Agentic AI Cannot be Built Using "Only" a Language Model

An agentic AI system might not be as complicated an endeavor as Artificial General Intelligence (AGI), but it still is plenty complicated, involving a platform that integrates any number of capabilities including reasoning and problem solving; learning and adaptation; communication and understanding; perception; decision-making and planning; memory and knowledge management; goal setting; self-monitoring and reflection; human interaction; creativity; autonomy; security and adaptability. 

As you would guess, each of these capabilities is embodied in different platforms or systems. 

Reasoning and Problem-Solving:

• Deductive Reasoning: Prolog, Expert Systems like CLIPS

•  Inductive Reasoning: Decision Trees, Bayesian Networks (e.g., Bayes Net Toolbox for MATLAB).

• Abductive Reasoning: Systems like Sherlock (a framework for abductive reasoning).

• Problem Decomposition: Planning systems like PDDL (Planning Domain Definition Language) used by planners like Fast Downward or LAMA

Learning and Adaptation:

• Supervised Learning: TensorFlow, PyTorch for training models like CNNs, RNNs.

• Unsupervised Learning: Autoencoders, K-means clustering implemented in scikit-learn.

• Reinforcement Learning: OpenAI's Gym, DeepMind's MuJoCo, or libraries like Stable Baselines3

Communication and Understanding:

• Large Language Models (LLMs): Hugging Face's Transformers with models like BERT, GPT-3, or custom implementations like those from xAI.

• Transformers: Libraries like Hugging Face Transformers or Microsoft's DeepSpeed

Perception:

• Image Recognition: OpenCV for basic operations, TensorFlow or PyTorch for CNNs like ResNet, YOLO for real-time object detection.

• Speech Recognition: Kaldi, Mozilla's DeepSpeech, or cloud APIs like Google Cloud Speech-to-Text.

• Sensor Integration: ROS (Robot Operating System) for robotics, or custom IoT frameworks for sensor data integration

Decision Making and Planning:

• Rule-Based Systems: Drools, Jess for Java, or custom Python rule engines.

• Neural Networks: Keras, TensorFlow, PyTorch for implementing various neural network architectures

Memory and Knowledge Management:

• Short and Long-Term Memory: Implementations of LSTM or GRU in neural network libraries for memory, or dedicated systems like MemN2N (Memory Networks).

• Knowledge Graphs: Neo4j for graph databases, or RDF systems like Apache Jena for semantic web applications.

Also required: 

• Goal Setting and Management: Custom implementations, or frameworks like BDI (Belief-Desire-Intention) agent architectures.

• Ethical Decision Making: Not standardized but could involve rule engines with ethical guidelines or AI fairness toolkits like Fairlearn.

• Self-Monitoring and Reflection: Could involve meta-learning frameworks or custom solutions using reinforcement learning for self-improvement.

• Interaction with Humans and Other Systems: Dialog systems like Rasa or Microsoft Bot Framework, or APIs for system integration.

• Creativity and Innovation: Generative Adversarial Networks (GANs) using TensorFlow or PyTorch, or systems like DALL-E for image generation.

• Autonomy in Execution: Custom agents using frameworks like JADE (Java Agent DEvelopment Framework) or integrating with IoT platforms for physical actions.

• Security and Privacy: Cryptographic libraries like OpenSSL, or frameworks ensuring differential privacy like TensorFlow Privacy.

• Adaptability to New Environments or Tasks: Meta-learning approaches like MAML (Model-Agnostic Meta-Learning) or transfer learning capabilities in deep learning frameworks.

• Reasoning and Problem-Solving:

• Deductive Reasoning: For logical conclusions from known premises.

• Inductive Reasoning: To infer general rules from specific instances.

• Abductive Reasoning: For forming hypotheses from incomplete data.

• Problem Decomposition: Ability to break down complex problems into manageable parts.

The point is that for all the legitimate attention now paid to large language models (generative AI), that field is but one among many that would have to be assembled and orchestrated to create agentic AI.

And even if an application-specific agent should be less complicated to create than AGI, it still is complicated.