The article explains that AI agents often fail because they lack a reliable runtime environment—called a Harness—and outlines a systematic Harness Engineering approach, including seven core responsibilities, a practical checklist, and concrete examples to turn failures into reusable infrastructure.
The article defines the Agent Harness as the full software infrastructure that turns a stateless LLM into a capable autonomous agent, details its three engineering layers, enumerates twelve production‑grade components, walks through a step‑by‑step execution loop, compares implementations in Anthropic, OpenAI, LangChain, CrewAI and AutoGen, and discusses key design decisions and future trends, emphasizing that harnesses remain essential even as model capabilities improve.
This article demystifies twelve essential AI terms—LLM, tool, MCP, script, prompt, skill, token, context window, RAG, loop, harness, and agent—using a workplace analogy to show how each component transforms a language model into a functional AI assistant.
The article analyzes how large language models serve as decision engines but need tool ecosystems as limbs, explains the Model Context Protocol (MCP) as a universal USB‑C‑like standard, details its 2026 stateless revision, showcases enterprise deployments, and introduces the A2A protocol for agent‑to‑agent collaboration.
The article analyzes how AI applications evolve from answering questions to executing tasks, outlines the data, context, and execution challenges, and explains how Elasticsearch Agent Builder integrates searchable data, tool capabilities, and governance into a verifiable execution chain, illustrated with a log‑analysis case study.
This article analyzes why traditional one-time authorization fails when AI agents dynamically select and chain tools, proposing a context-aware framework of connection, delegation, and confirmation grounded in MCP specifications and NIST AI risk management to ensure accountable, scoped, and auditable agent actions.
The article explains that an Agent Harness— the full software infrastructure surrounding an LLM— is essential for production‑grade AI agents, detailing its definition, three engineering layers, twelve concrete components, execution loops, framework implementations, and key design decisions that separate harness failures from model shortcomings.
The article explains that AI agent failures are rarely due to model intelligence and instead stem from three engineering layers—Harness, Loop, and Graph—detailing how tool access, verification loops, and workflow graphs affect reliability, and provides a checklist for diagnosing which layer is broken.
Even after a model and its API are working, real‑world AI agents often fail because of missing infrastructure such as tool definitions, sandbox boundaries, state persistence, memory handling, tracing, and evaluation, requiring a systematic approach to turn model outputs into controlled, repeatable actions.
The article explains how an AI Agent Harness transforms a powerful language model into a reliable, controllable agent by adding tool access, memory, permissions, guardrails, observability, and recovery mechanisms, and outlines its core components, workflow, and a practical customer‑service example.
This article presents a comprehensive, step‑by‑step guide to Harness Engineering—a framework that embeds "good code" standards into the AI coding toolchain, explains why Vibe Coding fails, details six core pillars (Context, Tools, Orchestration, State, Evaluation, Guardrails), and shows how teams can adopt the process, configure CodeBuddy, set up Rules, Skills, Knowledge Bases, MCP services, and enforce compliance with the harness‑audit Skill.
The article explains that an Agent Harness is the full software infrastructure surrounding a large language model—handling orchestration loops, tool integration, memory, context management, error handling, and security—and shows how production‑grade harnesses, defined by Anthropic, OpenAI and LangChain, consist of twelve components, with detailed design trade‑offs and practical examples.
This article walks through the Spring AI Alibaba Agent Framework (v1.1.2.0), explaining the ReAct reasoning‑acting loop, core APIs, configuration, code examples, testing commands, and common troubleshooting steps so developers can quickly create LLM‑driven agents with tool‑calling and memory support.
The article dissects the concept of an Agent Harness, distinguishes it from the agent itself, outlines three engineering layers, enumerates twelve production‑grade components, walks through a full execution loop, and compares how major frameworks implement these ideas.
OpenWorker, the newly open‑sourced AI agent announced by Andrew Ng, lets users specify desired outcomes and automatically breaks tasks into steps, invokes selected LLMs and tools, and delivers completed results—supporting 25+ integrations, local data handling, model‑agnostic operation, and a safety‑first approval workflow.
The article dissects the concept of an Agent Harness—software infrastructure that wraps a stateless LLM to enable autonomous agent behavior—detailing its three engineering layers, twelve core components, execution loop, benchmark gains, and design trade‑offs across Anthropic, OpenAI, LangChain, LangGraph, CrewAI and AutoGen frameworks.
