andy-stack_vaultkeeper-ai/Types/OrchestrationResult.ts

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import type { ExecutionStep } from "./ExecutionStep";
refactor: implement multi-agent orchestration architecture Restructure the AI workflow from a single-agent model to a specialized multi-agent system with distinct roles: - Add AgentType enum (Main, Orchestration, Planning, Execution) to define agent specializations - Replace AIControllerService and AIFunctionService with modular agent classes in Services/AIServices/: - MainAgent: Handles user interaction and delegates to orchestration - OrchestrationAgent: Coordinates plan execution and step-by-step workflow - PlanningAgent: Creates and revises execution plans - ExecutionAgent: Executes individual plan steps - AIController: Base class providing common agent loop functionality - Create specialized prompts (OrchestrationPrompt, ExecutionPrompt) for agent-specific behavior - Add OrchestrationResult type to communicate workflow control decisions (continue, abort, replan) - Introduce agent-specific function scoping: - ExecuteWorkflow for main agent - CompleteTask for execution agent - CompleteStep/Replan/CancelPlan for orchestration agent - SubmitPlan/AskUserQuestionPlanning for planning agent - Update BaseAIClass to use agentType property instead of isPlanningAgent boolean for model selection - Simplify ExecutionPlan and ExecutionStep types by removing execution state tracking (moved to agent coordination) - Remove PlanningEnabledAppendix and ExecutionStatus enum (superseded by agent architecture) - Add comprehensive integration tests for agent workflows This architecture provides better separation of concerns, clearer agent responsibilities, and more robust plan execution with explicit orchestration control flow.
2026-01-27 20:29:20 +00:00
type OrchestrationResultInit = {
continue?: boolean;
continueContext?: string;
refactor: implement multi-agent orchestration architecture Restructure the AI workflow from a single-agent model to a specialized multi-agent system with distinct roles: - Add AgentType enum (Main, Orchestration, Planning, Execution) to define agent specializations - Replace AIControllerService and AIFunctionService with modular agent classes in Services/AIServices/: - MainAgent: Handles user interaction and delegates to orchestration - OrchestrationAgent: Coordinates plan execution and step-by-step workflow - PlanningAgent: Creates and revises execution plans - ExecutionAgent: Executes individual plan steps - AIController: Base class providing common agent loop functionality - Create specialized prompts (OrchestrationPrompt, ExecutionPrompt) for agent-specific behavior - Add OrchestrationResult type to communicate workflow control decisions (continue, abort, replan) - Introduce agent-specific function scoping: - ExecuteWorkflow for main agent - CompleteTask for execution agent - CompleteStep/Replan/CancelPlan for orchestration agent - SubmitPlan/AskUserQuestionPlanning for planning agent - Update BaseAIClass to use agentType property instead of isPlanningAgent boolean for model selection - Simplify ExecutionPlan and ExecutionStep types by removing execution state tracking (moved to agent coordination) - Remove PlanningEnabledAppendix and ExecutionStatus enum (superseded by agent architecture) - Add comprehensive integration tests for agent workflows This architecture provides better separation of concerns, clearer agent responsibilities, and more robust plan execution with explicit orchestration control flow.
