How AI Agents Got Here: Where We Thought the Bottleneck Was

This English version was machine-translated from the Chinese original. Technical terms may require verification.

It's written in front.

This article was originally...What are we solving when we build AI Agent?In the chapter. The article wants to answer three questions:

• When you're building an AI Agent, what are you doing?
• When you want to solve a problem in a real scene with AI Agent, you have to solve your own problem?
• Why did an AI Agent project succeed and why failed?

These three questions are not visible in the last six months, so before answering, we need to go back to 2024 and see the way AI Agent came. The chapter was later extended to an unsuited level and was then taken apart and made separate. If you want to see the answers to those three questions, please return to the original; here's to the two years of AI Agent itself.

Time to come.

In the last two years, AI Agent was not a straight line forward. It's more like a string of speculations: we keep guessing about where the bottlenecks are, and then we put most of our manpower and budget in the position that we're guessing.

Each guess will produce a set of tools, a set of terms, posts and projects. When this layer is filled up or the model is gradually eliminated, the next layer becomes apparent. A small fraction of the last batch was deposited into infrastructure, with the remainder rapidly devalued.

Let us look at what we have discovered, what we have been guessing and what we have proposed in the past two years. They all answer one of the three questions, not the final answer.

If you're good at Agent, you can skip this chapter and just look at the title. I wrote that more to clean up my thoughts.

Enchant Engineering

ChatGPT's release is a landmark moment. We spent years finding a mathematical structure that was suitable for language sequence modelling, and we invented hardware infrastructure that matched its parallel calculations, while turning these mathematical structures into a very low threshold, extremely broad-based interaction -- chat. Without any foundation, ChatGPT would not have been so successful.

Since we have a talking robot, what else can we do? It seems that only what to say is decided. And then it's not good to believe that you're right to say it, and it's not good for AI to do it; it's not good for you to think that it's a model. And so talking became a technique, and the spell that AI was able to output better results became a science, and we called it Prompt Engineering, and the person who studied it was called Prompt Engineering, as if it had been paid well.

Prompt Engineering lasted a little longer. It seems to be just talking, but all kinds of technology are being put forward on this basis, and the classics are COT, Few-shot, Role-Play; and for the fun ones, take a deep breath, tip, threat, PUA, and re-emerge in the Skill era. It was never out of date to make the task clear, and we are still doing Prompt Engineering, just not talking about it anymore.

Prompt Engineering has a principle of Agent development that is still in place today: to make the matter clear. This noun is not wrong, but because we don't need a special Prompt Engineer, and everybody is Prompt Engineeringer.

Technical details of this paragraph may be consulted." Intense engineering and context learning "If you're curious about Spec's relationship to this section, you can refer to it.Spec is not a new paradigm.。

Knowledge enhancement and RAG

RAG is doing the AI Agent had to talk about. If you've done AI Agent between 2023 and the third quarter of 2025, then RAG is a 70% loop that can't be bypassed.

It is based on a simple idea. Training data for models have deadlines and most of the data within companies cannot be used for training; the output rate is better by giving a related material Prompt to the model. So, a tool that automatically looks for materials and then stuffs them into Prompt, it's quite natural to grow. The judgement then was equally natural: models were not unfeasible, but knowledge was probably the biggest bottleneck of the system.

So RAG became the default answer for those years. Vector databases receive significant financing, almost all traditional databases are retrieved, and each company is building its own internal knowledge base. The splitting strategy, rerank, hybrid search, Graphrag, RAPTOR, Agentic RAG, have been evolving.

The search itself is not wrong. Putting the material it needs before Agent does, remains one of the most effective practices today. In 2026 or more, as long as AI training technology itself does not revolution, RAG will not die.

But RAG is designed to be one-way (Agenda RAG, better, but still limited information is available to be able to flow back). Document, slice, vector, recall, spell Prompt, information flows forward into the context and stops there. After this mission, Agent, nothing went back. The three sections of the material recalled were useful, and the noise was the last one accepted or the whole rewritten, and the next time the same problem was encountered, it was not possible to find a search method, none of which was exported.