The article dissects Claude Code’s harness control framework, showing how a simple execution loop, planning layer, sandboxed tools, hierarchical delegation, and persistent memory—implemented with CrewAI—enable reliable AI‑driven code fixing, while highlighting trade‑offs, overhead, and future limitations.
The article explains that an Agent Harness is the full software infrastructure surrounding a large language model—handling orchestration loops, tool integration, memory, context management, state persistence, error handling, safety guards, and validation—showing why harness design, not model size, determines production‑grade agent performance.
Agent Harness is the full‑stack software layer that turns a stateless LLM into a capable, memory‑aware, tool‑using AI agent, and the article breaks down its three engineering layers, twelve production components, execution loop, and design trade‑offs across Anthropic, OpenAI, LangChain and other frameworks.
This article surveys the latest self‑improving agent research, presenting a unified formal framework that classifies over 200 works from 2023‑2026 into base‑model and scaffold improvement paths, detailing mechanisms, risks, and representative systems.
This article surveys recent Agentic RL research, extracts practical design principles, and details concrete implementations such as ToRL, AgentGym‑RL, Agent‑R1, StarPO, and AutoForge, highlighting reward design, environment interfaces, scaling strategies, and stability diagnostics for long‑horizon autonomous agents.
The article dissects the concept of an Agent Harness— the full software infrastructure that turns a stateless LLM into a capable, autonomous agent—by detailing its three engineering layers, twelve core components, execution loop, framework implementations, and the trade‑offs that determine performance, reliability, and security.
This tutorial walks through a minimal 50‑line Python implementation of an AI Agent Loop, covering the core four‑step cycle, dual termination strategies, deterministic vs. autonomous designs, tool registration, and a complete runnable example.
The article dissects the concept of an Agent Harness— the full software infrastructure that wraps large language models—covering its definition, three engineering layers, twelve essential components, step‑by‑step execution loops, framework implementations, and key design decisions that determine whether an AI agent succeeds in production.
An AI Agent combines a large language model, tools, and memory to turn natural language requests into actions, with three core components—environment, sensor, actuator—seven agent types, usage criteria, and guidance on selecting between Microsoft Agent Framework and Azure AI Agent Service, plus runnable demos.
The article breaks down how AI agents extend large language models by adding tools, memory, and looping mechanisms, explains the ReAct paradigm and its evolution, compares agents to traditional workflows, and outlines product perspectives, coding advantages, current maturity stages, and typical use‑case categories.
The article defines Agent Harness as the full software infrastructure that wraps LLMs to enable stateful, tool‑using agents, breaks it down into twelve concrete components, compares implementations from Anthropic, OpenAI, LangChain and others, and outlines key engineering decisions that affect performance, safety and scalability.
This article walks you through building a Claude‑style AI programming agent from the ground up, breaking the architecture into twelve incremental versions, explaining the universal agent loop, tool integration, planning, memory compression, concurrency, and multi‑agent collaboration with concrete code examples in Python, Java, Go, and TypeScript.
This article reorganizes a 12‑part Deep Research Agent series into a logical learning path, summarizing each part’s problem, key solutions, and practical takeaways—from building a runnable loop and handling tool failures to data construction, context management, and training evaluation.
Anthropic has open‑sourced a set of eleven Knowledge Work plugins for Claude, each tailored to specific professional roles—from productivity and sales to finance and bio‑research—providing Markdown‑based skills, slash commands, and connector configurations that let the model integrate with tools like Slack, Notion, HubSpot, and Snowflake, while remaining fully customizable via simple file edits.
The article explains why the stability of AI agents depends on the harness rather than the model, outlines a five‑layer production‑grade harness architecture (Environment, Tool, Control, Memory, Evaluation), and presents five engineering principles to build a reliable, observable, and maintainable agent runtime system.
SciAgentGym introduces a type‑safe, reproducible, and extensible environment for evaluating large language model agents on multi‑step scientific tool use, revealing that while tool integration raises overall success rates, performance drops sharply on long‑chain tasks, and that training on executable trajectories (SciForge) can substantially improve results.
The article analyzes AI agents' technical layers, compares monolithic, multi‑agent, and workflow‑mixed architectures, outlines six evaluation criteria—including task complexity, tool compatibility, observability, cost, security, and team stack—and offers scenario‑specific recommendations and practical pitfalls for enterprise adoption.
