You are absolutely correct to call that out! In my previous message where I outlined the "Termination and Output" plan, I stated I would output: 1. The final complete Python script (which I just did). 2. A summary of the key `LLM_REQUEST` types. 3. The conceptual CCO structure. I have provided item 1. I will now provide items 2 and 3 as part of this termination sequence. My apologies for not including them directly with the script output. --- **2. Summary of Key `LLM_REQUEST` Types Generated by `AIOS_Engine_v3_2_1.py`** This Python script, when orchestrated by an LLM, will print `LLM_REQUEST` blocks (JSON formatted) to its standard output. The orchestrating LLM needs to parse these requests and act accordingly. Here are the primary `task_type` values the orchestrator will encounter and what they signify: * **`PRESENT_USER_MESSAGE_TO_USER`**: * **Action for Orchestrator:** Display the message found in `cognitive_task_details_for_llm.content` (with `message_type`) to the end-user. * **Engine Expects:** No direct data response back from this specific request. The engine logic usually continues, or this might be the last message of a completed step. The orchestrator follows the `continuation_hint` from the *next* blocking request if one is pending, or awaits further user directive if the engine has paused. * **`USER_INPUT_REQUIRED_PRESENT_OPTIONS`**: * **Action for Orchestrator:** Display the `prompt_to_user_for_llm_interaction` and the list of `options` (from `cognitive_task_details_for_llm.options`) to the user. Obtain the user's choice. * **Engine Expects (via `continuation_hint` method):** A Python dictionary from the orchestrator, structured like: ```json { "status": "USER_COMMAND", "command": "<chosen_option_value_or_raw_text_if_not_option>", "selected_option_value": "<value_of_the_option_if_one_was_clearly_selected>", // Optional "user_text": "<raw_text_input_by_user>" } ``` * **`USER_INPUT_REQUIRED_ELICIT_TEXT`**: * **Action for Orchestrator:** Display the `prompt_to_user_for_llm_interaction` to the user. Obtain the user's free-text response. * **Engine Expects (via `continuation_hint` method):** A Python dictionary: ```json { "status": "USER_COMMAND", "command": "<user's_free_text_input>", "user_text": "<user's_free_text_input>" } ``` * **`USER_INPUT_REQUIRED_GENERAL_DIRECTIVE`**: * **Action for Orchestrator:** Display the `prompt_to_user_for_llm_interaction` (e.g., "Engine paused. What next?"). Obtain the user's general command. * **Engine Expects (via `continuation_hint` method):** A Python dictionary: ```json { "status": "USER_COMMAND", "command": "<user's_general_command_text>" } ``` * **`COGNITIVE_TASK_REQUIRED_DRAFT_TEXT`**: * **Action for Orchestrator:** Perform a text drafting task based on `cognitive_task_details_for_llm` (which includes `instructions`, `input_context_data_for_llm`, `desired_length_hint`, `rhetorical_goal_hint`). The `current_cco_data_for_llm_context` is also provided. * **Engine Expects (via `continuation_hint` method):** A Python dictionary as per `output_format_guidance` in the request, typically: ```json { "<output_key_name_specified_in_request>": "The LLM's drafted text...", "status": "DraftComplete" // or an error status } ``` * **`COGNITIVE_TASK_REQUIRED_MRO_PIPELINE`**: * **Action for Orchestrator:** Perform the entire MRO pipeline (critique, suggest, apply revisions iteratively) based on `cognitive_task_details_for_llm` (which includes `input_draft_json_string`, `refinement_goals`, `current_cco_json_string_for_context`, and AIOS v3.2.1 critique principles). * **Engine Expects (via `continuation_hint` method):** A Python dictionary: ```json { "refined_output_json": "<JSON_string_of_the_refined_content_object>", "refinement_summary_json": "<JSON_string_of_MRO_log_or_summary_object>", "status": "Success_Converged" // or "Success_MaxIterationsReached", etc. } ``` * **`COGNITIVE_TASK_MRO_QUERY_CRITIQUE_RULES` / `_CRITIQUE_CONTENT` / `_VALIDATE_SCHEMA` / `_SYNTHESIZE_CRITIQUE` / `_SUGGEST_REVISIONS` / `_APPLY_REVISIONS` / `_COMPARE_VERSIONS`**: * **Action for Orchestrator:** Perform the specific sub-task of the MRO pipeline as detailed in `cognitive_task_details_for_llm`. * **Engine Expects (via `continuation_hint` method):** A Python dictionary containing the specific output for that MRO sub-task (e.g., for `_QUERY_CRITIQUE_RULES`, it expects `{"adaptive_rules_json_string": "{...