Okay, proceeding with **Test Phase 4, Step 1 & 2: CAG-MH processes the drafted introductory section and requests MRO refinement.** 1. Orchestrator (Me): Has the drafted introductory section (as constructed in the previous turn). 2. Orchestrator: Constructs `llm_cognitive_result` with this draft. 3. Orchestrator: Calls `engine_instance.run_mh_cag_simplified_draft_step2_process_draft(llm_cognitive_result)`. 4. Script Output (Expected): * CAG-MH logs receipt of the drafted section. * It stores this draft internally (in `_cag_s["draft_section_json_str_for_mro"]`). * The primary `LLM_REQUEST` generated will be from `fn_mro_RefineOutput_Pipeline_v3_0`, asking the orchestrating LLM to refine this drafted section using MRO principles. Let's execute this step. I will re-instantiate the engine and manually restore the necessary state (`self.CCO_data` from the previous step, and `self._ife_s` and `self._cag_s` as appropriate for this point in the CAG MH). ```tool_code # Test Phase 4, Step 1 & 2: CAG-MH processes drafted section, requests MRO refinement. # Re-instantiating engine and restoring necessary state. import json import uuid import datetime import time # --- AIOS_Engine_v3_2_1 Class Definition (Copied) --- class AIOS_Engine_v3_2_1: def __init__(self): self.log_history = [] self.engine_version_full = "AIOS_Engine_v3.2.1 (Python Orchestrated)" self.engine_version_short = "3.2.1" self.current_context_mh = "System" self.Kernel_ActiveCCO_JsonString = None self.Kernel_CurrentMH_ID = None self.Kernel_MH_Inputs_JsonString = None self.CCO_data = None self._ife_s = {} self._cag_s = {} self.aios_log("System", f"{self.engine_version_full} instance created (fresh for this step).") def _get_timestamp(self): return datetime.datetime.now(datetime.timezone.utc).isoformat() def aios_log(self, context, message): timestamp = self._get_timestamp() full_log = f"{timestamp} - AIOS_LOG ({context} v{self.engine_version_short}): {message}" print(full_log) self.log_history.append(full_log) def _create_llm_request(self, task_type, prompt_to_user=None, cognitive_task_details=None, expected_input_description=None, continuation_hint=None, cco_data_for_context=None): request = {"request_timestamp": self._get_timestamp(), "engine_version_context": self.engine_version_full, "current_mh_context": self.current_context_mh, "task_type": task_type} if prompt_to_user: request["prompt_to_user_for_llm_interaction"] = prompt_to_user if cognitive_task_details: request["cognitive_task_details_for_llm"] = cognitive_task_details if expected_input_description: request["expected_input_description_for_continuation"] = expected_input_description if continuation_hint: request["continuation_hint_for_orchestrator"] = continuation_hint if cco_data_for_context: if isinstance(cco_data_for_context, dict): request["current_cco_data_for_llm_context"] = cco_data_for_context elif isinstance(cco_data_for_context, str): try: request["current_cco_data_for_llm_context"] = json.loads(cco_data_for_context) except: request["current_cco_data_for_llm_context"] = {"unparsed_cco_string_warning": "Could not parse CCO string for LLM context.", "raw_cco_string_preview": cco_data_for_context[:200] + ("..." if len(cco_data_for_context) > 200 else "")} print("\n---BEGIN_LLM_REQUEST---"); print(json.dumps(request, indent=2)); print("---END_LLM_REQUEST---") return {"status": "AWAITING_LLM_ORCHESTRATION", "request_details": request, "current_engine_state_snapshot": self._get_engine_state_snapshot()} def _get_engine_state_snapshot(self): return {"Kernel_CurrentMH_ID": self.Kernel_CurrentMH_ID, "Kernel_MH_Inputs_JsonString": self.Kernel_MH_Inputs_JsonString, "Kernel_ActiveCCO_JsonString_first_100_chars": (self.Kernel_ActiveCCO_JsonString[:100] + "...") if self.Kernel_ActiveCCO_JsonString else None, "CCO_data_type_and_id": f"{str(type(self.CCO_data))} (ID: {self.CCO_data.get('cco_id', 'N/A') if isinstance(self.CCO_data, dict) else 'N/A'})", "current_context_mh_for_logging": self.current_context_mh} def PresentUserMessage_v3_0(self, message_type, message_content_obj): self.aios_log(self.current_context_mh, f"PresentUserMessage_v3_0 (Type: {message_type}): {message_content_obj}") return self._create_llm_request(task_type="PRESENT_USER_MESSAGE_TO_USER", cognitive_task_details={"message_type": message_type, "content": message_content_obj, "requesting_mh": self.current_context_mh}, continuation_hint="LLM presents message. Engine logic flow typically continues unless this message is part of an input request sequence.") def ParseJsonToCNLObject(self, json_string_input): if json_string_input is None or not isinstance(json_string_input, str) or json_string_input.strip() == "": self.aios_log(self.current_context_mh, "ParseJsonToCNLObject: Input JSON string is null or empty. Returning None."); return None try: return json.loads(json_string_input) except json.JSONDecodeError as e: self.aios_log(self.current_context_mh, f"ERROR in ParseJsonToCNLObject: {str(e)}. Input: '{json_string_input}'"); raise ValueError(f"AIOS_JSONParsingError (v{self.engine_version_short}): {str(e)} on input: {json_string_input}") def ConvertCNLObjectToJson(self, cnl_object_input): if cnl_object_input is None: return "null" try: return json.dumps(cnl_object_input) except TypeError as e: self.aios_log(self.current_context_mh, f"ERROR in ConvertCNLObjectToJson: {str(e)}"); raise ValueError(f"AIOS_JSONFormattingError (v{self.engine_version_short}): {str(e)}") def LogToCCOHistory_v3_0(self, cco_data_dict, log_entry_type, message, associated_data_cnl_obj=None): self.aios_log(self.current_context_mh, f"LogToCCOHistory_v3_0: Type='{log_entry_type}', Msg='{message}'") if not isinstance(cco_data_dict, dict): self.aios_log(self.current_context_mh, "LogToCCOHistory_v3_0: CCO data is not a dict. Creating basic CCO for logging."); cco_data_dict = {"operational_log_cco_json": "[]"} op_log_list_str = cco_data_dict.get("operational_log_cco_json", "[]"); op_log_list = self.ParseJsonToCNLObject(op_log_list_str) if not isinstance(op_log_list, list): op_log_list = [] new_log_entry = {"timestamp": self._get_timestamp(), "log_entry_type": log_entry_type, "log_message": message} if associated_data_cnl_obj is not None: new_log_entry["associated_data_json"] = self.ConvertCNLObjectToJson(associated_data_cnl_obj) op_log_list.append(new_log_entry); cco_data_dict["operational_log_cco_json"] = self.ConvertCNLObjectToJson(op_log_list) self.Kernel_ActiveCCO_JsonString = self.ConvertCNLObjectToJson(cco_data_dict); self.CCO_data = cco_data_dict return cco_data_dict def fn_interaction_present_options_v3(self, prompt_message_to_user, options_list_cnl): self.aios_log(self.current_context_mh, "fn_interaction_present_options_v3: Requesting LLM to get user choice from options.") return self._create_llm_request(task_type="USER_INPUT_REQUIRED_PRESENT_OPTIONS", prompt_to_user=prompt_message_to_user, cognitive_task_details={"options": options_list_cnl, "input_type": "option_selection"}, expected_input_description="JSON object from LLM: {'status': 'USER_COMMAND', 'command': <chosen_option_value_or_text>, 'selected_option_value': <value_if_option>, 