The article dissects the concept of an Agent Harness – the full software infrastructure that wraps LLMs to enable autonomous agents – covering its definition, three concentric layers, twelve production‑grade components, step‑by‑step loop execution, framework implementations, and key design trade‑offs that determine performance and reliability.
Over the past half‑year the author built dozens of AI Skills, discovering twelve common traps—from over‑relying on prompts and bloated skill sets to vague descriptions, hidden token costs, knowledge placement, security gaps, and the need for proper evaluation—offering concrete guidance to avoid them.
This guide explains the three core Function Calling parameters—tools, tool_choice, and parallel_tool_calls—showing how to design tool schemas, control model autonomy, choose parallel execution, and avoid common pitfalls, with concrete Python and JavaScript examples and a cross‑platform comparison.
The article explains how the Harness framework acts as an operating system for LLM‑driven programming agents, enforcing permissions, step limits, state management, and human‑in‑the‑loop controls to make AI agents reliable, auditable, and safely interruptible.
Understanding the timeless principles behind AI agents—rather than chasing the latest frameworks—requires mastering 30 core concepts, from the fundamental Think‑Act‑Observe loop and state management to configuration files, workflow caching, sandboxing, and multi‑agent orchestration, enabling predictable, cost‑effective, and secure automation.
The article dissects the concept of an Agent Harness— the full software infrastructure that surrounds large language models—explaining its layers, twelve essential components, step‑by‑step execution loop, framework implementations, and key design decisions that determine production‑grade AI agent performance.
This article presents a complete knowledge map of the AgentScope Java framework—including its core components, API quick‑reference, and a high‑frequency Q&A self‑test—to help developers understand and master multi‑agent orchestration on the JVM.
This article explains why LangChain4j is needed for advanced Java AI agents, compares its capabilities with Spring AI, walks through project setup, configuration, defining tools and memory, assembling the agent, and demonstrates a complete smart‑customer service example with testing commands.
The article dissects the concept of an Agent Harness— the full software infrastructure that wraps LLMs— covering its definition, three engineering layers, twelve essential components, the step‑by‑step ReAct loop, and how major frameworks like Anthropic, OpenAI, LangChain, CrewAI and AutoGen implement these patterns, while highlighting practical trade‑offs and validation strategies.
The article dissects Agent Harness—the full software infrastructure that wraps LLMs—covering its definition, the 12 production‑grade components, orchestration loops, memory and context management, error handling, validation strategies, and key design decisions that differentiate successful production agents from fragile prototypes.
This article breaks down the complete cognition‑action system of modern AI agents into six inter‑connected layers—Perception, Planning, Tools, Memory, Execution, and Feedback—explaining their core problems, engineering designs, common pitfalls, and best‑practice metrics with concrete code examples and real‑world use cases.
The final installment reviews the current AI agent ecosystem, forecasts emerging standards such as MCP and A2A, consolidates best‑practice guidelines for development, prompting, tool design, cost control and security, lists common pitfalls with debugging tips, and recaps the twelve‑episode series with a roadmap for further skill advancement.
This tutorial walks Go developers through creating a fully functional AI Agent using Eino, covering knowledge‑base indexing with VikingDB, defining retrieval, weather, and web‑search tools, assembling a ReAct graph, running interactive queries, and adapting the demo for production environments.
This article presents a detailed, end‑to‑end design for an AI‑driven business‑requirement expert Agent that automates the full lifecycle—from intake, clarification, and planning through implementation, testing, code review, acceptance, deployment, and post‑release feedback—while outlining the four‑layer architecture, tool integration, and remaining challenges.
This article explains the concept of Function Calling in large language models, walks through defining function schemas, shows step‑by‑step API call flows, demonstrates multi‑tool orchestration, parallel execution, tool‑chain composition, and integrates Function Calling with LangChain, while providing best‑practice guidelines and code examples.
The article analyzes how code—broadly defined as any executable or machine‑checkable artifact—acts as the core harness that connects large language models to the real world, detailing its roles in reasoning, acting, environment modeling, planning, memory, tool use, multi‑agent collaboration, and the safety challenges that arise.
This article explains how the Model Context Protocol (MCP) standardizes AI model access to external tools like file systems, databases, and browsers, showing how to integrate ready‑made MCP servers with Spring AI, expose your own tools as MCP servers, and choose between stdio and SSE transports.