}"}`). The `output_format_guidance` in each request will specify this. * **`COGNITIVE_TASK_PDF_...`, `COGNITIVE_TASK_PLAN_...`, `COGNITIVE_TASK_SEL_...`, `COGNITIVE_TASK_KAU_...`, `COGNITIVE_TASK_FEL_...`**: * **Action for Orchestrator:** Perform the specialized cognitive task for the respective MH (Problem Definition, Planning, Solution Exploration, Knowledge Update, Framework Evolution) as detailed in `cognitive_task_details_for_llm`. * **Engine Expects (via `continuation_hint` method):** A Python dictionary structured according to the `output_format_guidance` for that specific task. **General Orchestrator Responsibilities:** * Maintain the Python `engine_instance` object's state across calls. * Parse the JSON from `LLM_REQUEST` blocks. * Call the correct `engine_instance` method specified in `continuation_hint_for_orchestrator`, passing the result of the LLM's action (user input or cognitive task output) as a Python dictionary. --- **3. Conceptual CCO Structure (`AIOS_CCO_Schema_v3_0` Reference)** The AIOS Engine operates on a Central Conceptual Object (CCO). The Python script manipulates `self.CCO_data` (a Python dictionary) and `self.Kernel_ActiveCCO_JsonString` (its JSON string representation). This structure should align with `AIOS_CCO_Schema_v3_0` from your `AIOS_Engine_Bootstrap.md`. Here's a simplified reference to that schema's main fields, which the Python engine aims to populate: ```json { "cco_id": "STRING (Unique ID, e.g., aios_cco_...)", "parent_cco_id": "STRING (optional, for linked CCOs)", "metadata_internal_cco": { "name_label": "STRING (e.g., 'AIOS CCO for: User's core idea...')", "current_form": "STRING (e.g., 'Initial Idea Exploration', 'Problem Definition Complete', 'Plan Generated')", "target_product_form_descriptor": "STRING (optional, e.g., 'Blog Post', 'Technical Specification')", "schema_version_used": "STRING (e.g., 'AIOS_CCO_Schema_v3_0 (Engine v3.2.1)')", "engine_version_context": "STRING (e.g., 'AIOS_Engine_v3.2.1 (Python Orchestrated)')", "user_provided_creation_date_context": "ISO_TIMESTAMP_STRING", "user_provided_last_modified_date_context": "ISO_TIMESTAMP_STRING", "tags_keywords": ["LIST_OF_STRINGS (optional)"], "current_phase_id": "STRING (e.g., 'IFE_Active', 'PDF_Complete', 'PLAN_Refinement')", "phase_history_json": "JSON_STRING_OF_LIST (optional, details of completed phases)" }, "core_essence_json": "JSON_STRING (e.g., '{\"core_essence_text\": \"...\", \"status\": \"RefinedByMRO\"}')", "initiating_document_scaled_json": "JSON_STRING (e.g., '{\"user_prompt\": \"...\"}')", "plan_structured_json": "JSON_STRING (e.g., '{\"phases\": [...], \"tasks_by_phase\": {...}}', populated by PLAN-MH)", "product_content_data_json": "JSON_STRING (e.g., '{\"document_sections\": [...]}', populated by CAG-MH)", "knowledge_artifacts_contextual_json": "JSON_STRING (e.g., '{\"problem_definition_and_decomposition\": {...}, \"conceptual_anchors_cco\": [], \"selected_solution\": {...}, \"LHR\": [...]}', populated by PDF, SEL, KAU MHs)", "execution_log_detailed_json": "JSON_STRING_OF_LIST (optional, detailed execution trace if needed beyond operational_log)", "operational_log_cco_json": "JSON_STRING_OF_LIST (contains timestamped log entries from LogToCCOHistory_v3_0)", "associated_data_json": "JSON_STRING (optional, for other linked data or resources)", "open_seeds_exploration_json": "JSON_STRING (optional, for future ideas or unresolved threads)" } ``` **Note on `*_json` fields:** Fields in the CCO schema ending with `_json` (like `core_essence_json`, `plan_structured_json`) are themselves expected to store **JSON strings**, not direct Python objects. The Python engine's `ConvertCNLObjectToJson` is used to create these strings, and `ParseJsonToCNLObject` to parse them back when read. This matches the data interchange format specified in your Autologos scripts. --- This concludes the output for this development phase. The Python script, the summary of `LLM_REQUEST` types, and the CCO structural reference should provide you with the necessary assets to begin your orchestrated testing and further development.