Conversational 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.

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.

Agentic AI denotes an architectural transition from single-turn, stateless generative models toward goal-directed systems capable of autonomous perception, planning, action, and adaptation through iterative control loops. An agentic system wraps a large language model in a runtime that gives the model access to tools (web search, code execution, APIs, file I/O), persistent memory, and feedback from prior steps. The model then decides dynamically which tools to call, in what order, and whether to loop or stop, rather than following a predefined code path. Two primary system types are commonly distinguished: (1) Workflows, in which LLMs and tools are orchestrated through predefined code paths, and (2) Agents, in which the LLM itself directs its process and tool usage dynamically. Both can be composed into multi-agent systems where specialized agents collaborate, with one acting as orchestrator and others as subagents. Key design patterns identified by Anthropic (2024) include prompt chaining, routing, parallelization, orchestrator-workers, and evaluator-optimizer loops. Andrew Ng's 2024 taxonomy describes four foundational patterns: Reflection, Tool Use, Planning, and Multi-Agent Collaboration. Formal frameworks model agentic control loops as Partially Observable Markov Decision Processes (POMDPs). The control loop is: perceive state → reason/plan → select action → execute tool → observe result → update state → repeat. Agentic systems introduce risks not present in single-turn models, including hallucination in action, prompt injection through observed content, infinite loops, reward hacking, and tool misuse.

Agents as a Service (AaaS) is a software delivery model in which a vendor provides the customer with an autonomous AI agent that performs concrete business tasks, instead of a traditional human-operated application. The term was publicly introduced on March 25, 2026, by Sierra co-founders Bret Taylor and Clay Bavor in a blog manifesto announcing their Ghostwriter agent as their own realization of this paradigm. Unlike Software as a Service (SaaS), where customers buy access to an interface (menus, form fields, tables) and perform the work themselves through clicks, in AaaS the customer defines a desired outcome in natural language, and the vendor delivers an agent that builds and improves production agents or performs the work directly. The defining property is full autonomy: the agent has access to data, tools, a sandboxed test environment, and the deployment pipeline, while the human acts as a supervisor approving changes. The key technical enabler is the agent harness — scaffolding of tools, memory, planning, and task context — combined with refactoring the platform into headless infrastructure that an agent can invoke programmatically rather than navigating through a UI. The work cycle includes analyzing interactions, identifying improvement opportunities, validating in a sandbox, and preparing for review — which Sierra calls an "agent assembly line." AaaS is tightly coupled with the Agentic AI paradigm (its technical foundation) and outcome-based billing models (its commercial superstructure).