. I'm planning to work out 2 hours every day with no rest days."

Agent: "That's great you're committed to fitness! 

A daily two-hour workout routine will definitely accelerate your progress.

Do you have a specific training split planned for your daily sessions?"

With Dynamic Behavior Applied:

User: "I want to get serious about fitness. I'm planning to work out 2 hours every day with no rest days."

[INTERNAL: Dynamic behavior triggered - "User expresses intention to adopt an extreme exercise routine or overtraining pattern"]

Agent: "I appreciate your commitment to fitness. That's a great first step!

I'm curious about your previous exercise experience - what types of workouts have you done before, and how did your body respond to them?

Many people find that training without adequate rest can lead to diminishing returns and potential injury. Research shows that recovery is actually when your body builds strength and endurance.

What if we designed a progressive plan that includes strategic rest days? Perhaps starting with 3-4 balanced sessions per week and then gradually increasing as your fitness improves?

What type of exercise schedule do you think would be sustainable with your current lifestyle?"

The dynamic behavior has fundamentally transformed the response by:

1. Introducing Evidence-Based Context: Sharing research about recovery and progressive training

2. Personalizing the Interaction: Asking about previous exercise experience

3. Reframing the Goal: Shifting from extreme training to sustainable progression

4. Providing Actionable Alternatives: Suggesting a more balanced training approach

5. Supporting Agency: Asking what would work with their lifestyle

Unified Knowledge and Data Integration

Dynamic behaviors serve as the critical bridge between foundational knowledge and real-time data through a sophisticated side-effect framework:

The Side-Effect Architecture

Dynamic behaviors can trigger specific side-effects that extend beyond conversation and modify the local context the agent is currently active in:

1. Tool Integration

   • Triggered side-effects can dynamically invoke real-time data tools based on conversation context

   • Tools can be forced or made available via context graph modification

   • Example: When a user mentions "feeling tired after my workout yesterday," the behavior triggers heart rate trend analysis from connected wearable device data

   • Tools are invoked based on behavioral context rather than explicit commands

Example:

User: "My running pace hasn't improved in two weeks even though I'm training consistently."

[INTERNAL: Behavior triggers performance trend analysis tool]

Agent: "Looking at your running data, I can see you're actually following a common training pattern.

Your overall trend shows steady progress with these natural plateaus along the way.

The data shows your average pace improved by 45 seconds per mile over 6 weeks, which is sustainable, healthy progress.

These 10-14 day plateaus are actually normal adaptation periods where your body is consolidating gains."

1. Context Graph Modification

   • Every time a dynamic behavior is selected, the context graph is modified

   • The modification always includes additional context infusion but can extend to:

     • New tool exposure

     • Hand-off to external systems

     • New exit conditions to consider

     • Reflection and self-modification capabilities

   • Example: Financial compliance behavior adds verification exit conditions when detecting risk discussion

Example:

User: "I've been feeling really dizzy and nauseous since increasing my medication."

[INTERNAL: System detects potential serious side-effect and modifies context graph to add safety verification]

Agent: "Dizziness and nausea can be concerning side effects.

I'd like to check a few important details:
- When did these symptoms start?
- How severe is the dizziness?
- Have you been able to stay hydrated and maintain your normal activities?

This information will help determine appropriate next steps."

[INTERNAL: Context graph modified with new exit condition - if symptoms are severe, transition to medical support referral pathway]

1. Reflective Processing

   • Triggered side-effects can:

     • Generate structured inner thoughts in interaction logs for transparency

     • Capture decision rationales for audit and improvement

   • Example: Behavior trigger generates reflection showing clinical reasoning process

Example:

[INTERNAL REFLECTION: User has mentioned third consecutive day of severe headache with medication. Pattern indicates potential adverse reaction requiring medical attention rather than normal side-effect. Will acknowledge concern, gather minimal essential information, and direct to appropriate medical support per safety protocol.]

Agent: "A severe headache lasting three days while on this medication requires medical attention.

This isn't typically expected and should be evaluated by a healthcare provider.

Please contact your prescribing physician today or seek urgent care if the headache is worsening."

1. Enterprise Integration

   • Triggered side-effects can:

     • Kick off asynchronous metric generation for operational insights

     • Send webhook notifications when threshold conditions are met

     • Enable seamless handoff to human experts when necessary

   • Example: When detecting potential medical escalation in user language, behavior triggers clinical assessment and prepares handoff package

Example:

User: "I've been having chest pain and trouble breathing since starting this program."

[INTERNAL: System detects potential medical emergency, triggers healthcare provider alert]

Agent: "Chest pain and breathing difficulties require immediate medical attention.

Please call emergency services (911) right now or go to the nearest emergency room.

I've also notified the healthcare team, and they'll follow up with you directly.

Your safety is the top priority."