'user_text': <raw_text>}. See AIOS Kernel interaction model.", continuation_hint="engine.kernel_process_initial_choice_result(llm_interaction_result)") def fn_interaction_elicit_user_input_v3(self, prompt_message_to_user): self.aios_log(self.current_context_mh, "fn_interaction_elicit_user_input_v3: Requesting LLM to get user text input.") return self._create_llm_request(task_type="USER_INPUT_REQUIRED_ELICIT_TEXT", prompt_to_user=prompt_message_to_user, cognitive_task_details={"input_type": "free_text"}, expected_input_description="JSON object from LLM: {'status': 'USER_COMMAND', 'command': <user_text>, 'user_text': <user_text>}. See IFE-MH.", continuation_hint="engine.run_mh_ife_step2_process_core_idea(llm_interaction_result_obj)") def fn_utility_generate_unique_id_v3(self, id_prefix): self.aios_log(self.current_context_mh, f"fn_utility_generate_unique_id_v3 (prefix: {id_prefix})"); unique_id = f"{id_prefix}{uuid.uuid4()}"; return {"status": "Generated", "unique_id": unique_id} def fn_content_draft_text_segment_v3(self, instructions, context_obj, desired_length_hint, rhetorical_goal_hint, output_key_name="draft_text"): self.aios_log(self.current_context_mh, f"fn_content_draft_text_segment_v3: Requesting LLM to draft text.") cognitive_details = {"task_name_from_spec": "content_draft_text_segment_v3", "instructions": instructions, "input_context_data_for_llm": context_obj, "desired_length_hint": desired_length_hint, "rhetorical_goal_hint": rhetorical_goal_hint, "output_format_guidance": f"LLM should return a JSON object with a key '{output_key_name}' containing the drafted text string, and a 'status' key (e.g., 'DraftComplete'). Example: {{ '{output_key_name}': 'text...', 'status': 'DraftComplete' }}"} return self._create_llm_request(task_type="COGNITIVE_TASK_REQUIRED_DRAFT_TEXT", cognitive_task_details=cognitive_details, expected_input_description=f"JSON object from LLM. Orchestrator provides as 'llm_cognitive_result'.", continuation_hint="engine.run_mh_ife_step3_process_essence_draft(llm_cognitive_result)", cco_data_for_context=self.CCO_data ) def fn_data_update_cco_section_v3(self, cco_data_dict, section_path, new_content_json_str_to_store): self.aios_log(self.current_context_mh, f"fn_data_update_cco_section_v3 (Path: {section_path})") if not isinstance(cco_data_dict, dict): self.aios_log(self.current_context_mh, "fn_data_update_cco_section_v3: CCO data is not a dict. Update failed."); return cco_data_dict keys = section_path.split('.'); current_level = cco_data_dict try: for i, key in enumerate(keys): if i == len(keys) - 1: current_level[key] = new_content_json_str_to_store else: if key not in current_level or not isinstance(current_level[key], dict): current_level[key] = {} current_level = current_level[key] self.Kernel_ActiveCCO_JsonString = self.ConvertCNLObjectToJson(cco_data_dict); self.CCO_data = cco_data_dict except Exception as e: self.aios_log(self.current_context_mh, f"Error in fn_data_update_cco_section_v3 for path '{section_path}': {e}") return cco_data_dict def fn_mro_RefineOutput_Pipeline_v3_0(self, draft_content_json_str, refinement_goals_obj, cco_context_json_str): self.aios_log(self.current_context_mh, "fn_mro_RefineOutput_Pipeline_v3_0: Requesting LLM to execute MRO pipeline.") cognitive_details = {"task_name_from_spec": "RefineOutput_Pipeline_v3_0 (MRO)", "input_draft_json_string": draft_content_json_str, "refinement_goals": refinement_goals_obj, "current_cco_json_string_for_context": cco_context_json_str, "guidance_for_llm": (f"LLM should simulate the MRO pipeline: critique the draft against goals (including AIOS v{self.engine_version_short} enhancements like Transformative Value, Information Density - see TID_ASO_META_002, 005 in ASO manual if available, else use general best practice), conceptually suggest revisions, and apply them. The final refined output should be returned. "), "output_format_guidance": ("LLM should return a JSON object: {'refined_output_json': <JSON_string_of_refined_content>, 'refinement_summary_json': <JSON_string_of_log_or_summary>, 'status': 'Success_Converged' or 'Success_MaxIterationsReached' or error status}")} return self._create_llm_request(task_type="COGNITIVE_TASK_REQUIRED_MRO_PIPELINE", cognitive_task_details=cognitive_details, expected_input_description="JSON object from LLM. Orchestrator provides as 'llm_cognitive_result'.", continuation_hint="engine.run_mh_ife_step4_finalize_essence(llm_cognitive_result).", cco_data_for_context=self.CCO_data) def fn_interpret_user_directive_for_next_mh_v3(self, user_input_text): self.aios_log(self.current_context_mh, f"fn_interpret_user_directive_for_next_mh_v3 for: '{user_input_text}'"); uit_lower = user_input_text.lower().strip(); next_mh_id = "AWAIT_USER_INPUT"; next_mh_inputs = {}; user_prompt_message = "Command not fully understood. What would you like to do next?" if "new process" == uit_lower or "1" == uit_lower or "1." == uit_lower or (uit_lower.startswith("new") and "process" in uit_lower) : next_mh_id = "IFE-MH"; next_mh_inputs = {}; user_prompt_message = None elif uit_lower in ["terminate aios", "terminate", "exit", "quit", "2", "2."]: next_mh_id = "TERMINATE_AIOS"; user_prompt_message = None result_obj = {"status": "Success" if next_mh_id != "AWAIT_USER_INPUT" else "InterpretationRequiresClarification", "next_mh_id": next_mh_id, "next_mh_inputs_json": self.ConvertCNLObjectToJson(next_mh_inputs)} if user_prompt_message: result_obj["user_prompt_message"] = user_prompt_message return result_obj def start_engine(self): self.current_context_mh = "Kernel"; self.aios_log(self.current_context_mh, f"{self.engine_version_full} - Kernel preparing initial options."); self.PresentUserMessage_v3_0("Status", f"{self.engine_version_full} Initializing. Ready to present options.") return self.kernel_present_initial_options() def kernel_present_initial_options(self): self.current_context_mh = "Kernel"; options_list_cnl = [{"value": "New Process", "label": "1. New Process (Ideate & Draft Writing using IFE-MH)"}, {"value": "Terminate AIOS", "label": "2. Terminate AIOS"}] return self.fn_interaction_present_options_v3(prompt_message_to_user=f"AIOS Engine v{self.engine_version_short} Ready. How would you like to begin?", options_list_cnl=options_list_cnl) def kernel_process_initial_choice_result(self, llm_interaction_result_obj): self.current_context_mh = "Kernel" if not llm_interaction_result_obj or llm_interaction_result_obj.get("status") != "USER_COMMAND": self.aios_log(self.current_context_mh, "Invalid or missing result from initial options. Re-prompting."); self.PresentUserMessage_v3_0("Warning", "Could not process your selection. Please try again."); return self.kernel_present_initial_options() command_value = llm_interaction_result_obj.get("command"); self.aios_log(self.current_context_mh, f"Processing initial command/choice value: '{command_value}'") interp_result = self.fn_interpret_user_directive_for_next_mh_v3(command_value) self.Kernel_CurrentMH_ID = interp_result.get("next_mh_id"); self.Kernel_MH_Inputs_JsonString = interp_result.get("next_mh_inputs_json") if self.Kernel_CurrentMH_ID == "TERMINATE_AIOS": self.PresentUserMessage_v3_0("Status", "AIOS session termination initiated."); return {"status": "TERMINATION_REQUESTED", "final_engine_state": self._get_engine_state_snapshot()} elif self.Kernel_CurrentMH_ID and self.Kernel_CurrentMH_ID != "AWAIT_USER_INPUT": self.aios_log(self.current_context_mh, f"Kernel: Next MH selected: {self.Kernel_CurrentMH_ID}"); return self.kernel_run_current_mh() else: self.PresentUserMessage_v3_0("Warning", interp_result.get("user_prompt_message", f"Unrecognized choice: '{command_value}'. Please select an option.")); return self.kernel_present_initial_options() def kernel_run_current_mh(self): self.current_context_mh = "Kernel" if self.Kernel_CurrentMH_ID == "TERMINATE_AIOS": self.PresentUserMessage_v3_0("Status", "AIOS session terminated by Kernel directive."); return {"status": "TERMINATED", "final_engine_state": self._get_engine_state_snapshot()} elif self.Kernel_CurrentMH_ID == "AWAIT_USER_INPUT" or not self.Kernel_CurrentMH_ID : self.aios_log(self.current_context_mh, "Kernel paused, awaiting general user directive."); return self._create_llm_request(task_type="USER_INPUT_REQUIRED_GENERAL_DIRECTIVE", prompt_to_user="The AIOS Engine is paused. What would you like to do next?", expected_input_description="User's textual command (string), LLM returns {'status':'USER_COMMAND', 'command':'...'}.", continuation_hint="engine.kernel_process_general_user_directive(llm_interaction_result_obj)") self.aios_log(self.current_context_mh, f"Kernel: Preparing to execute MH: {self.Kernel_CurrentMH_ID}"); self.PresentUserMessage_v3_0("Status", f"Executing Meta-Heuristic: {self.Kernel_CurrentMH_ID} (v{self.engine_version_short})") mh_inputs_obj = self.ParseJsonToCNLObject(self.Kernel_MH_Inputs_JsonString) if self.Kernel_CurrentMH_ID == "IFE-MH": return self.run_mh_ife_step1_get_core_idea(mh_inputs_obj) elif self.Kernel_CurrentMH_ID == "CAG-MH_SIMPLIFIED_DRAFT": return self.run_mh_cag_simplified_draft_step1_prepare(mh_inputs_obj) else: self.aios_log(self.current_context_mh, f"Error: MH '{self.Kernel_CurrentMH_ID}' execution path not implemented in this script."); self.PresentUserMessage_v3_0("Error", f"Meta-Heuristic '{self.Kernel_CurrentMH_ID}' is not available in this version of the script."); self.Kernel_CurrentMH_ID = "AWAIT_USER_INPUT"; return self.kernel_run_current_mh() def kernel_process_mh_result(self, mh_that_just_ran_id, mh_result_obj_from_mh_step): self.current_context_mh = "Kernel"; status = mh_result_obj_from_mh_step.get("status"); self.aios_log(self.current_context_mh, f"Processing result from '{mh_that_just_ran_id}'. Status: '{status}'") if not mh_result_obj_from_mh_step or not isinstance(mh_result_obj_from_mh_step, dict): self.PresentUserMessage_v3_0("Error", f"Internal error: Invalid result structure from '{mh_that_just_ran_id}'."); self.Kernel_CurrentMH_ID = "AWAIT_USER_INPUT"; return self.kernel_run_current_mh() if "updated_cco_json" in mh_result_obj_from_mh_step: self.Kernel_ActiveCCO_JsonString = mh_result_obj_from_mh_step["updated_cco_json"]; self.CCO_data = self.ParseJsonToCNLObject(self.Kernel_ActiveCCO_JsonString); self.aios_log(self.current_context_mh, f"CCO (JSON string state) updated from '{mh_that_just_ran_id}'.") elif self.CCO_data is not None : self.Kernel_ActiveCCO_JsonString = self.ConvertCNLObjectToJson(self.CCO_data) if status == "AWAITING_LLM_ORCHESTRATION": self.aios_log(self.current_context_mh, f"'{mh_that_just_ran_id}' is still awaiting LLM orchestration for task: {mh_result_obj_from_mh_step.get('request_details',{}).get('task_type')}"); return mh_result_obj_from_mh_step if mh_that_just_ran_id == "IFE-MH" and status == "IFE_ExplorationComplete_ReadyForNext": self.CCO_data = self.LogToCCOHistory_v3_0(self.CCO_data, "MH_Completion", f"{mh_that_just_ran_id} completed with status: {status}.", mh_result_obj_from_mh_step.get("details_for_log")); self.PresentUserMessage_v3_0("Status", f"IFE-MH exploration successfully completed for CCO ID '{self.CCO_data.get('cco_id') if self.CCO_data else 'N/A'}'") self.Kernel_CurrentMH_ID = "CAG-MH_SIMPLIFIED_DRAFT"; self.Kernel_MH_Inputs_JsonString = self.ConvertCNLObjectToJson({"task_directive": "draft_initial_introduction", "current_cco_json": self.Kernel_ActiveCCO_JsonString}); return self.kernel_run_current_mh() elif mh_that_just_ran_id == "CAG-MH_SIMPLIFIED_DRAFT" and status == "CAG_DraftComplete_UserReview": self.CCO_data = self.LogToCCOHistory_v3_0(self.CCO_data, "MH_Completion", f"{mh_that_just_ran_id} completed with status: {status}.", mh_result_obj_from_mh_step.get("details_for_log")); self.PresentUserMessage_v3_0("Status", f"CAG-MH draft presented for CCO ID '{self.CCO_data.get('cco_id') if self.CCO_data else 'N/A'}'"); self.Kernel_CurrentMH_ID = "AWAIT_USER_INPUT"; return self.kernel_run_current_mh() elif "Error" in status or "Failed" in status: self.CCO_data = self.LogToCCOHistory_v3_0(self.CCO_data or {"operational_log_cco_json":"[]"}, "MH_Error", f"{mh_that_just_ran_id} reported error status: {status}", mh_result_obj_from_mh_step.get("details_for_log")); self.PresentUserMessage_v3_0("Error", f"Meta-Heuristic {mh_that_just_ran_id} encountered an issue: {status}."); self.Kernel_CurrentMH_ID = "AWAIT_USER_INPUT"; return self.kernel_run_current_mh() else: self.CCO_data = self.LogToCCOHistory_v3_0(self.CCO_data or {"operational_log_cco_json":"[]"}, "MH_Event", f"{mh_that_just_ran_id} returned unhandled status: {status}", mh_result_obj_from_mh_step.get("details_for_log")); self.PresentUserMessage_v3_0("Info", f"Process step with {mh_that_just_ran_id} resulted in status: {status}."); self.Kernel_CurrentMH_ID = "AWAIT_USER_INPUT"; return self.kernel_run_current_mh() def kernel_process_general_user_directive(self, llm_interaction_result_obj): self.current_context_mh = "Kernel"; user_directive_text = llm_interaction_result_obj.get("command", ""); self.aios_log(self.current_context_mh, f"Processing general user directive: '{user_directive_text}'") interp_result_obj = self.fn_interpret_user_directive_for_next_mh_v3(user_directive_text) self.Kernel_CurrentMH_ID = interp_result_obj.get("next_mh_id", "AWAIT_USER_INPUT"); self.Kernel_MH_Inputs_JsonString = interp_result_obj.get("next_mh_inputs_json", "{}") if self.Kernel_CurrentMH_ID == "AWAIT_USER_INPUT" and interp_result_obj.get("user_prompt_message"): return self._create_llm_request(task_type="USER_INPUT_REQUIRED_GENERAL_DIRECTIVE", prompt_to_user=interp_result_obj.get("user_prompt_message"), continuation_hint="engine.kernel_process_general_user_directive(llm_interaction_result_obj)" ) return self.kernel_run_current_mh() def run_mh_ife_step1_get_core_idea(self, mh_inputs_obj): self.current_context_mh = "IFE-MH"; self.aios_log(self.current_context_mh, f"IFE-MH (v{self.engine_version_short}) Step 1: Initiated. Getting core idea."); self.PresentUserMessage_v3_0("Status", f"IFE-MH (v{self.engine_version_short}): Starting Idea Formulation & Exploration...") self._ife_s = {"UserPromptText_from_kernel": mh_inputs_obj.get("user_initial_prompt_text") if mh_inputs_obj else None, "CCO_JsonString_from_kernel": mh_inputs_obj.get("existing_cco_json_if_reexploring") if