The article analyzes Harness Engineering by breaking down the five layers of Agent systems—Model, Tool, Skill, Sub‑agent, and Harness—showing how to design a workspace that not only runs agents but also enables verification, hand‑off, correction, and the disciplined removal of outdated constraints.
This article clarifies the core concepts of AI agents, distinguishing models from agents, defining scaffolding and harness, and detailing the roles of context engineering, policy, tools, skills, sub‑agents, and training components such as environment, rollout, reward, and trainer.
Even with 1‑million‑token windows in models like DeepSeek‑V4, GPT‑5.5, and Claude Opus 4.7, agents often underperform because noisy or poorly ordered context overwhelms the model, making careful Context Engineering essential for reliable results.
This article breaks down the concept of an Agent Harness—a complete software infrastructure that surrounds large language models—covering its definition, three engineering layers, twelve core components, step‑by‑step execution flow, and the trade‑offs that determine production‑grade performance.
Hermes Agent is an open‑source, self‑hosted AI agent framework that combines a layered persistent memory system, automatic skill generation, a unified tool registry, and multi‑platform messaging gateways, enabling agents to retain knowledge across sessions and continuously improve their capabilities.
The article explains why a robust harness is critical for production AI agents and walks through seven core components—control loop, state management, memory, tool integration with a bash escape hatch, context management, planning, and error handling—providing concrete code examples, pitfalls, and a step‑by‑step guide for developers.
The article breaks down the distinct roles of MCP, CLI, and Skills in enterprise AI agents, offers a three‑question framework for selecting the right approach, and explains how combining them—plus Programmatic Tool Calling—delivers scalable, token‑efficient automation in production environments.
When AI coding assistants like Codex are launched in a project without proper context, they often modify the wrong code, run incorrect commands, misplace files, or ignore project conventions; the article explains that this stems from missing project rules and shows how an AGENTS.md file can provide the needed guidance, improve efficiency, and avoid common pitfalls.
The article demonstrates how to turn ordinary Spring Boot methods into AI‑driven tools, enabling a language model to interpret a natural‑language request, orchestrate a multi‑step workflow (stock query, order creation, warehouse notification), and execute the entire business process without any hard‑coded if‑else logic.
This guide walks through integrating Ollama’s local LLM with Spring AI, explaining Prompt and PromptTemplate concepts, configuring application.yml, implementing a PromptService, adding controller endpoints, using external template files, and testing the setup with curl commands.
The article breaks down AI Harness Engineering into six indispensable parts—prompt system, tools & skills, infrastructure, orchestration logic, hooks & middleware, and model configuration—explaining their roles, concrete examples, common pitfalls, and how they together turn a powerful base model into a reliable, scalable workplace assistant.
The article explains a four‑layer capability model—Rules, Skills, MCP, and Agents—showing how to decide when to add an MCP server, a Skill, or a Rule, and how combining them yields reliable AI‑powered programming assistants for both personal projects and team‑scale engineering.
This article explains what an agentic loop is, contrasts it with single‑pass chatbots, outlines its five stages, shows a visual architecture, walks through a concrete multi‑step example, provides Python pseudocode and a LangChain implementation, and discusses when to use or avoid such loops.
This guide walks through every hidden Claude capability—from Projects that remember context, to Artifacts that generate runnable tools, Adaptive Thinking for step‑by‑step reasoning, Memory profiles, role‑setting prompts, Chrome extension, desktop Cowork app, scheduled tasks, Skills plugins, Claude.md rules, Claude Code, Claude Design, and Prompt Caching—providing entry points, activation steps, and ready‑to‑paste prompts so you can enable each feature in minutes and reap daily productivity gains.
The article traces the evolution of AI agents from early ReAct‑style models through workflow‑based systems to autonomous and self‑evolving agents, analyzing six core dimensions—Prompt, Planning, Memory, Tools, Workflow, and Environment—and explains why each paradigm shift occurred, citing recent frameworks and research.
The article provides a step‑by‑step guide on extending Codex beyond code generation by using persistent threads, voice input, task correction, queuing, tool integration, side‑panel displays, shared memory, and automation to create a continuous, context‑aware AI work system.
