English | Chinese Original

English translation of the Chinese original. This version is generated for international readers and may be refined over time.

Date: 2026-04-07

Introduction: from "talking" to "doing things"

Remember that feeling? When I was in touch with ChatGPT, I thought it was interesting, and I could talk about anything, but at the same time I thought it was not right about the mouth of the horse, and sometimes I could even tell you black. But after a while, you'll find a more basic question -- it's good to say, but not very good to do. You let it write a plan, it gives you thousands of words; you let it really help you get things done, it stops.

Early generic large models have only ** generating capacity** and lack capacity for autonomous dismantling missions, continuous call tools and closed landings. But in 2026, AI Agent would turn it into a closed loop to complete a full set of processes.

The CEO of CB Insights recently put it in place: "AI Agent has changed from an experimental to an enterprise's priority in just two years. I have seen a tenfold increase in the number of references to Agent at the newspaper conference since 2023. This speed is something I have never seen before."

Eighty-two per cent of the enterprises indicated that they would apply AI intelligence to the client support area within the next 12 months. Five of the top 10 investment-financed transactions in 2025 were directly related to AI Agent, in more than 1, 500 technology break-down tracks. In other words, the hottest investment hotspot, half from the Agent concept.

It's not a bubble, it's a paradigm shift in productivity.

Today, I will show you the true face of AI Agent in 2026, from the three dimensions of technological principles, business landings, and developer practice.

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I. Technological rationale: the three pillars of efficient intelligence

It would be more intuitive to simulate AI Agent as a human employee. What capacity does it need? Understand the mandate, remember the context, call the tools, plan the steps and implement the landing. The corresponding technical core is three dimensions:** memory management**,** tool learning**,** planning reasoning**.

Memory management: the brain of an intelligent body

Why can't your AI Agent remember like a goldfish? Because memory management isn't good.

There are two layers of intelligent memory:

** Working Memory**, equivalent to a human workstation. All the task information currently being processed is here. The problem is that the context window is limited and you can't let Agent put the whole Red House Dream in. So there are two ways to optimize:

• ** Text compression**: Industry mainstream would be better stored with long text summaries, light quantitative memory compression programmes.
• ** Potential memory**: Some of the programmes will be achieved by optimising KV caches to speed up context reading, real long-term retention of invisible memories, or by a summary archive + vector bank.

** External memory**, equivalent to the "hard disk" of an intelligent body. The model itself can't handle things. Throw them outside. The most common is vector databases, where terminological similarities are retrieved, and where knowledge maps are useful, where physical relationships are organized to support multi-jump reasoning.

There is also a key issue in memory management:** the forgotten strategy**. Memory will grow indefinitely and there must be a mechanism for its elimination.

The rule-driven approach is low-cost but may miss important information; the LLM-driven approach is self-adapted but increases the cost of calculating. The hybrid strategy is the current mainstream - using rules to determine when the merger should be triggered and then using LLM for specific compression.

Tool learning: the smart body's "hands and feet"

AI Agent is not just a language model, it needs to really do something. This involves the ability to mobilize tools.

The evolution of tool learning is interesting. The early way is simple and rough -- give the model a list of tools that it decides which to call. The problem is that the model is always a little bit messy. It's supposed to check the database. It's called you a weather API.

Programmes are now more systematic. An overview of the three-stage framework for learning with tools, issued jointly by universities such as AI Lab in Shanghai and Reddan**:

-** Tool discovery**: Agent senses what tools are available. This requires a good tool registration and description mechanism. -** Tool selection**: Task given, Agent selects the most appropriate mix of tools. This test is the model ' s mission understanding capability. -** Tool alignment**: Agent knows how to call the tool correctly, how to fill the parameters and how to return the results.

The new agreement of interest in 2026 is ** MCP (Model subject Protocol)**. It's an open standard led by Anthropic, and you can interpret it as the "USB interface" of the AI model -- whatever type of AI, if you support the MCP, you can insert tools and data sources.

The core advantage of MCP is standardization. An MCP server has been developed and is available to all AI clients supporting MCP. A two-way communications capability allows servers to proactively send updates, which is important for a time-use scenario.

Planning reasoning: the mind of a smart body

How many steps do you put elephants in the fridge? Humans know three steps. AI Agent needs to learn how to decompose.

Planning capacity determines how complex an Agent can handle a task. The mainstream programmes include:

• Chain of Thought: Let the model articulate the reasoning, step by step.
• React: Switching between reasoning and action and adjusting next steps to the results of implementation.
• Tree of Thought: Exploring multiple possible paths and choosing optimal solutions.

