feat: Implement SASC Orchestrator CLI for Agent Management#5
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This commit introduces the SASC Orchestrator, a high-level command-line interface for managing the SASC agent workforce. This fulfills the requirements of Workorder 2a, building upon the foundation established in Workorder 1a without modifying the underlying agent system. The SASC Orchestrator (`sasc_orchestrator/main.py`) provides an interactive chat console that acts as the management and creative layer for the entire SASC ecosystem. Key Features: - **Interactive Chat Interface:** A simple, extensible CLI for issuing commands to the AI management layer. - **Command Parsing:** Distinguishes between natural language input and explicit, executable commands prefixed with `!`. - **Integration with `sascctl`:** Uses the `sascctl` tool as a subprocess to control the lifecycle of the workforce agents, ensuring a clean separation of concerns between the management and workforce layers. - **Orchestrator "Thought Cloning":** Maintains its own dedicated log (`orchestrator_thought_log.txt`) to record high-level decisions and actions, providing transparency into the reasoning of the management layer itself. This component serves as the primary user-facing entry point for interacting with and directing the autonomous SASC agent system.
This commit introduces the SASC Orchestrator, a high-level command-line interface for managing the SASC agent workforce. This fulfills the requirements of Workorder 2a, building upon the foundation established in Workorder 1a without modifying the underlying agent system. The SASC Orchestrator (`sasc_orchestrator/main.py`) provides an interactive chat console that acts as the management and creative layer for the entire SASC ecosystem. Key Features: - **Interactive Chat Interface:** A simple, extensible CLI for issuing commands to the AI management layer. - **Command Parsing:** Distinguishes between natural language input and explicit, executable commands prefixed with `!`. - **Integration with `sascctl`:** Uses the `sascctl` tool as a subprocess to control the lifecycle of the workforce agents, ensuring a clean separation of concerns between the management and workforce layers. - **Orchestrator "Thought Cloning":** Maintains its own dedicated log (`orchestrator_thought_log.txt`) to record high-level decisions and actions, providing transparency into the reasoning of the management layer itself. - **Live AI Integration:** Includes integration with Google's Vertex AI for interacting with the Gemma model. - **Mocked Local Model Interface:** Provides a mock interface for a local Qwen-Coder model, preparing the architecture for future MLC LLM integration. This component serves as the primary user-facing entry point for interacting with and directing the autonomous SASC agent system.
This commit enhances the SASC system to be architecturally-aware, simulating a high-fidelity host/guest environment as per the revised requirements of Workorder 3a. This prepares the system for a future transition to a real Cuttlefish virtual environment. Key Enhancements: - **Architecturally-Aware SCM:** The `polyglot_state.yaml` manifest has been updated to include distinct configurations for the `AARCH64_HOST_CONFIG` and the `X86_64_CUTTLEFISH_GUEST_CONFIG`. This provides a complete blueprint of the target architecture. - **Host-Aware Orchestrator:** The `sasc_orchestrator` now loads the `AARCH64_HOST_CONFIG` and logs its "thoughts" with the context of operating on an AArch64 host. - **Guest-Aware Simulated Agent:** The `native_agent_simulator` is now "guest-aware," loading the `X86_64_CUTTLEFISH_GUEST_CONFIG` and logging its "thoughts" as if it were running inside a Cuttlefish x86_64 VM. - **Updated `launch-agent` Command:** The `sascctl launch-agent` command has been updated to correctly parse the new SCM structure and pass the guest configuration to the simulated agent. - **Revised Demonstration Scenario:** A new `run_demo_revised.sh` script provides an end-to-end test of the architecturally-aware system, verifying the "thought logs" of both the host and guest components. This implementation successfully pivots from a simple simulation to a high-fidelity, architecturally-aware model of the target environment, all while working within the constraints of the current non-virtualized execution environment.
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This change introduces the SASC Orchestrator, a high-level command-line interface for managing the SASC agent workforce. This fulfills the requirements of Workorder 2a.
The orchestrator provides an interactive chat console that acts as the management and creative layer for the SASC ecosystem. It can parse user commands, execute the
sascctltool to manage agents, interact with live AI models on Vertex AI, and maintains its own "thought log" for transparency.PR created automatically by Jules for task 9011214904568932602