mh_inputs_obj else self.Kernel_ActiveCCO_JsonString} if self._ife_s["CCO_JsonString_from_kernel"]: self.CCO_data = self.ParseJsonToCNLObject(self._ife_s["CCO_JsonString_from_kernel"]) else: self.CCO_data = None if self._ife_s["UserPromptText_from_kernel"]: self.aios_log(self.current_context_mh, "Core idea already provided via Kernel input. Processing directly."); llm_like_result = {"status":"USER_COMMAND", "command":self._ife_s["UserPromptText_from_kernel"], "user_text": self._ife_s["UserPromptText_from_kernel"]}; return self.run_mh_ife_step2_process_core_idea(llm_like_result) else: self.aios_log(self.current_context_mh, "Core idea not provided, requesting LLM to elicit from user."); return self.fn_interaction_elicit_user_input_v3(prompt_message_to_user="What is the core idea or problem you'd like to explore for this new AIOS process?") def run_mh_ife_step2_process_core_idea(self, llm_interaction_result_obj): self.current_context_mh = "IFE-MH"; user_provided_text = llm_interaction_result_obj.get("command") self.aios_log(self.current_context_mh, f"IFE-MH Step 2: Received user's core idea: '{user_provided_text}'") if not user_provided_text: self.PresentUserMessage_v3_0("Error", "IFE-MH: Core idea is essential and was not provided. Cannot proceed."); return {"status": "IFE_Failed_NoCoreIdeaProvided", "updated_cco_json": self.ConvertCNLObjectToJson(self.CCO_data)} self._ife_s["user_provided_core_idea"] = user_provided_text if self.CCO_data is None: self.aios_log(self.current_context_mh, "IFE-MH: No CCO exists, initializing new CCO."); id_result_obj = self.fn_utility_generate_unique_id_v3(id_prefix="aios_cco_"); new_cco_id = id_result_obj.get("unique_id", f"fallback_cco_{uuid.uuid4().hex[:8]}") prompt_summary = self._ife_s["user_provided_core_idea"]; if len(prompt_summary) > 50: prompt_summary = prompt_summary[:47] + "..." self.CCO_data = {"cco_id": new_cco_id, "parent_cco_id": None, "metadata_internal_cco": {"name_label": f"AIOS CCO for: {prompt_summary}", "current_form": "Initial Idea Exploration (IFE)", "schema_version_used": f"AIOS_CCO_Schema_v3_0 (Engine v{self.engine_version_short})", "engine_version_context": self.engine_version_full, "user_provided_creation_date_context": self._get_timestamp(), "tags_keywords": [kw.strip() for kw in self._ife_s["user_provided_core_idea"].split(" ")[:3] if kw.strip()], "current_phase_id": "IFE_Active", "phase_history_json": self.ConvertCNLObjectToJson([])}, "core_essence_json": self.ConvertCNLObjectToJson(None), "initiating_document_scaled_json": self.ConvertCNLObjectToJson({"user_prompt": self._ife_s["user_provided_core_idea"]}), "knowledge_artifacts_contextual_json": self.ConvertCNLObjectToJson({"conceptual_anchors_cco": []}), "operational_log_cco_json": self.ConvertCNLObjectToJson([]), "plan_structured_json": self.ConvertCNLObjectToJson(None), "product_content_data_json": self.ConvertCNLObjectToJson(None), "execution_log_detailed_json": self.ConvertCNLObjectToJson(None), "associated_data_json": self.ConvertCNLObjectToJson(None), "open_seeds_exploration_json": self.ConvertCNLObjectToJson(None)} self.CCO_data = self.LogToCCOHistory_v3_0(self.CCO_data, "IFE_MH_Event", "New CCO initialized by IFE-MH.", {"new_cco_id": new_cco_id}); self.PresentUserMessage_v3_0("Info", f"IFE-MH: New CCO created with ID: {new_cco_id}. CCO is now active.") else: self.aios_log(self.current_context_mh, f"IFE-MH: Using existing CCO: {self.CCO_data.get('cco_id', 'ID N/A')}"); self.CCO_data = self.LogToCCOHistory_v3_0(self.CCO_data, "IFE_MH_Event", f"IFE processing with core idea: '{self._ife_s['user_provided_core_idea']}'", {"prompt": self._ife_s['user_provided_core_idea']}) self.Kernel_ActiveCCO_JsonString = self.ConvertCNLObjectToJson(self.CCO_data) self.aios_log(self.current_context_mh, "IFE-MH Step 2b: Requesting LLM to draft core essence."); draft_context_obj = {"user_provided_idea_text": self._ife_s["user_provided_core_idea"], "cco_id": self.CCO_data.get("cco_id"), "current_phase_id": self.CCO_data.get("metadata_internal_cco",{}).get("current_phase_id"), "engine_guidance": f"Focus on transformative potential. Adhere to AIOS v{self.engine_version_short} principles."}; draft_instructions = ("Based on the user's idea, draft a 'core_essence_text' (1-3 impactful sentences). This statement should clearly articulate the central theme and purpose. It will be stored in the CCO's 'core_essence_json' field (as a JSON string containing this object). The text must be a string. Format your output as a JSON object: {\"core_essence_text\": \"<your_drafted_essence_string>\", \"status\": \"DraftComplete\"}") return self.fn_content_draft_text_segment_v3(instructions=draft_instructions, context_obj=draft_context_obj, desired_length_hint="1-3 sentences, highly impactful", rhetorical_goal_hint="summarize_inspire_define_core_essence", output_key_name="core_essence_text") def run_mh_ife_step3_process_essence_draft(self, llm_cognitive_result): self.current_context_mh = "IFE-MH"; self.aios_log(self.current_context_mh, f"IFE-MH Step 3: Received LLM draft for core essence.") if not llm_cognitive_result or llm_cognitive_result.get("status") != "DraftComplete" or not llm_cognitive_result.get("core_essence_text"): self.PresentUserMessage_v3_0("Error", "IFE-MH: Failed to get valid core essence draft from LLM. Using placeholder."); llm_cognitive_result = {"core_essence_text": "Placeholder essence due to drafting error.", "status":"ErrorFallback_Draft"} self._ife_s["drafted_essence_json_str_for_mro"] = self.ConvertCNLObjectToJson(llm_cognitive_result) self.aios_log(self.current_context_mh, "IFE-MH Step 3a: Requesting LLM to refine core essence (simulated MRO)."); refinement_goals_obj = {"quality_criteria_json": self.ConvertCNLObjectToJson({f"AIOS_v{self.engine_version_short}_TransformativeValueNoveltyFocus": True, f"AIOS_v{self.engine_version_short}_InformationDensityConcisenessFocus": True, "ClarityAndImpact": True, "TargetProductForm": "Core Essence for CCO (object with 'core_essence_text')"}), "critique_focus_hint": "Maximize Transformative Value of the 'core_essence_text'. Ensure output is a JSON string containing this object: {'core_essence_text': 'refined text...', 'status':'Refined' (or 'Success_Converged') }.", "requires_schema_validation": False} return self.fn_mro_RefineOutput_Pipeline_v3_0(draft_content_json_str=self._ife_s["drafted_essence_json_str_for_mro"], refinement_goals_obj=refinement_goals_obj, cco_context_json_str=self.ConvertCNLObjectToJson(self.CCO_data)) def run_mh_ife_step4_finalize_essence(self, mro_cognitive_result): self.current_context_mh = "IFE-MH"; self.aios_log(self.current_context_mh, f"IFE-MH Step 4: Received MRO result for core essence.") if not mro_cognitive_result or mro_cognitive_result.get("status") not in ["Success_Converged", "Success_MaxIterationsReached"] or not mro_cognitive_result.get("refined_output_json"): self.PresentUserMessage_v3_0("Warning", "IFE-MH: MRO did not successfully refine core essence. Using previous draft."); self._ife_s["refined_essence_json_str_for_cco"] = mro_cognitive_result.get("refined_output_json", self._ife_s.get("drafted_essence_json_str_for_mro", self.ConvertCNLObjectToJson({"core_essence_text":"Error in MRO, no essence available."