But the biggest bottleneck in planning capacity is not "want to get" but ** cost**. By comparison with the single LLM dialogue, Agent has led to resource consumption at the index level as a result of the incremental transfer of memory, tools and planning. There's an image saying: OpenClaw, an Agent tool, has made many people "play for a week, hundreds of dollars gone".

Optimization is therefore a key issue. The core approach is to maximize mission success at fixed cost or minimize costs at the same level.

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Two, AI Agent is sweeping everything

Three Agent types: each with its own territory

There are currently three main types of AI Agent, each with different applications.

# Browner Agent: A master in web automation

The core capacity of Browner Argentina is ** to automate web pages to perform cross-platform tasks.** It understands the web interface, understanding buttons and input boxes, and then executes clicks, fills, submissions, etc., like people.

Typical application scenarios include: automated completion of complex web-page forms, aggregation of data from multiple sites, and manual duplicate web-page tasks for bulk processing. Imagine you don't need to do anything manually in back-office systems anymore, and Browner Agent can help you automate all the mechanized web operations.

Coding Agent: Independent completion of the entire process from demand to deployment

Coding Agent is the hottest track in the group of developers. It will be able to independently complete the full development process from needs analysis, code writing, test validation to deployment online.

It is now possible to understand the required documentation prepared by the product manager, generate code that conforms to project specifications, automatically prepare test examples and run them, deploy codes to the cloud environment, and even automatically screen and repair online problems. A three-person team was accompanied by several Coding Agents, the output of which was likely to compensate for the traditional development team of the previous 10 people.

Cursor, Windsurf, GitHub Copilot Workspace, Trae, Qoder, should be new to you.

##Multi-Agent Team: Multi-role collaboration to solve complex problems

The first two were single-handed, while Multi-Agent Team brought together multiple Agent teams to work together on complex issues.

For example, in a software development project, there might be an Agent who's in charge of architecture design, a front-end development, a back-end development, a test, a deployment, inter-Agent passage.A2A(Agent to Agent)Agreements communicate with each other, coordinate progress and share information.

The strong point of this model is that it can break the maximum capacity of individual Agents - complex missions are broken down into sub-missions, each of which is performed by the best Agent, and is eventually aggregated into full results.

# Digital talking: landing faster than expected

A study released by McKinsey in November 2025 showed that 78 per cent of the organizations worldwide had used some type of AI tool in their daily operations, and 85 per cent of them had integrated AI Agent into at least one workflow. This means that AI Agent has moved from an experimental tool to an enterprise-level practical phase.

The figures are even more surprising:

• 23 per cent of enterprises have deployed **Agenda AI in at least one business function within the enterprise
• 39 per cent of enterprises are in a pilot phase, with most of the scaleed deployments covering 1-2 functions
• In the area of finance and electronics, AI Agent has over 30% penetration
• 20% in relatively slow-down manufacturing

In 2025, the creation and deployment of AI Agent in Salesforce increased by 119 per cent, and the number of completed operations increased by approximately 80 per cent per month.

The data of the China Business and Industry Research Institute are more telling: the global AI smarts market was about $11. 3 billion in 2025 and $5. 1 billion in 2024 - more than doubled a year. China ' s market is growing faster, at around $6. 9 billion in 2025 and 2. 87 billion in 2024.

Industry infiltration: from passenger service to full link

The CB Insights report states that AI Agent will be in the business stream by 2026 and that industry-specific applications will accelerate landing.

** Client services** are currently the most mature applications. It is not funny that 82 per cent of enterprises plan to apply AI intelligence to client support in the next 12 months. Voice AI intelligents will be able to handle complex conversations and achieve zero manual intervention. Meta's successive acquisition of voice AI start-ups in 2025 has released signals of accelerated integration in the industry.

** Software development** was the first to be destabilized. Cursor earned $500 million in annual revenue, which was only established in 2022; Loveable and Mercor earned $100 million in annual income and only in 2023. This rate of growth is beyond the reach of traditional software companies.

** The financial, medical and retail sectors** are also being followed up quickly. Accompanying diagnostic scenarios such as focus image recognition and report generation in the medical field, with user buy-back rates exceeding 40 per cent.

From Copilot to Autonomous Age

Most of the current AI Agent is still in the "co-pilot" phase - operating in restricted environments, using structured workflows and "guards" to achieve specific objectives while retaining some decision control.

However, the trend is clear: basic modelling capacity is increasing, as is the autonomy of Agent.

The Google A2A agreement was prepared for this trend. When individual Agent has limited capacity, multiple Agents work together. Financial analysis Agent and code generated Agent's duties, and client service Agent's unmanageable issues were transferred to professional Agent - working as a human team.

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III. Developer core competitiveness: Harness Engineering

Why isn't it the "Trainer"?

