You are absolutely correct to point that out, and I apologize if the previous output seemed truncated or if my explanation wasn't clear enough about its scope. The Python script I provided in the last response **is the complete code for the `AIOS_Engine_v3_2_1` class *as we have developed and tested it so far in this session***. It includes: * The main `AIOS_Engine_v3_2_1` class structure. * The **Kernel logic** for: * Starting the engine (`start_engine`). * Presenting initial user options (`kernel_present_initial_options`). * Processing the user's initial choice (`kernel_process_initial_choice_result`). * Dispatching to Meta-Heuristics (`kernel_run_current_mh`). * Processing the results from Meta-Heuristics (`kernel_process_mh_result`). * Handling general user directives when paused (`kernel_process_general_user_directive`). * The **`IFE-MH` (Idea Formulation & Exploration)** logic, broken into four resumable steps: * `run_mh_ife_step1_get_core_idea` * `run_mh_ife_step2_process_core_idea` * `run_mh_ife_step3_process_essence_draft` * `run_mh_ife_step4_finalize_essence` * The **simplified `CAG-MH` (Content Auto-Generation)** logic for drafting an introduction, broken into three resumable steps: * `run_mh_cag_simplified_draft_step1_prepare` * `run_mh_cag_simplified_draft_step2_process_draft` * `run_mh_cag_simplified_draft_step3_finalize_draft` * The Python implementations of the **Autologos Library Functions** (like `PresentUserMessage_v3_0`, `ParseJsonToCNLObject`, `ConvertCNLObjectToJson`, `LogToCCOHistory_v3_0`). * The Python interface methods for the **"AI Cognitive Functions"** (like `fn_interaction_present_options_v3`, `fn_content_draft_text_segment_v3`, `fn_mro_RefineOutput_Pipeline_v3_0`, etc.) which are designed to generate `LLM_REQUEST` blocks for an orchestrating LLM. * The necessary helper methods (`_get_timestamp`, `_create_llm_request`, `_get_engine_state_snapshot`). **Why it might seem shorter or what's "missing" to make it a "full, complete version of the AIOS engine" as per your original `AIOS_Engine_Bootstrap.md`:** 1. **Other Meta-Heuristics:** As discussed previously, the following MHs from your specification are **not yet translated** into Python methods in this script: * `PDF-MH` * `PLAN-MH` * A *full, complex* `CAG-MH` (beyond the current simplified intro drafter) * `SEL-MH` * `KAU-MH` * `TDE-MH` * `FEL-MH` Each of these would require its own set of `run_mh_..._stepX_...` methods, similar to how IFE and the simplified CAG are structured, to handle their internal logic and interactions. 2. **`MRO_Orchestrator_v3_0` Granularity:** The `MRO_Orchestrator_v3_0` (MetaRefineOutput) is a complex process in your Autologos specification, with its own internal loop, state, and calls to multiple cognitive functions (critique, suggest revisions, apply revisions, validate). In the current Python script, `fn_mro_RefineOutput_Pipeline_v3_0` treats this entire MRO pipeline as a **single cognitive task for the LLM**. * To make it a "fuller" Python implementation, the MRO logic itself would need to be translated into resumable Python methods (e.g., `run_mro_step1_critique`, `run_mro_step2_suggest`, etc.), each potentially making `LLM_REQUESTS` for specific sub-tasks. 3. **Depth of Cognitive Function Simulation:** The `fn_..._v3` methods currently just define the *interface* (the `LLM_REQUEST`) for the orchestrating LLM. They don't contain the actual complex AI logic themselves. This is by design for the LLM-orchestrated model, but it means the Python script alone isn't "intelligent" for those tasks. The intelligence comes from the orchestrating LLM that fulfills these requests. 4. **Operational Protocols:** Many of the detailed operational protocols (e.g., CCO Phase Reset, advanced error handling, heuristic prioritization, proactive CCO save prompts beyond basic logging) are only conceptually present or minimally implemented. Their full logic would need to be woven into the Kernel and relevant MH methods. 5. **External File Dependencies:** The script doesn't load or parse external files like `function_declarations_v3.2.json` or schema files. The "cognitive functions" are directly mapped to Python methods (`fn_..._v3`). Schema validation (e.g., `utility_validate_data_against_schema_v3` from your MRO Autologos) is not explicitly implemented as a Python function that loads and uses a schema file. **How to structure the work to get to a "full, complete version":** We need to continue the process we've started: 1. **Prioritize the Next Component:** Decide which MH or core functionality is most critical to implement next. For example: * A more complete `CAG-MH` for generating various sections of a document. * `PLAN-MH` to allow the engine to create a plan based on the IFE output. * `TDE-MH` for task execution and CCO phase management. * A more granular, step-by-step Python implementation of the `MRO_Orchestrator_v3_0` logic. 2. **Translate Autologos to Resumable Python Methods:** For the chosen component: * Analyze its Autologos script from `AIOS_Engine_Bootstrap.md`. * Break its logic into sequential, resumable Python methods (e.g., `run_mh_plan_step1_analyze_inputs`, `run_mh_plan_step2_draft_plan_structure`, etc.). * Each point where the Autologos script `INVOKE`s an interactive function or a complex cognitive function will become a point where the Python method returns an `LLM_REQUEST`. * Internal state for that MH will be managed within the `AIOS_Engine_v3_2_1` instance (e.g., in a `self._plan_s` dictionary). 3. **Define Interfaces for New Cognitive Functions:** If the new MH uses cognitive functions not yet defined as `fn_..._v3` methods, create those interfaces to generate appropriate `LLM_REQUESTS`. 4. **Update Kernel Dispatch and Result Processing:** * Add an `elif` block in `kernel_run_current_mh` to dispatch to the new MH's entry method. * Add logic in `kernel_process_mh_result` to handle the completion statuses of the new MH and determine the next transition (e.g., after PLAN-MH, maybe it goes to TDE-MH or CAG-MH). 5. **Iterative Testing:** Test each new MH or significant piece of logic step-by-step using the LLM orchestration model in this chat, just as we've been doing for IFE and CAG. **To be absolutely clear, the script I provided last *is* the complete code for what has been translated and tested *so far*.** It is a functional "slice" of your AIOS engine. To make it a "full" version, we need to add Python translations for the remaining MHs and functionalities from your specification document. **What would you like to implement next?** Given your goal of a text-based process engine that can take an idea to a finished piece of writing, perhaps a more robust `CAG-MH` or the `PLAN-MH` would be logical next steps after IFE. Or, if you prefer, I can give you the current script again, and you can try running it with an LLM orchestrator in AI Studio to experience the current Kernel -> IFE -> Simplified CAG flow yourself.