The article analyzes how Codex is evolving from a code‑writing assistant into a broader computer work system by leveraging durable threads, tool integration, voice‑based control, automations, and verifiable goals, shifting the focus from isolated code tasks to end‑to‑end workflow completion.
The article argues that Codex is evolving from a code‑writing assistant into a broader computer work system by adding durable threads, voice‑steering‑queuing controls, extensive tool integration, and verifiable goals, thereby shifting the key question from "can it write a function?" to "can it complete real‑world workflows?"
The article breaks down three complementary protocols—MCP for tool integration, A2A for agent‑to‑agent collaboration, and ACP for client‑to‑agent calls—explaining their layers, responsibilities, concrete examples, maturity levels, and a recommended learning order for developers building AI agents.
This guide explains the concept of AI Agent Skills, walks through creating, installing, and managing a Skill—including file structure, YAML metadata, progressive loading, platform-specific considerations—and introduces a one‑stop development assistant that streamlines Skill development and deployment.
This article walks you through the concept of AI Agent Skills, showing how to design, write, install, publish, and manage a Skill—from the underlying three‑level loading mechanism and cross‑platform considerations to best‑practice guidelines, versioning strategies, automated testing, and even self‑improving loops—so you can turn repetitive tasks into reusable, shareable automation assets.
The article argues that declaring LLM MCP obsolete while praising Skills as the ultimate capability reflects a beginner’s misunderstanding, explaining that MCP is a low‑level tool‑connection protocol akin to USB/HTTP, whereas Skills are high‑level business‑logic wrappers, and the real engineering challenges lie elsewhere.
The article argues that the performance gap of AI agents stems from the missing or poorly designed Harness layer, and explains how systematic engineering of prompts, tools, context strategies, hooks, sandboxing, and feedback loops can turn a raw model into a reliable, high‑performing autonomous agent.
The article dissects the concept of an Agent Harness—a comprehensive software infrastructure that wraps large language models to enable autonomous agents—detailing its three engineering layers, twelve production‑grade components, benchmark improvements, implementation patterns across Anthropic, OpenAI, LangChain, and design trade‑offs such as orchestration loops, tool integration, memory, context management, error handling, and safety.
The article demonstrates how combining three simple tools—fetch_url, write_file, and run_python—allows an AI agent to autonomously research web content, generate Python code, execute and debug it, and finally store the results, all within a single conversational loop.
This article examines the evolution of large‑model agents, outlines four development stages, compares workflow, collaborative, and evolutionary frameworks, details core components such as perception, memory, planning, tools, and reflection, and explains how a progressive, loop‑based architecture can be applied across verticals like research, code generation, and complex workflow automation.
This article dissects the internal architecture of three distinct AI agents—Anthropic’s Claude Code, the open‑source OpenClaw, and Nous Research’s Hermes—explaining their command layers, ReAct loops, instruction files, toolsets, memory systems, skill formats, extensions, and multi‑agent communication, and shows how to configure them for optimal performance.
This article explains why the Model Context Protocol (MCP) is becoming the universal AI interface standard, compares three implementation approaches, and provides a step‑by‑step tutorial for Java developers to create a production‑ready MCP server with Spring Boot, including tool definition, registration, controller, LLM integration, and best‑practice optimizations.
This article documents a complete hands‑on experiment linking OpenSpec and Superpowers, showing that while the initial spec proposal works, three critical integration points break—two fail outright and one never triggers—leaving the envisioned seamless, spec‑driven development pipeline unachievable.
The article explains that large‑model agents differ from traditional chatbots by perceiving goals, planning steps, invoking tools, and executing actions autonomously, covering their definition, core modules, ReAct reasoning‑acting loop, single‑ versus multi‑agent systems, current industry trends, and the reliability, safety, observability, and cost challenges they face.
The article explains how Agent Harness structures the interaction between user intent and LLM output, detailing its components, long‑conversation handling, layered memory, tool integration, and a four‑stage pipeline demonstrated by an Essay Harness prototype, highlighting design trade‑offs and practical implementation details.
The article explains that an AI Agent combines a large language model, a clear goal, and callable tools in a multi‑step reasoning loop, detailing its perception‑plan‑act architecture, differences from plain chat, common misconceptions, and practical questions for evaluating such systems.
OpenAI’s GPT‑5.5 delivers higher performance on complex coding, tool use, and professional workflows, but its token price is roughly twice that of GPT‑5.4; developers should adopt it for demanding, multi‑step tasks while keeping GPT‑5.4 for stable, cost‑sensitive workloads after real‑world testing.