In 2026, when IAgent was developed, many would ask: What is the framework for learning? What model should we use?

But the real problem is: it's already out of control. Models will get smarter, but they will continue to fail in an unexpected manner. Because the stronger the model, the more complex the task we give it, the more border-crossing.

A team observed a year of proxy development failures and concluded that this was not a model issue, but a ** configuration issue**.

coding agent = AI model(s) + harness

Your code Agent = AI Model + External Configuration. Both are equally important, and even in some contexts, the harness (outside configuration) determines success or failure.

This is the core idea of Harness Engineering: rather than looking for stronger models to solve all the problems, focus on how to maximize the capacity of the current ** model.

Harness Engineering

Harness Engineering describes a practice of customizing and improving the quality and reliability of Agent ' s output by adjusting its configuration points.

Which are the configuration points?

-Skills: static context files containing documents, modes, examples -MCP server: Run-time connection to external tools and data sources Sub-agents: Sub-agents sharing complex tasks

• Memory: long-term memory mechanisms -AGENTS.md document: Project Level Directive

Each point is worth digging. In the case of Skills, many do not understand why, sometimes, Agent does not trigger your Skill - the problem is almost never the content of Skill, but the trigger conditions for Skill are not set.

Skill's design has several key principles:

1. ** Clear trigger conditions**: under what circumstances should the Skill be called? Terms are described with precision.
2. ** Sufficient context**: the more stuffed, the better it is.
3. Enforceability: Give concrete steps, not abstract descriptions.

2026 Developer's Required Skills

Based on an analysis of current ecology, in 2026 AI Agent developers need core competencies:

** The protocol understanding capability**: A2A, MCP, and Skills constitute the infrastructure for the 2026 AI application. You don't need to know everything, but you need to understand their respective scenarios.

System design capability: Agent is not a single combat. You need to design a multi-Agent collaboration structure to consider how to split tasks, how to share situations, how to deal with anomalies.

** Prompt Engineering**: The term has been misspelled, but the core competencies have not changed - how to express intent clearly and how to impose effective constraints.

** Assessment and debugging**: Agent's implementation process is often black box. You need to put in place an effective evaluation system to know when Agent is in trouble and where.

** Cost awareness**: Token is real cost. You need to know how to balance effects and expenses and how to design efficient Agent systems.

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IV. Technological ecology: A2A, MCP and Skills

Distinction and linkage between the three core concepts

In 2026 AI Ecology, you need to understand four keywords:Agent,A2A,MCP,Skills.

Put them together:

• Agent is an implementer - a digital workforce capable of autonomous decision-making
• ** A2A** is the collaboration agreement between Agent - allows multiple Agents to communicate
• ** MCP** is the standard for Agent's connection to the outside world - to enable Agent to access tools and data
• Skills is Agent's professional skill pack - **Agent's knowledge and operations in a particular field

MCP and Skills are two different ways of expanding AI capabilities, the choice of which depends on the scene:

MCP is suitable for scenarios that require** real-time data** and** integration of external systems**, such as access to databases and access to internal API.

Skills is suitable for scenarios ** of specific areas of knowledge** and ** of practice**, such as corporate code codes, approval processes.

You're probably using both in the actual project.

How the A2A protocol works

The Google-led A2A agreement standardized collaboration between Agent.

The core mechanisms include:

Agent Card: Each Agent publishes its own "digital business card", stating its capabilities and endpoints.

{
 "name": "finance_analyzer",
 "capabilities": ["data_analysis", "report_generation"],
 "endpoint": "https://agent.example.com/a2a",
 "version": "1. 0"
}

** Mandated process**:

Services Discovery - Find Agent who can complete the mission Mandate consultation - confirmation of acceptance Implementation of surveillance - supporting flow-back progress Return of results - step or synchronized results

This means that you can build a multi-Agent system: the user says, "Help me develop an electrician's website" and then, after planning for Agent's dismantling mission, entrusts it to the front-end, back-end Agent, database Agent, and finally deploys Agent to go online.

Frame Evolution: From functional stacks to safe control

The mainstream framework (Langchain, Crewai, AutoGen, etc.) is undergoing a paradigm shift.

Early framework pursuits are functional and can do anything. The direction is now:

• ** Sandbox execution**: prevention of hazardous operations by Agent
• ** Permission control**: Agent can only access authorized resources -** Visibility**: implementation log, performance monitoring, debugging tool -** Enterprise level deployment**: containerization, high availability, resource management

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Five, 2026, six trend predictions

Trends I: Fundamental improvements in memory mechanisms

In 2026, AI Agent will achieve a critical breakthrough in long-term autonomy. Context window processing capacity will be increased by more than 10 times to support full software project development, cross-sectoral business processes, etc.