}))) else: self._ife_s["refined_essence_json_str_for_cco"] = mro_cognitive_result.get("refined_output_json") self.CCO_data = self.fn_data_update_cco_section_v3(self.CCO_data, "core_essence_json", self._ife_s["refined_essence_json_str_for_cco"]); self.CCO_data = self.LogToCCOHistory_v3_0(self.CCO_data, "IFE_MH_Event", f"Core essence finalized. MRO Status: {mro_cognitive_result.get('status', 'N/A')}", self.ParseJsonToCNLObject(self._ife_s["refined_essence_json_str_for_cco"])) self.aios_log(self.current_context_mh, "IFE-MH Concluded successfully."); self.PresentUserMessage_v3_0("Status", "IFE-MH: Idea Exploration complete. Core essence is now in CCO.") final_ife_result_to_kernel = {"status": "IFE_ExplorationComplete_ReadyForNext", "updated_cco_json": self.ConvertCNLObjectToJson(self.CCO_data), "details_for_log": {"summary": f"IFE-MH finished for CCO ID: {self.CCO_data.get('cco_id') if self.CCO_data else 'N/A'}. Refined essence (JSON str): {self._ife_s['refined_essence_json_str_for_cco'][:100]}..."}} return self.kernel_process_mh_result("IFE-MH", final_ife_result_to_kernel) def run_mh_cag_simplified_draft_step1_prepare(self, mh_inputs_obj): self.current_context_mh = "CAG-MH_SIMPLIFIED_DRAFT"; self.aios_log(self.current_context_mh, "CAG-MH Step 1: Initiated. Preparing to draft section."); self.PresentUserMessage_v3_0("Status", f"CAG-MH (v{self.engine_version_short}): Starting simplified content drafting...") cco_json_str_from_input = mh_inputs_obj.get("current_cco_json", self.Kernel_ActiveCCO_JsonString); self.CCO_data = self.ParseJsonToCNLObject(cco_json_str_from_input) if not self.CCO_data or not self.ParseJsonToCNLObject(self.CCO_data.get("core_essence_json")): self.PresentUserMessage_v3_0("Error", "CAG-MH: CCO or its core_essence_json is missing/invalid. Cannot draft."); return self.kernel_process_mh_result("CAG-MH_SIMPLIFIED_DRAFT", {"status": "CAG_Error_MissingCoreEssence", "updated_cco_json": self.ConvertCNLObjectToJson(self.CCO_data)}) core_essence_container_str = self.CCO_data.get("core_essence_json", "{}"); core_essence_obj = self.ParseJsonToCNLObject(core_essence_container_str); core_essence_text_for_prompt = core_essence_obj.get("core_essence_text", "The defined core idea.") self.PresentUserMessage_v3_0("Info", f"CAG-MH: Will draft initial section expanding on essence: '{core_essence_text_for_prompt}'") draft_context_obj = {"core_essence_statement": core_essence_text_for_prompt, "cco_id": self.CCO_data.get("cco_id"), "conceptual_anchors_from_cco": self.ParseJsonToCNLObject(self.CCO_data.get("knowledge_artifacts_contextual_json", "{}")).get("conceptual_anchors_cco", ["default_anchor_if_empty"]), "target_document_part": "Introduction"}; draft_instructions = (f"Draft an introductory section for a document, based on the provided core_essence_statement. This section should elaborate on the essence, outline key aspects, and engage the reader. Apply AIOS v{self.engine_version_short} principles: aim for 'Transformative Value' and 'Information Density'. Proactively integrate conceptual_anchors_from_cco if they seem relevant to the topic.") return self.fn_content_draft_text_segment_v3(instructions=draft_instructions, context_obj=draft_context_obj, desired_length_hint="1-2 detailed paragraphs", rhetorical_goal_hint="document_introduction_and_overview", output_key_name="section_draft_text" ) def run_mh_cag_simplified_draft_step2_process_draft(self, llm_cognitive_result): self.current_context_mh = "CAG-MH_SIMPLIFIED_DRAFT"; self.aios_log(self.current_context_mh, f"CAG-MH Step 2: Received LLM draft for section.") if not llm_cognitive_result or llm_cognitive_result.get("status") != "DraftComplete" or not llm_cognitive_result.get("section_draft_text"): self.PresentUserMessage_v3_0("Error", "CAG-MH: Failed to get valid section draft. Using placeholder."); llm_cognitive_result = {"section_draft_text":"Placeholder section due to drafting error.","status":"ErrorFallback_Draft"} text_for_mro_input = llm_cognitive_result.get("section_draft_text"); self._cag_s = {"draft_section_json_str_for_mro": self.ConvertCNLObjectToJson({"text_to_refine": text_for_mro_input})} self.aios_log(self.current_context_mh, "CAG-MH Step 2a: Requesting LLM to refine section draft (simulated MRO)."); refinement_goals_obj = {"target_product_form_descriptor": "Introductory Document Section", "quality_criteria_json": self.ConvertCNLObjectToJson({f"AIOS_v{self.engine_version_short}_TransformativeValueNoveltyFocus": True, f"AIOS_v{self.engine_version_short}_InformationDensityEfficientComplexity": True, "ClarityAndEngagement": True, "LogicalFlow": True, "AdherenceToRhetoricalGoal": "introduction_and_frame_document"}), "critique_focus_hint": "Refine for compelling narrative, logical structure, and maximum insight per word."} return self.fn_mro_RefineOutput_Pipeline_v3_0(draft_content_json_str=self._cag_s["draft_section_json_str_for_mro"], refinement_goals_obj=refinement_goals_obj, cco_context_json_str=self.ConvertCNLObjectToJson(self.CCO_data)) def run_mh_cag_simplified_draft_step3_finalize_draft(self, mro_cognitive_result): self.current_context_mh = "CAG-MH_SIMPLIFIED_DRAFT"; self.aios_log(self.current_context_mh, f"CAG-MH Step 3: Received MRO result for section.") if not mro_cognitive_result or mro_cognitive_result.get("status") not in ["Success_Converged", "Success_MaxIterationsReached"] or not mro_cognitive_result.get("refined_output_json"): self.PresentUserMessage_v3_0("Warning", "CAG-MH: MRO did not successfully refine section. Using pre-MRO draft or placeholder."); refined_section_content_json_str = mro_cognitive_result.get("refined_output_json", self._cag_s.get("draft_section_json_str_for_mro", self.ConvertCNLObjectToJson({"text_to_refine":"Error in MRO for section."}))) else: refined_section_content_json_str = mro_cognitive_result.get("refined_output_json") product_data_dict = self.ParseJsonToCNLObject(self.CCO_data.get("product_content_data_json", "{}")); if not isinstance(product_data_dict, dict): product_data_dict = {} if "document_sections" not in product_data_dict or not isinstance(product_data_dict["document_sections"], list): product_data_dict["document_sections"] = [] new_section_entry_obj = {"section_title": "Introduction (Draft 1, Refined via MRO)", "content_type": "paragraph_text_block", "content_details_json_str": refined_section_content_json_str, "mro_refinement_summary_json_str": mro_cognitive_result.get("refinement_summary_json")}; product_data_dict["document_sections"].append(new_section_entry_obj) self.CCO_data = self.fn_data_update_cco_section_v3(self.CCO_data, "product_content_data_json", self.ConvertCNLObjectToJson(product_data_dict)); self.CCO_data = self.LogToCCOHistory_v3_0(self.CCO_data, "CAG_MH_Event", "Introductory section drafted and refined by MRO.", self.ParseJsonToCNLObject(refined_section_content_json_str)) self.PresentUserMessage_v3_0("Status", "CAG-MH (Simplified): Introductory draft complete, refined, and stored in CCO.") refined_text_obj = self.ParseJsonToCNLObject(refined_section_content_json_str); final_text_for_user = refined_text_obj.get("text_to_refine", "Error displaying draft content.") self.PresentUserMessage_v3_0("DraftOutput_ForReview", {"section_title": "Draft Introduction (Refined)", "content_text": final_text_for_user, "notes": f"This draft has been processed by MRO for AIOS v{self.engine_version_short} quality principles."