We spent two years doing the "how to get knowledge in" very finely, but few people did the "how to get the results back." This asymmetry, we will refer to it again in the main text.

If you're curious about RAG itself, you can refer to it.Text Embedding: From Word Bag Model to Qwen3 Embeddingand"AI Agent How to Get Information From the Internet"; how to be organized in context and memory after knowledge is sent in, can be seen first《Context is All You Need》andFrom Memory Generation to Memory GovernanceWe'll come back to that.

LangGraph, LangChain and WorkFlow

Workflow is organized as an AI Agent Dev topic that is also indissociable. This section wants to talk about what the whole community has been doing over the past two years, and why it is needed.

The first batch of systems was n8n and Langchain. And we actually lived in the automated paradigm of the past: a linear process, connecting different external services, computing, and then making decisions. LLM is a node in this process, covering those parts of the traditional script that cannot be solved, writing an e-mail, understanding a natural language and making decisions into new branches, etc.

And what next? The LangGraph, Diffy, systems like this, which replaces DAG with a loop map, are starting to appear, React, which allows LLM to recycle in a local process until the problem is solved. Very Powerful Tools, a circular machine would not have had that capability before LLM appeared. Loop has returned in the last two months, and in a sense it is similar. LLM got stronger, so the cycle got bigger and stronger.

As a contrast to the workflow, the multi-intellectual framework also has a little heat. Since models are so human, let them work like people. MetaGPT, ChatDev, AutoGen gave Agent product manager, architect, programmer and test hand-held cards to meet with SOP, deliver documents, and evaluate each other. It's hot, and soon there's no use for it. We're in the main.

Why is the workflow developed by far more people than more intelligent bodies? Why do firms make products with little choice of multiple intelligence? In a scenario that gives the process, a Runtme process can well absorb the uncertainty of LLM itself. It's a very good engineering idea to keep uncertainty out of the system in the age when models are wrong, and then Agent Harnesss does something similar, except that LLM is less uncertain and naturally less bound.

References From the cognitive structure of an intelligent body to the framework of an intelligent body Discussing the boundary between WorldFlow, Agen, Supervisor, Agent Team and MAS, and the engineering value and abstract cost of these frameworks, MetaGPT, AutoGen. Reading "Wondering the outer space for LLM to wear a definite shackles." Think about what to do to keep uncertainty out of the system.

Function Calling and MCP

A robot that can only be Chat is not what we want. While gathering information is a large part of human work, there is still another part of taking it to make decisions and implement certain action. Turning Next Token Predation into an interactive intelligence that directly helps us to get our jobs done, people to talk, AI to do it, looks like a very good future.

If you have ideas, you have to do it. The first of the first creations of the FUNCING was the ability to access basic external tools for the model by simple JSON Schema: modify files, execute codes, search information. These are common operations for people and are beginning to enter the AI capability range. In order to harmonize incompatible interfaces, Model Context Protocol was born and gradually became a widely compatible factual standard. As the model capacity continues to improve, the even lighter seal that wraps Prompt together with Tool is mainstreamed and used in large quantities. On the other side of the standardized interface, GUI Agent, with the maturity of VLM and Reassoning, Manus, which has been a project of this kind, has been difficult to further expand.

But is it enough to standardize tools and pile them up more?

SWE-agent and the ACI it brought together deserve our further reflection. The same model, a set of interfaces designed for Agent, and throw it straight to a naked shell, with a low score of less than the same system. The model hasn't changed, it can see, it can do, and it can get new information after it's done. The MCP rigid Tool Calling mechanism may be appropriate for models at the end of 2024, but a large number of tools are injected into information and round-cycles to use tools that gradually begin to corrose the context, and mitigate these problems by adapting to loads, programmable functions calls and the idea of Skyll Scripts.

