Agent Harness / Agent Infrastructure

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.

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.

An AI Agent (autonomous agent) is a single, autonomous system based on an AI model — most often an LLM — that dynamically directs its own process and tool usage to accomplish a given goal. In Anthropic's definition (December 2024), an agent is a system in which an LLM independently controls its actions, in contrast to a workflow, where LLMs and tools are orchestrated through predefined code paths. An AI Agent is the concrete executable artifact of the Agentic AI paradigm — analogous to how a microservice is an instance of the microservice paradigm. A single agent has a clearly defined goal, access to a set of tools (web search, code execution, file operations, APIs, MCP), memory (in-context and optionally external), a control loop (perceive → reason → act → observe), and termination conditions (goal achievement, max_steps, escalation). The agent starts work from a command or interactive discussion with a human; once the task is clarified, it operates independently, optionally returning for further information or approval. During execution it obtains "ground truth" from the environment after each step (tool results, code execution) and may pause at checkpoints. In practice, an AI Agent is typically just an LLM using tools in a loop based on environmental feedback — the implementation is often simpler than a framework, but requires care in designing the agent-computer interface (ACI) and tool documentation. AI Agent should be distinguished from related concepts: Agentic AI is the paradigm (class of systems), an AI Agent is an instance (concrete actor); a Multi-Agent System is a collective of multiple cooperating agents; a Workflow is a predefined orchestration of LLMs without decisional autonomy.

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.

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.

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).

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.