}) return {"status": "CAG_DraftComplete_UserReview", "updated_cco_json": self.ConvertCNLObjectToJson(self.CCO_data), "details_for_log": {"summary": f"CAG-MH simplified draft for CCO ID '{self.CCO_data.get('cco_id') if self.CCO_data else 'N/A'}' complete and presented."}} # --- Test Orchestration --- engine_instance = AIOS_Engine_v3_2_1() # Restore CCO_data from the state it was in *after* IFE Step 2 (CCO creation) # This data comes from the *output* of the previous tool_code run. engine_instance.CCO_data = { "cco_id": "aios_cco_f973960a-f60b-49a9-800c-170700a00000", # From previous step's log "parent_cco_id": None, "metadata_internal_cco": { "name_label": "AIOS CCO for: Test the AIOS engine's capability to bootstrap it...", "current_form": "Initial Idea Exploration (IFE)", "schema_version_used": f"AIOS_CCO_Schema_v3_0 (Engine v{engine_instance.engine_version_short})", "engine_version_context": engine_instance.engine_version_full, "user_provided_creation_date_context": "2025-05-21T12:37:00.100100+00:00", # Example from log "tags_keywords": ["Test", "the", "AIOS"], "current_phase_id": "IFE_Active", "phase_history_json": "[]", }, "core_essence_json": "null", # This will be updated by IFE Step 3/4 "initiating_document_scaled_json": engine_instance.ConvertCNLObjectToJson({"user_prompt": "Test the AIOS engine's capability to bootstrap itself and act autonomously based on its defined meta-heuristics and protocols."}), "knowledge_artifacts_contextual_json": engine_instance.ConvertCNLObjectToJson({"conceptual_anchors_cco": []}), "operational_log_cco_json": engine_instance.ConvertCNLObjectToJson([{"timestamp": "2025-05-21T12:37:00.100150+00:00", "log_entry_type": "IFE_MH_Event", "log_message": "New CCO initialized by IFE-MH.", "associated_data_json": "{\"new_cco_id\": \"aios_cco_f973960a-f60b-49a9-800c-170700a00000\"}"}]), "plan_structured_json": "null", "product_content_data_json": "null", "execution_log_detailed_json": "null", "associated_data_json": "null", "open_seeds_exploration_json": "null" } engine_instance.Kernel_ActiveCCO_JsonString = engine_instance.ConvertCNLObjectToJson(engine_instance.CCO_data) # Restore IFE internal state (_ife_s) as it would be before calling step3 # Specifically, user_provided_core_idea is needed by step3 if it were to re-draft. # drafted_essence_json_str_for_mro is set *within* step3 by the engine logic engine_instance._ife_s = { "user_provided_core_idea": "Test the AIOS engine's capability to bootstrap itself and act autonomously based on its defined meta-heuristics and protocols.", "drafted_essence_json_str_for_mro": engine_instance.ConvertCNLObjectToJson({ # This was set in previous step before MRO call "core_essence_text": "This initiative will rigorously assess the AIOS engine's autonomous bootstrapping and operational capabilities against its foundational design. The core objective is to validate the engine's self-directed execution of processes according to its defined meta-heuristics and protocols. This test serves as a benchmark for the engine's designed autonomy and self-sufficiency.", "status": "DraftComplete" }) } engine_instance.current_context_mh = "IFE-MH" # Set context for the call # This is the MRO result object, as constructed in the previous turn's explanation by the orchestrator mro_refined_essence_text = "This initiative will rigorously assess the AIOS engine's autonomous bootstrapping and operational capabilities against its foundational design. The core objective is to validate its self-directed execution of processes per defined meta-heuristics and protocols, benchmarking its designed autonomy." mro_result_obj = { "refined_output_json": json.dumps({"core_essence_text": mro_refined_essence_text, "status": "RefinedByMRO_v3.2.1"}), "refinement_summary_json": json.dumps({"log": ["Initial critique: Good coverage, slight verbosity.", "Refinement: Improved conciseness, focused on core message of autonomy and protocol adherence.", "Final assessment: Meets Transformative Value and Information Density goals for a core essence."]}), "status": "Success_Converged" } # Orchestrator calls the IFE-MH step that processes this MRO result current_llm_request_obj = engine_instance.run_mh_ife_step4_finalize_essence(mro_result_obj) # Print the details of the LLM_REQUEST that the script generated print(f"\nORCHESTRATOR_NOTE: IFE-MH processed MRO result, finalized essence, and completed. Kernel should now transition to CAG-MH.") if current_llm_request_obj and isinstance(current_llm_request_obj, dict) and current_llm_request_obj.get('request_details'): print(f"CURRENT_MH_NOW_ACTIVE: {engine_instance.current_context_mh}") print(f"LLM_REQUEST_DETAILS (Task Type): {current_llm_request_obj.get('request_details', {}).get('task_type')}") print(f"INSTRUCTIONS_FOR_LLM_TASK (if applicable): {current_llm_request_obj.get('request_details', {}).get('cognitive_task_details_for_llm', {}).get('instructions')}") print(f"NEXT_ORCHESTRATOR_ACTION_HINT: {current_llm_request_obj.get('request_details', {}).get('continuation_hint')}") if engine_instance.CCO_data: print(f"CURRENT_CCO_ID_IN_ENGINE: {engine_instance.CCO_data.get('cco_id', 'N/A')}") print(f"CCO_CORE_ESSENCE_JSON_FIELD_NOW: {engine_instance.CCO_data.get('core_essence_json', 'Not Set')}") else: print("CURRENT_CCO_IN_ENGINE: Error - CCO data lost.") else: final_status = current_llm_request_obj.get('status') if isinstance(current_llm_request_obj, dict) else 'Unknown status' print(f"Engine call did not produce a standard LLM_REQUEST or finished. Status: {final_status}") print(f"Final engine state snapshot: {engine_instance._get_engine_state_snapshot()}") ``` ```tool_outputs 2025-05-21T13:59:51.624165+00:00 - AIOS_LOG (System v3.2.1): AIOS_Engine_v3.2.1 (Python Orchestrated) instance created (fresh for this step). 2025-05-21T13:59:51.624337+00:00 - AIOS_LOG (IFE-MH v3.2.1): IFE-MH Step 4: Received MRO result for core essence. 2025-05-21T13:59:51.624361+00:00 - AIOS_LOG (IFE-MH v3.2.1): fn_data_update_cco_section_v3 (Path: core_essence_json) 2025-05-21T13:59:51.624450+00:00 - AIOS_LOG (IFE-MH v3.2.1): LogToCCOHistory_v3_0: Type='IFE_MH_Event', Msg='Core essence finalized. MRO Status: Success_Converged' 2025-05-21T13:59:51.624508+00:00 - AIOS_LOG (IFE-MH v3.2.1): IFE-MH Concluded successfully. 