There often is a tendency to believe that lower-cost large language models (generative artificial intelligence) have direct implications for the cost of other forms of AI, that is at best partly true, one can argue.
Consider the relationship between LLMs and agentic AI or “artificial general intelligence.”
While LLMs provide language fluency and broad knowledge, they lack deep reasoning, memory, planning, and real-world interaction. A true AGI would integrate LLMs with other AI paradigms, including:
Symbolic AI for logic & reasoning
Reinforcement Learning for decision-making
Memory systems for persistent knowledge
Multimodal AI for vision, speech, and sensory input
Self-learning and world modeling for adaptability
Artificial General Intelligence (AGI) would require a system that can learn, reason, adapt, and generalize across a wide range of tasks, much like a human. While LLMs (Large Language Models) are powerful in processing and generating text, they have key limitations that prevent them from achieving AGI on their own. However, they can play an important role as a component within a larger AGI system.
Likewise, LLMs provide language understanding, reasoning, and decision support, making them useful for agentic AI in several ways:
Language comprehension and generation – LLMs enable agents to process natural language instructions, communicate with users, and generate responses.
Reasoning and planning – Through prompt engineering (e.g., Chain-of-Thought prompting), LLMs can simulate step-by-step problem-solving.
Knowledge retrieval and synthesis – LLMs act as information processors, integrating and summarizing knowledge from different sources.
Code and automation – LLMs can generate and execute code, allowing agents to perform automated workflows
However, LLMs alone are reactive. They respond to prompts rather than initiating actions autonomously. To become true agents, AI needs additional capabilities.
The Role of LLMs in AGI
LLMs can serve as a language and knowledge engine in AGI by:
Understanding and generating natural language (communication)
Encoding vast amounts of world knowledge
Generating code, plans, and reasoning chains (for problem-solving)
However, AGI needs much more than just language modeling. It requires learning, reasoning, memory, perception, and real-world interaction.
To create an AGI system, additional AI subsystems beyond LLMs would be needed, including:
Memory and long-term knowledge retention, as LLMs do not retain memory between sessions.AGI needs episodic memory (remembering past interactions) and semantic memory (storing structured facts over time). So LLMs need to integrate with or interface with databases or vector memory systems.
Reasoning and planning (LLMs can do some reasoning, but they do not truly understand causality or plan long-term. So any AGI would require logic-based reasoning systems, similar to symbolic AI or neuro-symbolic approaches.
Learning beyond pretraining (AGI must be able to continually learn and update its knowledge based on new experiences. This might involve meta-learning, reinforcement learning, and active learning approaches.
Multimodal perception (AGI would need vision, audio, and sensor-based perception
Goal-directed behavior and autonomy (AGI would need an autonomous agent system that can pursue objectives, optimize actions, and self-correct over time)
Embodiment and real-world interaction (Some argue AGI will need a physical or simulated "body" to interact with the world, similar to how humans learn)
Instead of replacing LLMs, AGI systems may incorporate them as a central knowledge and communication layer while combining them with other AI components.
Role of LLMs for Agentic AI
To transform LLMs into autonomous agents, researchers combine them with additional components, such as:
Memory-Augmented LLMs (vector databases (such as Pinecone, Weaviate, ChromaDB) to store and retrieve past interactions, allowing agents to remember previous tasks and refine their behavior over time. AutoGPT and BabyAGI use memory to track goals and intermediate steps, for example.
Planning and Decision-Making Modules (LLMs are combined with reinforcement learning, symbolic AI, or search-based planning systems to enable structured reasoning. OpenAI’s tool-use framework lets LLMs call APIs, retrieve information, and solve complex problems step-by-step, for example.
API and Environment Interaction (LLM-powered agents need tools to execute actions, such as calling APIs, running scripts, or manipulating environments. LangChain and OpenAI Functions enable LLMs to interact with external tools (databases, automation scripts), for example.
Feedback & Self-Improvement Loops (Agents use self-reflection to evaluate and refine their outputs).
Several AI frameworks integrate LLMs into agentic systems:
AutoGPT and BabyAGI (LLM-based agents autonomously define objectives, plan tasks, execute steps, and iterate on results. An AutoGPT agent for market research might break down tasks into three major tasks: research competitors; summarize trends and draft a strategy report.
LangChain Agents (which enable external tools and application programming interfaces; store and recall memory; plan and execute workflows. An example is a customer service agent that remembers user history and escalates issues as needed.
ReAct (Reasoning + Acting) is an architecture allowing LLMs to reason about tasks, proceed step-by-step and decide on actions. For example, a travel agent would, on behalf of a user, conclude that "I need to find a flight to New York." So "I should check Google Flights" and then "compare prices before booking."
The main point is that LLMs are a functional part of platforms aiming to provide agentic AI and future AGI systems, but that LLM cannot do so in the same way that an operating system enables a personal computer to function. An LLM is part of a suite of capabilities.
The implication is that lower-cost LLM training and inference costs contribute to other developments in agentic AI and AGI, but are not a sole and sufficient driver of those developments.