2026-01-27 20:29:20 +00:00
abort?: boolean;
abortContext?: string;
complete?: boolean;
skipStep?: boolean;
skipReason?: string;
reviseStep?: boolean;
revisedDescription?: string;
revisedInstruction?: string;
revisedContext?: string;
revisePlan?: boolean;
revisedSteps?: ExecutionStep[];
refactor: implement multi-agent orchestration architecture Restructure the AI workflow from a single-agent model to a specialized multi-agent system with distinct roles: - Add AgentType enum (Main, Orchestration, Planning, Execution) to define agent specializations - Replace AIControllerService and AIFunctionService with modular agent classes in Services/AIServices/: - MainAgent: Handles user interaction and delegates to orchestration - OrchestrationAgent: Coordinates plan execution and step-by-step workflow - PlanningAgent: Creates and revises execution plans - ExecutionAgent: Executes individual plan steps - AIController: Base class providing common agent loop functionality - Create specialized prompts (OrchestrationPrompt, ExecutionPrompt) for agent-specific behavior - Add OrchestrationResult type to communicate workflow control decisions (continue, abort, replan) - Introduce agent-specific function scoping: - ExecuteWorkflow for main agent - CompleteTask for execution agent - CompleteStep/Replan/CancelPlan for orchestration agent - SubmitPlan/AskUserQuestionPlanning for planning agent - Update BaseAIClass to use agentType property instead of isPlanningAgent boolean for model selection - Simplify ExecutionPlan and ExecutionStep types by removing execution state tracking (moved to agent coordination) - Remove PlanningEnabledAppendix and ExecutionStatus enum (superseded by agent architecture) - Add comprehensive integration tests for agent workflows This architecture provides better separation of concerns, clearer agent responsibilities, and more robust plan execution with explicit orchestration control flow.
2026-01-27 20:29:20 +00:00
};
export class OrchestrationResult {
public continue: boolean;
public continueContext: string;
refactor: implement multi-agent orchestration architecture Restructure the AI workflow from a single-agent model to a specialized multi-agent system with distinct roles: - Add AgentType enum (Main, Orchestration, Planning, Execution) to define agent specializations - Replace AIControllerService and AIFunctionService with modular agent classes in Services/AIServices/: - MainAgent: Handles user interaction and delegates to orchestration - OrchestrationAgent: Coordinates plan execution and step-by-step workflow - PlanningAgent: Creates and revises execution plans - ExecutionAgent: Executes individual plan steps - AIController: Base class providing common agent loop functionality - Create specialized prompts (OrchestrationPrompt, ExecutionPrompt) for agent-specific behavior - Add OrchestrationResult type to communicate workflow control decisions (continue, abort, replan) - Introduce agent-specific function scoping: - ExecuteWorkflow for main agent - CompleteTask for execution agent - CompleteStep/Replan/CancelPlan for orchestration agent - SubmitPlan/AskUserQuestionPlanning for planning agent - Update BaseAIClass to use agentType property instead of isPlanningAgent boolean for model selection - Simplify ExecutionPlan and ExecutionStep types by removing execution state tracking (moved to agent coordination) - Remove PlanningEnabledAppendix and ExecutionStatus enum (superseded by agent architecture) - Add comprehensive integration tests for agent workflows This architecture provides better separation of concerns, clearer agent responsibilities, and more robust plan execution with explicit orchestration control flow.
2026-01-27 20:29:20 +00:00
public abort: boolean;
public abortContext: string;
public complete: boolean;
public skipStep: boolean;
public skipReason: string;
public reviseStep: boolean;
public revisedDescription: string | undefined;
public revisedInstruction: string | undefined;
public revisedContext: string | undefined;
public revisePlan: boolean;
public revisedSteps: ExecutionStep[];
refactor: implement multi-agent orchestration architecture Restructure the AI workflow from a single-agent model to a specialized multi-agent system with distinct roles: - Add AgentType enum (Main, Orchestration, Planning, Execution) to define agent specializations - Replace AIControllerService and AIFunctionService with modular agent classes in Services/AIServices/: - MainAgent: Handles user interaction and delegates to orchestration - OrchestrationAgent: Coordinates plan execution and step-by-step workflow - PlanningAgent: Creates and revises execution plans - ExecutionAgent: Executes individual plan steps - AIController: Base class providing common agent loop functionality - Create specialized prompts (OrchestrationPrompt, ExecutionPrompt) for agent-specific behavior - Add OrchestrationResult type to communicate workflow control decisions (continue, abort, replan) - Introduce agent-specific function scoping: - ExecuteWorkflow for main agent - CompleteTask for execution agent - CompleteStep/Replan/CancelPlan for orchestration agent - SubmitPlan/AskUserQuestionPlanning for planning agent - Update BaseAIClass to use agentType property instead of isPlanningAgent boolean for model selection - Simplify ExecutionPlan and ExecutionStep types by removing execution state tracking (moved to agent coordination) - Remove PlanningEnabledAppendix and ExecutionStatus enum (superseded by agent architecture) - Add comprehensive integration tests for agent workflows This architecture provides better separation of concerns, clearer agent responsibilities, and more robust plan execution with explicit orchestration control flow.
