"Chain-of-thought" autonomous agentic wrappers such as AutoGPT around an LLM such as GPT-4, and similar Language Model Cognitive Architectures (LMCAs) [or another commonly used term is Language Model Autonomous Agents (LMAAs)], are a recent candidate approach to building an AGI. They create, edit, and maintain a natural language context by recursively feeding parts of this into the LLM along with suitable prompts for activities like subtask planning, self-criticism, and memory summarization, generating a textual stream-of-consciousness, memories etc. They thus combine LLM neural nets with natural language symbolic thinking more along the lines of GOFAI. Recent open-source examples are quite simple and not particularly capable, but it seems rather plausible that they could progress rapidly. They could make interpretability much easier than pure neural net systems, since their 'chain-of-though'/'stream of consciousness' and 'memories' would be written in human natural language, so interpretable and editable by a monitoring human or LLM-based monitoring system (modulo concerns about opaque natural language or detecting possible hidden steganographic side-channels concealed in apparently-innocent natural language). This topic discusses the alignment problem for systems combining such agentic wrappers with LLMs, if they are in fact capable of approaching or reaching AGI.