Short-term memory enhancement, long-term memory architecture, and self-evolutionary capacity - improvements at these three levels will give Agent a real "continuing job" capability.

# Trends II: Voice Accelerator Rise

The fastest growing early generation of talent, AI, focuses on AI Agent applications, especially voice AI development. Business is shaping the future of "human interaction with AI through dialogue rather than text interfaces".

Meta's successive acquisition of voice AI start-ups has released signals of industrial integration.

# Trends III: AI Merger and Acquisition Wave

The AI smart-body solution led the top AI exit in 2025. By 2025, more than 35 acquisitions had taken place between AI and the Copilot area. Business buyers are increasingly seeking comprehensive smart solutions.

# trend four: profit pressures spread

The reasoning model increased the number of tokens exported by about 20 times. This means that cost pressures spread from programming to other vertical areas. Start-ups need to rethink business models.

Trends V: More Agent collaboration mainstreams

Agent alone can't cover all the scenes. Multiple Agent divisions of labour - financial Agent processing data, code Agent being implemented, client Agent docking - will become standard architecture.

# Trends VI: the rise of AAI tools

Moving from traditional product+AI functions to tools and platforms built around AI functions. Such products are not intended to replace traditional software, but to redefine what is called "smart tools".

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Write at the end: 2026 is Agent's year of deployment

Recall the central message of this post:

-** Status**: AI Agent has moved from the experimental concept to the production deployment phase, and 72 per cent of enterprises have deployed Agent in at least one business process. -Types: Browner Agent, Coding Age and Multi-Agent Team have advantages, respectively, for automation, development and complex collaboration. -Technology: Rectangular model, tool call, memory system form Agent ' s technological triangle, making it truly capable of a sensory-drill-action-learning closed circle. -** Eco**: A2A (Agent collaboration agreement), MCP (model-tool connectivity criteria) and Skills (Professional Capability Package) have a clear division of labour - A2A allows multiple Agent to communicate, MCP allows Agent to access external tools, and Skills gives Agent knowledge in specific areas. These three constitute the infrastructure of the 2026 AI application. -** Developer capacity**: Harness Engineering is becoming core competitiveness - rather than looking for stronger models to solve all problems, focus on maximizing the ability to use current models. Skills trigger conditions, MCP server configuration, Sub-agents division of labour, emulation mechanisms, which determine the final performance of Agent. -** Path to land**: Enterprise import should go from Agent for employees to process Agentization and MultiAgent collaboration.

• ** Security floor**: security governance cannot be absent, sandboxes, authority, manual review, audit logs are standard.

2025 is the year of AI Agent Business, and 2026 is the year of Agent Deployment - a crucial year from pilot to scale.

At this turning point, who really opens the gap is not the strongest model, but:

• Who runs through the first Agent loop in his real business**;
• Who can replace ** reusable Harness** in the pit;
• Who can build a truly stable Agent system with flexible combinations A2A, MCP, Skills.

Technology won't wait until you're ready. But the good news is,** you don't have to wait till it's perfect.**

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Reference source

1. 15 AI Agent studies on the 2026 Agenic Al Industry Panorama with downloads - Zhihu Column - Search Engine
2. AI agent trends 2026 report | Google Cloud - Search Engine
3. In-depth analysis of Google 'AI Agent Trends 2026: successful AI projects... - Search Engine
4. 2026: Age of Agent - AI How to Reshape Productivity and Industrial Ecology - Zhihu Column - Search Engine
5. 2026. - Search Engine
6. AI Agent Enterprise Panoramic Guide 2026: From Technology Selection to Value Drop-NetEase - Search Engine
7. Big model retreats, AI Agent rises! - Search Engine
8. In 2026, AI Agent revealed six major trends: where is the next windfall after the programming boom? - Thirty-six krypton. - Search Engine
9. Agenic AI Ten Key Trends 2026: Technology, Applications and Governance - Search Engine
10. 2026 AI Agent Ecological Panorama Analysis: Technological evolution from single combat to intellectual collaboration... - Search Engine
11. Harness design for long-running application development - Anthropic - Search Engine
12. How to Build an AI Agent from Scratch in 2026: A Complete Step-by... - Search Engine
13. Harness Engineering: The Skill That Will Define 2026 for Solo Devs - Search Engine
14. Skills Required for Building AI Agents in 2026 - DEV Community - Search Engine
15. MCP vs Skills: Understanding AI Coding Assistant Integrations in 2026 - Search Engine
16. Skill Issue: Harness Engineering for Coding Agents - HumanLayer - Search Engine
17. Shipping Features to Close the AI Velocity Paradox - Harness - Search Engine