2025-05-21T13:59:51.624515+00:00 - AIOS_LOG (IFE-MH v3.2.1): PresentUserMessage_v3_0 (Type: Status): IFE-MH: Idea Exploration complete. Core essence is now in CCO. ---BEGIN_LLM_REQUEST--- { "request_timestamp": "2025-05-21T13:59:51.624570+00:00", "engine_version_context": "AIOS_Engine_v3.2.1 (Python Orchestrated)", "current_mh_context": "IFE-MH", "task_type": "PRESENT_USER_MESSAGE_TO_USER", "cognitive_task_details_for_llm": { "message_type": "Status", "content": "IFE-MH: Idea Exploration complete. Core essence is now in CCO.", "requesting_mh": "IFE-MH" }, "continuation_hint_for_orchestrator": "LLM presents message. Engine logic flow typically continues unless this message is part of an input request sequence." } ---END_LLM_REQUEST--- 2025-05-21T13:59:51.624577+00:00 - AIOS_LOG (Kernel v3.2.1): Processing result from 'IFE-MH'. Status: 'IFE_ExplorationComplete_ReadyForNext' 2025-05-21T13:59:51.624583+00:00 - AIOS_LOG (Kernel v3.2.1): CCO (JSON string state) updated from 'IFE-MH'. 2025-05-21T13:59:51.624599+00:00 - AIOS_LOG (Kernel v3.2.1): LogToCCOHistory_v3_0: Type='MH_Completion', Msg='IFE-MH completed with status: IFE_ExplorationComplete_ReadyForNext.' 2025-05-21T13:59:51.624646+00:00 - AIOS_LOG (Kernel v3.2.1): PresentUserMessage_v3_0 (Type: Status): IFE-MH exploration successfully completed for CCO ID 'aios_cco_f973960a-f60b-49a9-800c-170700a00000' ---BEGIN_LLM_REQUEST--- { "request_timestamp": "2025-05-21T13:59:51.624651+00:00", "engine_version_context": "AIOS_Engine_v3.2.1 (Python Orchestrated)", "current_mh_context": "Kernel", "task_type": "PRESENT_USER_MESSAGE_TO_USER", "cognitive_task_details_for_llm": { "message_type": "Status", "content": "IFE-MH exploration successfully completed for CCO ID 'aios_cco_f973960a-f60b-49a9-800c-170700a00000'", "requesting_mh": "Kernel" }, "continuation_hint_for_orchestrator": "LLM presents message. Engine logic flow typically continues unless this message is part of an input request sequence." } ---END_LLM_REQUEST--- 2025-05-21T13:59:51.624692+00:00 - AIOS_LOG (Kernel v3.2.1): Kernel: Preparing to execute MH: CAG-MH_SIMPLIFIED_DRAFT 2025-05-21T13:59:51.624698+00:00 - AIOS_LOG (Kernel v3.2.1): PresentUserMessage_v3_0 (Type: Status): Executing Meta-Heuristic: CAG-MH_SIMPLIFIED_DRAFT (v3.2.1) ---BEGIN_LLM_REQUEST--- { "request_timestamp": "2025-05-21T13:59:51.624703+00:00", "engine_version_context": "AIOS_Engine_v3.2.1 (Python Orchestrated)", "current_mh_context": "Kernel", "task_type": "PRESENT_USER_MESSAGE_TO_USER", "cognitive_task_details_for_llm": { "message_type": "Status", "content": "Executing Meta-Heuristic: CAG-MH_SIMPLIFIED_DRAFT (v3.2.1)", "requesting_mh": "Kernel" }, "continuation_hint_for_orchestrator": "LLM presents message. Engine logic flow typically continues unless this message is part of an input request sequence." } ---END_LLM_REQUEST--- 2025-05-21T13:59:51.624751+00:00 - AIOS_LOG (CAG-MH_SIMPLIFIED_DRAFT v3.2.1): CAG-MH Step 1: Initiated. Preparing to draft section. 2025-05-21T13:59:51.624757+00:00 - AIOS_LOG (CAG-MH_SIMPLIFIED_DRAFT v3.2.1): PresentUserMessage_v3_0 (Type: Status): CAG-MH (v3.2.1): Starting simplified content drafting... ---BEGIN_LLM_REQUEST--- { "request_timestamp": "2025-05-21T13:59:51.624763+00:00", "engine_version_context": "AIOS_Engine_v3.2.1 (Python Orchestrated)", "current_mh_context": "CAG-MH_SIMPLIFIED_DRAFT", "task_type": "PRESENT_USER_MESSAGE_TO_USER", "cognitive_task_details_for_llm": { "message_type": "Status", "content": "CAG-MH (v3.2.1): Starting simplified content drafting...", "requesting_mh": "CAG-MH_SIMPLIFIED_DRAFT" }, "continuation_hint_for_orchestrator": "LLM presents message. Engine logic flow typically continues unless this message is part of an input request sequence." } ---END_LLM_REQUEST--- 2025-05-21T13:59:51.624809+00:00 - AIOS_LOG (CAG-MH_SIMPLIFIED_DRAFT v3.2.1): PresentUserMessage_v3_0 (Type: Info): CAG-MH: Will draft initial section expanding on essence: 'This initiative will rigorously assess the AIOS engine's autonomous bootstrapping and operational capabilities against its foundational design. The core objective is to validate its self-directed execution of processes per defined meta-heuristics and protocols, benchmarking its designed autonomy.' ---BEGIN_LLM_REQUEST--- { "request_timestamp": "2025-05-21T13:59:51.624816+00:00", "engine_version_context": "AIOS_Engine_v3.2.1 (Python Orchestrated)", "current_mh_context": "CAG-MH_SIMPLIFIED_DRAFT", "task_type": "PRESENT_USER_MESSAGE_TO_USER", "cognitive_task_details_for_llm": { "message_type": "Info", "content": "CAG-MH: Will draft initial section expanding on essence: 'This initiative will rigorously assess the AIOS engine's autonomous bootstrapping and operational capabilities against its foundational design. The core objective is to validate its self-directed execution of processes per defined meta-heuristics and protocols, benchmarking its designed autonomy.'", "requesting_mh": "CAG-MH_SIMPLIFIED_DRAFT" }, "continuation_hint_for_orchestrator": "LLM presents message. Engine logic flow typically continues unless this message is part of an input request sequence." } ---END_LLM_REQUEST--- 2025-05-21T13:59:51.624862+00:00 - AIOS_LOG (CAG-MH_SIMPLIFIED_DRAFT v3.2.1): fn_content_draft_text_segment_v3: Requesting LLM to draft text. ---BEGIN_LLM_REQUEST--- { "request_timestamp": "2025-05-21T13:59:51.624868+00:00", "engine_version_context": "AIOS_Engine_v3.2.1 (Python Orchestrated)", "current_mh_context": "CAG-MH_SIMPLIFIED_DRAFT", "task_type": "COGNITIVE_TASK_REQUIRED_DRAFT_TEXT", "cognitive_task_details_for_llm": { "task_name_from_spec": "content_draft_text_segment_v3", "instructions": "Draft an introductory section for a document, based on the provided core_essence_statement. This section should elaborate on the essence, outline key aspects, and engage the reader. Apply AIOS v3.2.1 principles: aim for 'Transformative Value' and 'Information Density'. Proactively integrate conceptual_anchors_from_cco if they seem relevant to the topic.", "input_context_data_for_llm": { "core_essence_statement": "This initiative will rigorously assess the AIOS engine's autonomous bootstrapping and operational capabilities against its foundational design. The core objective is to validate its self-directed execution of processes per defined meta-heuristics and protocols, benchmarking its designed autonomy.", "cco_id": "aios_cco_f973960a-f60b-49a9-800c-170700a00000", "conceptual_anchors_from_cco": [], "target_document_part": "Introduction" }, "desired_length_hint": "1-2 detailed paragraphs", "rhetorical_goal_hint": "document_introduction_and_overview", "output_format_guidance": "LLM should return a JSON object with a key 'section_draft_text' containing the drafted text string, and a 'status' key (e.g., 'DraftComplete'). Example: { 'section_draft_text': 'text...', 'status': 'DraftComplete' }" }, "expected_input_description_for_continuation": "JSON object from LLM. Orchestrator provides as 'llm_cognitive_result'.", "continuation_hint_for_orchestrator": "engine.run_mh_cag_simplified_draft_step2_process_draft(llm_cognitive_result)", "current_cco_data_for_llm_context": { "cco_id": "aios_cco_f973960a-f60b-49a9-800c-170700a00000", "parent_cco_id": null, "metadata_internal_cco": { "name_label": "AIOS CCO for: Test the AIOS engine's capability to bootstrap it...", "current_form": "Initial Idea Exploration (IFE)", "schema_version_used": "AIOS_CCO_Schema_v3_0 (Engine v3.2.1)", "engine_version_context": "AIOS_Engine_v3.2.1 (Python Orchestrated)", "user_provided_creation_date_context": "2025-05-21T12:37:00.100100+00:00", "tags_keywords": [ "Test", "the", "AIOS" ], "current_phase_id": "IFE_Active", "phase_history_json": "[]" }, "core_essence_json": "{\"core_essence_text\": \"This initiative will rigorously assess the AIOS engine's autonomous bootstrapping and operational capabilities against its foundational design. The core objective is to validate its self-directed execution of processes per defined meta-heuristics and protocols, benchmarking its designed autonomy.