SWE-agent can hardly say how much it's changed for industry, because it's not as good as MCP to be a protocol, an ecology, a program. The quality of interfaces is a trade problem, which is difficult to standardize and to write into the reporting material. But it is a direction of improvement in very high value for money: the tools are not as good as the tools, and the quality of the tools is probably more important for Agent than even the underlying models. Tools is never a list of tools, but AI Agent can see and influence the world.

The technology evolution of this line is a reference.Technical Evolution of the Use of the LLM Tooland《MCP (Model Context Protocol)》Why did Skills win again after MCP?From MCP to Agent SkillsI'm sorry. As for the last judgment of this section, it's possible to refer to it."AEnvirron: Age Dev Why Do You Need Interact Environment Layer?and"Harness what is it?"。

Context Arms Race and Context Engineering

Talk about Context Engineering after Prompt Engineering. This should be the word most Agent developers have heard. When we want an AI Agent to do more for us, whether it's a longer output in Chat or a multi-wheeler to solve the problem, Context is a problem that we can't get around. The early model is only 1k to 8k Context, and it is hard to imagine that a complex task in the eyes of a human being can be described and solved with thousands of words, and the emergence of CoT/RAG technology has exacerbated the problem of the context. Perhaps the complexity of the task is due to the fact that the context window is too small, and we have to try to solve it.

If the first principle is to be taken, then the first consideration in the context of the study must be to modify the model. The location code is changed from absolute to RPE, and the relative position code is then expanded without training by position plugs, NTK-aware, YaRN, etc.; the attention side is designed to bypass the square complexity of the length of the sequence, consider the thinness of the slide window, local and global rotation; and do the optimization of this IO project at the IO level. With a series of jobs and more calculus, the context window went from 8k to 128k, and now 1M, almost double every six months, and the Context budget became larger.

Context Engineering, of course, is not just the context. As we have the longer Context, Context Rot's problem surfaces: just long windows, stacking in, it doesn't work better. We need to view the context as a limited resource to move rather than fill it. Comparation, Subagent, Memoory systems were created, Skylls introduced incremental loading through the file system to reduce the input burden of the hints, MCP moved from full tool description to local injection as needed, and the tiered Memoory system was made a reality with the file system, and Context began to be managed dynamically. From the end of 2025 to the first half of 2026, the progress of Contex Engineering was the basis for this massive leap of intelligence, and people began to think seriously: whether knowledge should be in parameters, context, external, and how it should be correctly deployed.

The details of the training in this section can be seen first." Self-Regular Mechanisms and Transformer Architecture "andLLM Life Cycle OverviewThe price of the long window on the side of the reasoning is to be found.Why Output Token is more expensive: from KV Cache to Agent Cost ProjectI'm sorry. The real project after the window grew longer,"Context is All You Need: Context Project for Smart Bodies"It's much more elaborate than this; it's on the side of memory.From Memory Generation to Memory Governance: A Panorama of Age MemooryandAgent Memoory and Runtme Technical InventoryAnd these two are exactly where the RAG came back. As to why incremental loading is considered to be a tool protocol improvement, it can be looked back at.From MCP to Agent Skills。

Go back to the model itself and the new words.

This chapter is coming to the end, let's see what we've been doing in the half of 2026.

Let's see the model first. LLM has experienced a huge fall in the years since 2023, when it was asked whether it would be necessary to conduct further training in certain areas (generally Mid-Training to add knowledge). At the earliest, not as much as we talked about, Training was the only option for empowerment, for the field fit QA and for the Training almost equals. This is also a habit of the deep learning age that LLM had before, when the generalization of models was hard to trust.

As the LLM's ability to generalize has grown, people's views have gradually changed. We have even less to mention SFT, Lora, RL when we are doing engineering issues, and a lot of training has been put back on the ground and made part of the basic model. The Second Half suggests that we have entered a new era, trained Infra, matured, trained from algorithm to closed-ring engineering. Models become more and more user-friendly, and it seems that we can really trust the underlying models and make their own assessments rather than think about training first.

