Agentic AI

A Large Language Model (LLM) is a class of machine learning models based on the Transformer architecture, trained on large text datasets via autoregressive language modeling (next-token prediction). These models have billions of parameters and can generate coherent text, answer questions, write code, translate languages, and perform many other language-cognitive tasks without task-specific fine-tuning. The term covers models such as GPT, LLaMA, Gemini, Claude, and Mistral. Most modern LLMs are instruction-tuned (SFT + RLHF) after the pre-training phase.

Chain-of-Thought (CoT) Reasoning is a prompting technique introduced by Wei et al. (2022) in which a large language model is induced to generate a series of intermediate natural-language reasoning steps as part of its output, prior to producing a final answer. The technique was shown to significantly improve LLM performance on arithmetic, commonsense, and symbolic reasoning benchmarks where standard few-shot prompting yields flat or poor results. In the original formulation (few-shot CoT), a small number of exemplar question-answer pairs are included in the prompt, where each answer consists of a chain of thought followed by the final answer. The model learns from these demonstrations to produce its own reasoning chains. A subsequent zero-shot variant (Kojima et al., 2022) showed that appending the phrase 'Let's think step by step' to a question is sufficient to elicit reasoning chains from large models without any exemplars. CoT is an emergent property: empirical results in the originating paper show that reasoning ability via CoT prompting appears only in models above a certain parameter threshold (approximately 100B parameters for the models tested in 2022), with smaller models not benefiting or performing worse. This relationship has been revisited by subsequent work as smaller models have been fine-tuned on CoT data. Key extensions include Self-Consistency CoT (Wang et al., 2022), which samples multiple reasoning paths and selects the most frequent final answer; Tree of Thoughts (Yao et al., 2023), which frames reasoning as search over a tree of intermediate thoughts; and native reasoning models such as OpenAI o1 (2024) and DeepSeek-R1 (2025), which internalize extended reasoning through reinforcement learning on process reward signals rather than relying on prompting.

Tool-augmented LLM is an architectural pattern in which a large language model is equipped with access to one or more external tools that it can invoke during inference by generating structured function-call or API-call outputs. The model learns when and how to call tools by producing special tokens or structured output (e.g., JSON function calls) that are intercepted by a host runtime, executed against the tool, and whose results are returned to the model as new context for continued generation. The canonical formalization appeared in the Toolformer paper (Schick et al., Meta AI, 2023), which demonstrated that LLMs can learn to self-supervise their own tool-use through API call annotation without requiring large labeled datasets. Toolformer showed that models trained this way can decide which tools to call, when, and with which arguments, and that tool use substantially improves performance on tasks requiring fresh information, arithmetic, multilingual lookup, and question answering. The pattern encompasses several mechanisms: (1) in-context tool specification, where tool interfaces are described in the system prompt or context (JSON Schema, OpenAPI, natural language); (2) function calling APIs, where the model produces structured output matched to a defined schema and the host dispatches the call; (3) ReAct-style interleaving, where the model alternates reasoning traces with tool-use observations; and (4) parallel tool calling, where the model emits multiple tool calls simultaneously to be executed concurrently. Key implementations include OpenAI function calling (GPT-4, June 2023), Anthropic tool use (Claude, 2023), Google Gemini function calling, and the Model Context Protocol (MCP, 2024) which standardizes tool server connectivity.

Retrieval-Augmented Generation (RAG) was introduced by Lewis et al. (2020) as a general-purpose fine-tuning recipe combining pre-trained parametric memory (a seq2seq language model, specifically BART in the original paper) with non-parametric memory (a dense vector index of Wikipedia, accessed via Dense Passage Retrieval, DPR). In the original formulation, both the retriever and the generator are fine-tuned end-to-end: given an input query x, the retriever retrieves top-k documents z from the corpus, and the generator produces an output y conditioned on x and z. Two formulations were proposed: RAG-Sequence (the same retrieved documents condition the full output sequence) and RAG-Token (different documents may be used per generated token, marginalized during generation). In widespread contemporary usage (post-2022, with the growth of LLM applications), 'RAG' has expanded to describe a broader class of retrieve-then-generate pipelines, typically with a frozen LLM, a vector store containing pre-computed dense embeddings of document chunks, and a retrieval step that fetches top-k relevant chunks based on embedding similarity to the query. The retrieved chunks are appended to the prompt as context before the LLM generates a response. This non-trainable pipeline variant is technically distinct from the original Lewis et al. formulation but is the dominant practical interpretation of RAG as of 2023–2025. The canonical modern RAG pipeline consists of an offline indexing phase (document chunking, embedding computation, storage in a vector database) and an online query phase (query embedding, approximate nearest neighbor search, context-augmented generation). Key design decisions include: chunk size and overlap, embedding model choice, retrieval strategy (dense, sparse/BM25, or hybrid), number of retrieved documents k, and context integration method (prepend to prompt, cross-attention injection, or fusion-in-decoder). RAG addresses two fundamental limitations of parametric-only LLMs: the knowledge cutoff problem (inability to access post-training information) and hallucination (generation of factually incorrect content). However, RAG introduces its own failure modes, including retrieval of irrelevant or misleading context and the LLM's susceptibility to being distracted by retrieved content that contradicts its parametric knowledge.

