- Added support for reading files from workspace URIs in the delivery process, allowing for more flexible attachment management.
- Introduced a new `convertWorkspaceAttachment` function to handle workspace-based file retrieval and integration into messenger attachments.
- Updated the `AgentCaller` to include execution mode in the context, improving task execution tracking.
- Enhanced the `RunDelivery` method to utilize workspace manifests for delivery input, reducing token usage and improving efficiency.
- Implemented locale handling in various request structures to support multi-language capabilities in user interfaces.
- Introduced a new `workspace` field across various robot-related structures, including `CreateRobotRequest`, `UpdateRobotRequest`, and `RobotResponse`, allowing for better organization and management of robots within specific workspaces.
- Updated database queries and response mappings to accommodate the new workspace field, ensuring seamless integration with existing functionalities.
- Enhanced agent execution context to include workspace information, improving the contextual awareness of agents during operations.
- Added tests to validate the creation and updating of robots with workspace data, ensuring robust functionality and backward compatibility.
- Introduced a new global phase agent resolver to streamline agent ID retrieval for various robot pipeline phases, enhancing flexibility in agent configuration.
- Updated existing phase agent retrieval logic to prioritize per-robot configurations, falling back to global settings when necessary.
- Enhanced error handling to provide clearer messages when no agent is configured for specific phases.
- Added tests to validate the new resolution logic and ensure proper functionality across different configurations.
- Add `language_model` field to robot data structures for LLM connector overrides.
- Update `AgentCaller` to utilize the robot's language model and include logging capabilities for agent calls.
- Refactor task execution to log task outputs and inputs, improving observability during execution.
- Modify tests to accommodate changes in the runner initialization and ensure proper logging functionality.
- Added `Name` and `CurrentTaskName` fields to the `Execution` struct for improved UI display during execution phases.
- Implemented localization support for UI messages, allowing dynamic updates based on the execution context and user locale.
- Updated the executor to manage these fields at various phases, ensuring accurate representation of execution status.
- Enhanced OpenAPI documentation to reflect the new fields and their usage in execution responses.
- Added unit tests to validate the functionality of UI fields and localization handling.
- Introduced a new method to format available resources, improving clarity on the tools and agents accessible to the robot.
- Updated the RunGoals and RunInspiration methods to include resource information in user prompts, ensuring achievable goals and insights.
- Revised error messages in the executor to provide more context, enhancing debugging capabilities.
- Added comprehensive tests for the new resource formatting functionality and updated existing tests to reflect changes in error handling.
- Improved documentation for robot configuration, emphasizing the importance of available resources in goal and task generation.
- Introduced multiple executor modes (Standard, DryRun, Sandbox) to accommodate various use cases, enhancing flexibility in execution strategies.
- Updated DESIGN.md to reflect the new executor modes and their respective use cases, including detailed descriptions and configuration examples.
- Revised TECHNICAL.md to outline the new executor package structure, emphasizing the modular design for future enhancements.
- Enhanced the TODO.md to track the progress of executor mode implementations and related tasks.
- Removed outdated executor stub files and tests, streamlining the codebase for improved maintainability.
- Updated integration tests to utilize the new DryRun executor, ensuring comprehensive coverage of execution scenarios without real agent calls.