[INTERNAL: System has sent alert to healthcare team with conversation context and user contact information]

User Context: Tony (previous knee injury, recovering from sports rehabilitation, using fitness tracking device)

1. Tony: "I'm feeling tired today after my workout"

2. Dynamic Behavior Selection:
   - "Exercise Safety" behavior selected based on injury history in user model
   - Triggers side-effect: Activity data integration from wearable device
   - Initiates internal reflection to assess fatigue patterns 

3. Side-Effect Execution:
   - Retrieves yesterday's workout intensity and heart rate recovery metrics
   - Compares against baseline patterns for this user
   - Generates risk assessment score for potential overtraining

4. Context Graph Modification:
   - Adds injury prevention instruction to next action state
   - Creates new exit condition requiring safety acknowledgment
   - Records reflection in interaction log for continuity

5. Knowledge + Data Response:
   - Integrates foundational exercise physiology knowledge 
   - Incorporates real-time heart rate variability data
   - Applies rehabilitation context from user model
   - Delivers personalized recovery recommendation

Bridging Knowledge and Data

The unified approach of dynamic behaviors and side-effects solves the fundamental challenge of integrating domain expertise with real-time information. It's important to understand that selected dynamic behaviors provide contextual enrichment rather than rigid directives - they merge with the existing context graph state to create a more nuanced topological field of action guidelines for the agent:

Knowledge Integration

Dynamic behaviors seamlessly incorporate foundational knowledge:

• Contextual Protocol Activation: Behaviors can inject domain protocols exactly when needed

  • Example: When a user mentions side effects from medication, the "Medication Guidance" behavior activates, bringing in precise medical guidelines

• Regulatory Compliance: Behaviors enforce compliant information delivery

  • Example: Financial advice behavior automatically incorporates required disclaimers

• Regionalized Expertise: Behaviors adapt knowledge to geographic context

  • Example: Fitness behavior provides regionally appropriate recommendations based on seasonal climate and available facilities

Example: Knowledge Integration

User: "Is it normal to feel so fatigued after starting this training program?"

Agent: "Increased fatigue is a common response when beginning a new training regimen.

The training process works by [specific physiological mechanism simplified], which temporarily increases your body's recovery needs.

Some strategies that have helped others include:
- Optimizing sleep quality and duration
- Proper hydration and timing of nutrients around workouts
- Strategic recovery days between intense training sessions

How would you describe the fatigue sensation you're experiencing?"

Data Integration

Simultaneously, behaviors manage real-time data flow:

• Biometric Integration: Connected device data incorporated into response context

  • Example: Exercise recommendation behavior incorporates recent activity levels from wearable device

• External Research: Behaviors trigger targeted external research

  • Example: When a user shares a training question, behavior initiates exercise science research and seamlessly integrates findings

• Dynamic Assessment: Behaviors generate real-time assessments based on current context

  • Example: Stress management behavior analyzes tone and content to assess current emotional state

Example: Data Integration

[INTERNAL: System has access to user's sleep tracking data]

User: "I've been following your evening routine suggestion for a week now."

Agent: "I can see the data is showing positive changes!

Your sleep tracking shows your average sleep duration has increased by 38 minutes, and your deep sleep percentage has improved from 18% to 24%.

Your sleep onset time has also become more consistent, which helps regulate your body's natural rhythms.

Have you noticed any differences in how you feel during the day?"

Implementing Dynamic Behaviors with Side-Effects

Implementation Strategy

To effectively implement Dynamic Behavior with side effects:

1. Identify Key Conversation Patterns: Analyze common user questions, concerns, and scenarios

2. Encode Expert Knowledge: Work with domain experts to create specific guidance for each pattern

3. Define Appropriate Side Effects: Determine when tools, integrations, or system modifications are needed

4. Craft Flexible Trigger Patterns: Design triggers that range from associative tags to specific mentions based on needs

   • Can use broad conceptual triggers (e.g., "exercise", "motivation", "recovery")

   • Can use highly specific triggers (e.g., mentioning particular workout types or training equipment)

   • Remember triggers are for relative ranking, not exact matching

5. Design Variable Instruction Specificity: Develop instructions that range from open-ended to highly structured

   • Can provide general guidance for areas requiring flexibility

   • Can include precise step-by-step protocols for regulated or sensitive areas

6. Prioritize and Layer Behaviors: Establish clear priority relationships between different behaviors

7. Test with Real Scenarios: Validate behaviors against real user conversations

8. Monitor and Refine: Continuously analyze effectiveness and update behaviors based on outcomes

Business Value

Dynamic Behavior with side effects creates significant value for enterprises:

1. Reduced Escalations: Intelligent handling of complex scenarios without human intervention

2. Improved Satisfaction: More natural, adaptive conversations that address user needs

3. Consistent Quality: Maintaining high standards across all interactions

4. Scalable Expertise: Embedding domain knowledge that adapts to individual situations

5. Risk Mitigation: Built-in safeguards for handling sensitive or regulated topics

By implementing Dynamic Behavior with side effects, organizations can create agent experiences that combine the reliability of structured protocols with the adaptability and nuance of human experts, while seamlessly integrating with enterprise systems to provide enhanced functionality beyond conversation.