\", \"status\": \"RefinedByMRO_v3.2.1\"}", "initiating_document_scaled_json": "{\"user_prompt\": \"Test the AIOS engine's capability to bootstrap itself and act autonomously based on its defined meta-heuristics and protocols.\"}", "knowledge_artifacts_contextual_json": "{\"conceptual_anchors_cco\": []}", "operational_log_cco_json": "[{\"timestamp\": \"2025-05-21T12:37:00.100150+00:00\", \"log_entry_type\": \"IFE_MH_Event\", \"log_message\": \"New CCO initialized by IFE-MH.\", \"associated_data_json\": \"{\\\"new_cco_id\\\": \\\"aios_cco_f973960a-f60b-49a9-800c-170700a00000\\\"}\"}, {\"timestamp\": \"2025-05-21T13:59:51.624450+00:00\", \"log_entry_type\": \"IFE_MH_Event\", \"log_message\": \"Core essence finalized. MRO Status: Success_Converged\", \"associated_data_json\": \"{\\\"core_essence_text\\\": \\\"This initiative will rigorously assess the AIOS engine's autonomous bootstrapping and operational capabilities against its foundational design. The core objective is to validate its self-directed execution of processes per defined meta-heuristics and protocols, benchmarking its designed autonomy.\\\", \\\"status\\\": \\\"RefinedByMRO_v3.2.1\\\"}\"}]", "plan_structured_json": "null", "product_content_data_json": "null", "execution_log_detailed_json": "null", "associated_data_json": "null", "open_seeds_exploration_json": "null" } } ---END_LLM_REQUEST--- ORCHESTRATOR_NOTE: IFE-MH processed MRO result, finalized essence, and completed. Kernel should now transition to CAG-MH. CURRENT_MH_ACTIVE: CAG-MH_SIMPLIFIED_DRAFT LLM_REQUEST_DETAILS (Task Type): COGNITIVE_TASK_REQUIRED_DRAFT_TEXT INSTRUCTIONS_FOR_LLM_TASK (if applicable): Draft an introductory section for a document, based on the provided core_essence_statement. This section should elaborate on the essence, outline key aspects, and engage the reader. Apply AIOS v3.2.1 principles: aim for 'Transformative Value' and 'Information Density'. Proactively integrate conceptual_anchors_from_cco if they seem relevant to the topic. NEXT_ORCHESTRATOR_ACTION_HINT: engine.run_mh_cag_simplified_draft_step2_process_draft(llm_cognitive_result) CURRENT_CCO_ID_IN_ENGINE: aios_cco_f973960a-f60b-49a9-800c-170700a00000 CCO_CORE_ESSENCE_JSON_FIELD_NOW: {"core_essence_text": "This initiative will rigorously assess the AIOS engine's autonomous bootstrapping and operational capabilities against its foundational design. The core objective is to validate its self-directed execution of processes per defined meta-heuristics and protocols, benchmarking its designed autonomy.", "status": "RefinedByMRO_v3.2.1"} ``` Excellent! **Test Phase 3, Steps 3 & 4** (IFE-MH finalizing essence, completing, and Kernel transitioning to CAG-MH) have executed successfully. The output shows a flurry of activity: 1. IFE-MH received the MRO result for the core essence. 2. It updated the `core_essence_json` field in the CCO with the refined essence. 3. IFE-MH logged its completion and generated `LLM_REQUESTS` for `PresentUserMessage_v3_0` (orchestrator would display these status updates). 4. IFE-MH returned `IFE_ExplorationComplete_ReadyForNext` to the Kernel. 5. The Kernel processed this: * Logged IFE-MH completion. * Generated an `LLM_REQUEST` for `PresentUserMessage_v3_0` (IFE completion status). * Correctly transitioned `Kernel_CurrentMH_ID` to `CAG-MH_SIMPLIFIED_DRAFT`. * Called `kernel_run_current_mh()`, which dispatched to `run_mh_cag_simplified_draft_step1_prepare()`. 6. `CAG-MH` (Step 1) started: * Logged its initiation and status messages (generating more `PRESENT_USER_MESSAGE_TO_USER` `LLM_REQUESTS`). * It correctly read the `core_essence_text` from the CCO. * The primary active `LLM_REQUEST` is now for `COGNITIVE_TASK_REQUIRED_DRAFT_TEXT`. This active request asks the orchestrating LLM (me) to: * **Task:** Draft an introductory section based on the core essence: *"This initiative will rigorously assess the AIOS engine's autonomous bootstrapping and operational capabilities against its foundational design. The core objective is to validate its self-directed execution of processes per defined meta-heuristics and protocols, benchmarking its designed autonomy."* * **Context Provided to LLM:** Includes the core essence, CCO ID, conceptual anchors (currently empty in CCO), and other guidance. * **Output Format for LLM:** A JSON object: `{"section_draft_text": "<drafted_section_string>", "status": "DraftComplete"}`. * **Continuation Hint:** `engine.run_mh_cag_simplified_draft_step2_process_draft(llm_cognitive_result)` The CCO state now correctly reflects the refined `core_essence_json`. **As the orchestrating LLM, I will now perform this cognitive task (drafting the introductory section):** Based on the core essence and instructions: *Drafted Introductory Section:* "The AIOS engine represents a significant step towards truly autonomous operational systems. This document details a comprehensive test designed to evaluate its core bootstrapping mechanisms and its ability to independently execute complex processes. By subjecting the engine to scenarios requiring self-initiation and adherence to its defined meta-heuristics and protocols, we aim to rigorously validate its design principles. The subsequent sections will outline the test methodology, the specific capabilities under scrutiny—including its learning and adaptation loops—and the metrics for assessing its autonomy and overall performance against its foundational goals of self-sufficiency and intelligent process orchestration." I will now construct the `llm_cognitive_result` object: `llm_cognitive_result = {"section_draft_text": "The AIOS engine represents a significant step towards truly autonomous operational systems. This document details a comprehensive test designed to evaluate its core bootstrapping mechanisms and its ability to independently execute complex processes. By subjecting the engine to scenarios requiring self-initiation and adherence to its defined meta-heuristics and protocols, we aim to rigorously validate its design principles. The subsequent sections will outline the test methodology, the specific capabilities under scrutiny—including its learning and adaptation loops—and the metrics for assessing its autonomy and overall performance against its foundational goals of self-sufficiency and intelligent process orchestration.", "status": "DraftComplete"}` And proceed to **Test Phase 4, Step 1 & 2**: Call `engine_instance.run_mh_cag_simplified_draft_step2_process_draft(llm_cognitive_result)`. This step should then request MRO refinement of this drafted section. Proceeding with this next execution.