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Agent Orchestration

The agent orchestration layer coordinates the activities of our three specialized agents, managing conversations, context sharing, and handoffs to create a seamless experience. This system ensures that the right agent is engaged at the right time while maintaining continuity across interactions.

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Orchestration Architecture

Agent Orchestration Architecture Diagram The orchestration layer sits between user interfaces and individual agents, managing the flow of information and coordination of activities:

Request Router

Directs incoming queries to the most appropriate agent based on content, context, and user history

Context Manager

Maintains conversation history and user context across agent interactions

Handoff Controller

Manages smooth transitions between agents when specialized capabilities are needed

Response Coordinator

Assembles cohesive responses when multiple agents contribute insights

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Core Capabilities

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Request Routing

The orchestration system determines which agent should handle each request based on:
  • Content analysis of the current query
  • Conversation history and ongoing context
  • User preferences and past interaction patterns
  • Specialized knowledge requirements
  • Current agent availability and load

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Context Management

The context management system:
  • Maintains a unified view of conversation history across agents
  • Preserves important user information and preferences
  • Identifies relevant context for each interaction
  • Manages short-term and long-term memory
  • Ensures privacy and security of sensitive information
1

Context Capture

Information from user interactions is collected, including queries, responses, and interaction patterns.
2

Context Processing

Key elements are extracted and organized into structured context representation.
3

Memory Management

Short-term conversation context and long-term user preferences are maintained separately.
4

Context Distribution

Relevant context is provided to agents based on their current task and authorization level.
5

Context Refreshing

Outdated or irrelevant context is pruned to maintain focus and efficiency.

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Agent Handoffs

When conversations require capabilities from multiple agents, our handoff system ensures smooth transitions:
Automatic identification of scenarios where a different agent’s capabilities would be beneficial, based on query content, user needs, or explicit requests.
Relevant conversation history, user preferences, and current task state are packaged and transferred to the receiving agent.
Clear communication to the user about the handoff, setting expectations about the transition and new capabilities available.
Preservation of conversation flow and relationship through consistent voice adaptation and reference to previous interactions.

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Inter-Agent Communication Protocols

Agents communicate through standardized protocols that enable effective collaboration:

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Sequential Collaboration

User → MOOD MNKY (initial interaction)
     → CODE MNKY (technical question)
     → MOOD MNKY (conversation continuation)
In this pattern, the orchestration layer:
  • Identifies when a technical question requires CODE MNKY’s expertise
  • Manages the transition with appropriate context
  • Returns to MOOD MNKY when the technical question is resolved
  • Maintains relationship continuity throughout

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Parallel Consultation

User → MOOD MNKY (primary interaction)
   ↑
   ← CODE MNKY (background technical input)
   ↑
   ← SAGE MNKY (background content input)
In this pattern, the orchestration layer:
  • Maintains MOOD MNKY as the primary interface
  • Consults other agents in the background for specialized input
  • Integrates insights from multiple agents into a cohesive response
  • Preserves a consistent voice and experience

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Expert Delegation

User → MOOD MNKY (request received)
   ↓
   → SAGE MNKY (learning need identified)
   ↓
   → User (direct SAGE MNKY response)
In this pattern, the orchestration layer:
  • Identifies when full delegation to a specialist agent is appropriate
  • Manages a complete handoff with full context transfer
  • Maintains awareness of the conversation for future reference
  • Enables return to the original agent when appropriate

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Integration Points

The orchestration layer integrates with several system components:
  • User Interfaces: Chat widgets, embedded assistants, and other interaction channels
  • Knowledge Base: Shared information repository for all agents
  • User Profiles: Personalization data and preferences
  • Analytics System: Interaction tracking and performance monitoring
  • Agent Deployment: Configuration and scaling of agent instances

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Implementation Technologies

Our orchestration implementation leverages:
  • LangChain for agent chaining and orchestration flows
  • Redis for fast, distributed context management
  • Node.js for the core orchestration service
  • WebSockets for real-time communication
  • Custom prompt engineering for effective inter-agent collaboration

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Future Enhancements

Planned improvements to the orchestration system include:
  • More sophisticated handoff detection using intent recognition
  • Enhanced multi-agent collaborative problem solving
  • Streamlined context summarization for more efficient transfers
  • Improved personalization of agent selection based on user preferences
  • Advanced analytics to optimize orchestration decisions
When implementing integrations with the agent system, always use the orchestration layer rather than connecting directly to individual agents to ensure proper context management and consistent user experience.