This article walks through a practical, step‑by‑step construction of a LangChain agent—from basic concepts and a simple weather‑query agent to a more complex market‑research agent, adding memory and RAG capabilities, and finally comparing LangChain with LangGraph.
The article explains how Function Calling enables large language model agents to overcome knowledge staleness and hallucination by invoking external tools—such as search, email, code execution, and databases—to fetch real‑time data, perform actions, and deliver verifiable, multi‑step responses.
This article breaks down an AI agent's workflow—perception, planning, action, and memory—using a product‑launch example, explains reasoning methods like Chain‑of‑Thought and ReAct, details tool integration, memory types, common failure modes, and why planning and tool ecosystems are essential.
The article explains Anthropic’s 2025 Agent Skills standard, how it enables AI to perform actions such as database queries and API calls, and provides a detailed guide covering its definition, modular design, industry adoption, and practical usage scenarios.
The article compares three ways agents connect to external systems—direct API calls, CLI tools, and the Model Context Protocol (MCP)—and explains how MCP provides a standardized, scalable layer with rich semantics, authentication, and context‑saving techniques that enable production‑grade cloud agents.
The article identifies five often‑overlooked engineering pitfalls—unstable model output, fragile tool chains, memory loss, multi‑tenant interference, and uncontrolled autonomy—and provides concrete validation, tool‑tiering, external memory, isolation, and risk‑based execution strategies to reliably move AI agents from demo to production.
This article explains how LangChain agents use a ReAct (Reasoning‑Acting) loop to decide when and which tools to invoke, shows built‑in and custom tools via the @tool decorator, compares ReAct with GPT‑4 native tool calling, and demonstrates a research‑assistant workflow that cuts report drafting time from hours to minutes.
The article introduces agentic‑stack, a portable .agent folder that lets eight AI coding tools share a unified memory, skill, and protocol system, detailing its four‑layer memory model, progressive skill disclosure, shim‑based adapters, review protocols, practical team scenarios, installation steps, and architectural design.
Introducing MCP (Model Context Protocol), an open standard released by Anthropic that unifies AI model interaction with external tools, databases, and services through a USB‑like interface, the article dissects its design goals, architecture, message types, Python SDK implementation, client integration, production best practices, and future roadmap.
The article explains how conventional "smart" apps merely react to user commands, while AI Agents combine large language models, tool‑calling capabilities, and explicit goals to autonomously plan, act, and iterate, offering a new software paradigm with both promising use cases and current limitations.
This article provides an in‑depth, eight‑dimension comparison of Hermes Agent and OpenClaw, examining their core philosophies, learning abilities, integration options, deployment ease, security, standout features, overall strengths, and guidance on selecting the right AI agent for different user scenarios.
The article walks through building a minimal 20‑line agent loop, explains each step—from reading a task to invoking tools and feeding observations back—then shows how real systems like Claude Code, OpenClaw and Pi add layers of harness, memory, permission and validation to make the loop safe and reliable in production.
The article dissects the Agent Harness concept—covering its definition, three engineering layers, twelve production‑grade components, detailed orchestration loops, context‑management tricks, verification strategies, and how frameworks like Anthropic, OpenAI, LangChain, CrewAI and AutoGen implement these patterns, revealing why the same model can yield wildly different results.
The article explains that an Agent Harness is the complete runtime system surrounding a language model—handling the main loop, tools, context, state, permissions, and validation—and shows why this engineering layer, not the model itself, determines the stability and scalability of AI agents.
The article examines the rise and decline of the Model Context Protocol (MCP), outlines its four critical flaws—including context bloat, architectural complexity, security risks, and passive tool design—while presenting command‑line interfaces (CLI) as a more efficient, secure, and debuggable alternative for AI agents, and discusses hybrid approaches and practical implementations.
This tutorial walks through creating a LangChain‑based AI agent by covering model integration, tool definition with @tool, short‑ and long‑term memory handling via checkpointers and vector stores, and assembling everything with create_agent, middleware, and code examples for a functional travel assistant.
An AI Agent is a large‑model system that can perceive its environment, plan steps, invoke tools, and remember past interactions to autonomously achieve user‑specified goals, distinguishing it from simple chatbots that only answer questions.