Model Context Protocol (MCP) is an open protocol developed by Anthropic and released in November 2024. It addresses the M×N integration problem in AI systems: connecting M different LLM applications to N different external tools previously required M×N bespoke connectors. MCP defines a standardized client-host-server architecture where hosts (LLM applications) manage one or more clients, each maintaining a stateful session with a specific server. Servers expose capabilities as three primitives: Resources (structured data for context), Prompts (templated instructions), and Tools (executable functions). Clients expose two primitives: Roots (filesystem entry points) and Sampling (server-initiated LLM completions). Communication is based on JSON-RPC 2.0. Capability negotiation occurs at session initialization. The protocol is transport-agnostic and has been implemented in Python, TypeScript, C#, and Java SDKs. In December 2025, Anthropic donated MCP governance to the Agentic AI Foundation (AAIF) under the Linux Foundation.

Multi-Agent Systems (MAS) are a paradigm in Distributed Artificial Intelligence in which multiple autonomous software entities — agents — interact within a shared environment to achieve individual or collective goals. Each agent perceives its environment through sensors or interfaces, reasons about its state, and acts through actuators or API calls. In the context of LLM-based MAS (emerging prominently from 2023 onward), agents are powered by large language models that provide the cognitive core (planning, reasoning, natural language communication), supplemented by memory modules, tool-use interfaces, and role-specific prompts. The system architecture defines how agents coordinate: coordination topologies include sequential pipelines, hierarchical orchestration (orchestrator-worker), parallel fan-out/fan-in, publish-subscribe messaging, and decentralized peer-to-peer communication. Core agent properties, as defined by Wooldridge and Jennings (1995), include autonomy, social ability, reactivity, and pro-activeness. In LLM-based systems, key components are: the agent (an LLM with a system prompt defining its role), a communication channel (natural language messages, structured function calls, or shared memory), an orchestrator or coordinator (managing task decomposition, routing, and state), tool-use interfaces (external APIs, code execution, web search), and a memory subsystem (short-term context, long-term vector storage). Prominent frameworks implementing LLM-based MAS include AutoGen (Microsoft, 2023), CAMEL (2023), MetaGPT (2023), CrewAI, and LangGraph.

A reasoning model (also: large reasoning model, LRM, reasoning language model, RLM) is a type of large language model that has been specifically post-trained to solve complex multi-step problems by explicitly generating intermediate reasoning steps before committing to a final response. Unlike standard LLMs that generate a direct response in a single forward pass, reasoning models allocate additional computation at inference time — a property known as test-time compute scaling — by producing a long internal chain of thought (CoT). The reasoning trace typically includes steps such as problem decomposition, hypothesis generation, self-verification, reflection, and correction of errors. The defining characteristics of reasoning models are: (1) post-training via large-scale reinforcement learning (RL) using reward signals based on final answer correctness (and sometimes intermediate step quality via process reward models); (2) the emergence of extended, often hidden, reasoning traces that precede the final answer; (3) a consistent empirical relationship between the length or computational budget allocated to the reasoning trace and final answer quality (test-time scaling law); (4) superior performance on verifiable tasks requiring multi-step logic, such as mathematics, competitive programming, and scientific reasoning. The term 'reasoning model' was introduced as a product category by OpenAI in September 2024 with the release of the o1-preview model. OpenAI described o1 as trained via a large-scale RL algorithm teaching the model to use chain of thought productively. The approach does not rely on explicit tree search algorithms; instead, implicit search emerges via RL-trained CoT generation. In January 2025, DeepSeek published the first detailed open technical description of this class of models in the DeepSeek-R1 paper (arXiv:2501.12948), demonstrating that reasoning capabilities can be incentivized via pure RL without supervised fine-tuning, using Group Relative Policy Optimization (GRPO) as the RL framework. Reasoning models typically employ the same base Transformer decoder architecture as standard LLMs, with the key difference residing entirely in the post-training pipeline: RL replaces or augments standard RLHF/SFT, and reward signals are grounded in verifiable outcomes. The resulting models generate substantially longer token sequences during inference (reasoning tokens), which are often hidden from end users but incur real compute costs. Performance consistently improves with both more training-time RL compute and more inference-time thinking budget.