2026-01-27 20:29:20 +00:00
constructor(init: OrchestrationResultInit) {
this.continue = init.continue ?? false;
this.continueContext = init.continueContext ?? "";
refactor: implement multi-agent orchestration architecture Restructure the AI workflow from a single-agent model to a specialized multi-agent system with distinct roles: - Add AgentType enum (Main, Orchestration, Planning, Execution) to define agent specializations - Replace AIControllerService and AIFunctionService with modular agent classes in Services/AIServices/: - MainAgent: Handles user interaction and delegates to orchestration - OrchestrationAgent: Coordinates plan execution and step-by-step workflow - PlanningAgent: Creates and revises execution plans - ExecutionAgent: Executes individual plan steps - AIController: Base class providing common agent loop functionality - Create specialized prompts (OrchestrationPrompt, ExecutionPrompt) for agent-specific behavior - Add OrchestrationResult type to communicate workflow control decisions (continue, abort, replan) - Introduce agent-specific function scoping: - ExecuteWorkflow for main agent - CompleteTask for execution agent - CompleteStep/Replan/CancelPlan for orchestration agent - SubmitPlan/AskUserQuestionPlanning for planning agent - Update BaseAIClass to use agentType property instead of isPlanningAgent boolean for model selection - Simplify ExecutionPlan and ExecutionStep types by removing execution state tracking (moved to agent coordination) - Remove PlanningEnabledAppendix and ExecutionStatus enum (superseded by agent architecture) - Add comprehensive integration tests for agent workflows This architecture provides better separation of concerns, clearer agent responsibilities, and more robust plan execution with explicit orchestration control flow.
2026-01-27 20:29:20 +00:00
this.abort = init.abort ?? false;
this.abortContext = init.abortContext ?? "";
this.complete = init.complete ?? false;
this.skipStep = init.skipStep ?? false;
this.skipReason = init.skipReason ?? "";
this.reviseStep = init.reviseStep ?? false;
this.revisedInstruction = init.revisedInstruction;
this.revisedContext = init.revisedContext;
this.revisePlan = init.revisePlan ?? false;
this.revisedSteps = init.revisedSteps ?? [];
refactor: implement multi-agent orchestration architecture Restructure the AI workflow from a single-agent model to a specialized multi-agent system with distinct roles: - Add AgentType enum (Main, Orchestration, Planning, Execution) to define agent specializations - Replace AIControllerService and AIFunctionService with modular agent classes in Services/AIServices/: - MainAgent: Handles user interaction and delegates to orchestration - OrchestrationAgent: Coordinates plan execution and step-by-step workflow - PlanningAgent: Creates and revises execution plans - ExecutionAgent: Executes individual plan steps - AIController: Base class providing common agent loop functionality - Create specialized prompts (OrchestrationPrompt, ExecutionPrompt) for agent-specific behavior - Add OrchestrationResult type to communicate workflow control decisions (continue, abort, replan) - Introduce agent-specific function scoping: - ExecuteWorkflow for main agent - CompleteTask for execution agent - CompleteStep/Replan/CancelPlan for orchestration agent - SubmitPlan/AskUserQuestionPlanning for planning agent - Update BaseAIClass to use agentType property instead of isPlanningAgent boolean for model selection - Simplify ExecutionPlan and ExecutionStep types by removing execution state tracking (moved to agent coordination) - Remove PlanningEnabledAppendix and ExecutionStatus enum (superseded by agent architecture) - Add comprehensive integration tests for agent workflows This architecture provides better separation of concerns, clearer agent responsibilities, and more robust plan execution with explicit orchestration control flow.
2026-01-27 20:29:20 +00:00
}
}