2026, Agent's big leap was Agent's own big leap? My answer is no, it's more anxiety and top-down driving. But the power of Agent is actually advancing at a very rapid pace, but it's not from Agent Harness, but from models more. Actually, a lot of Harnesses are patches, not necessary. The Harness job you did three months ago was removed free of charge from the next version of the model, which is the normal pattern for Argentina Dev.

We've gone from the Workflow to the Claw, and the rect cycle has not changed much since birth, and the model in the cycle, and with the new model, the simple Loop can win the WorkFlow.

Multiple Agencies starts to exit because model + context works allow individual Agent to finish it in one breath, and the division of labour starts to yield less than the loss and expense associated with the complexity of communication. People are beginning to try more dynamic, self-contained intelligent bodies with a certain degree of restraint, rather than a complete division of labour.

Loop and Goal emerged, and people wanted to get less and less involved, and to get a strong, autonomous Agent to achieve that end. It's a bigger R.A.C., and last year's Ralph, Loop, never was a new play.

Is Harness a whole new concept? The old WorkFlow was a Harness, but now that you're not developing the WorkFlow, you're more concerned about the autonomous Agent, and you need a new word to emphasize what we're doing, and we're not doing the same thing we did before.

2026 The first half of the year is not the half year that new things have emerged, but the half year that old things have finally been given shape and name. After the model had eaten the framework, the largest number of conventions had been found in different abstract models per company. The influx of new arrivals during the six months also requires us to find new names to facilitate discussion and reporting. Marketing is not a mere problem in itself, but a synergy between people and technology.

What is there to be used to train the line?LLM The Nature of Inference and TrainingWhy does it go from algorithm to closed-ring engineering?Actic RL: Why closed rings is more important than training algorithmsAnd the Second Half itself, it's a reference.From RL Agent to LLM AgentI'm sorry. The way the model is eating the frame, it's not the way it is.《Model Is Good Enough》That's what I'm talking about, and...《Claude Code or Codex》It is a specific slice of its product level: the same very simple Loop, and in the alternative, the experience is completely different."Harness what is it?"The whole thing is arguing that the word covers a whole bunch of old problems; the complete version of the multi-smart account is in theFrom the cognitive structure of an intelligent body to the framework of an intelligent bodyLee.

Benchmark and Evaluation

The last section of the road is a place of little attention.

Each floor has a name that is loud, tools, ecology, and things that can be set up. The floor is called Evals, which sounds like a process before delivery, so it is the least valued link of the entire chain for a long time. The real lesson of the big leap is that indicators replace targets, and Evals is the only thing that answers them positively.

Developers of halfway through the line are particularly vulnerable to skipping this level. Evals is always in the last chapter of the curriculum, and it doesn't produce Demo and the benefits that it can see, and there's nothing to report on. But the cost of skipping it will be returned once again.

Evals is actually connected to a lot of questions. Whether or not to change the model, Prompt is better or worse, whether or not the part where context is compressed is important, and the tool is useful to describe the change. Why do you say this is better than the last edition? Without Evals, these judgments are all based on feelings, and feel very unreliable on a probabilistic system, you'll be in place for a long time and you'll be in the same place.

So this is a low-level, and there is no concept that really sounds important, but it's part of Agent Dev. If you can, don't skip Evals, at least think about it when you're not busy.

About Evals, what should we do?《Demystifying evals for AI agents》From basic concepts to a zero-sum set of eval merits, I'm here to see what the engineering will decide and how it will be.What are we solving when we build AI Agent?The Evals section talks about their own practices.

It's written at the end.

Seven floors of speculation is over. Each layer was right at that time, and a number of tools, terminology and projects were produced; a small fraction of them are deposited into today's infrastructure, with the remainder rapidly devalued.

Look back at this road to answer the three questions: When you're building an AI Agent, what are you doing? You have to solve AI Agent's own problems first? Why did an AI Agent project succeed and why failed? My answer is...What are we solving when we build AI Agent?Lee. It's time to go back.