2026 AI Agent Guide: What to Learn, What to Use, and What to Avoid?

God Translation Bureau is a compilation team under 36Kr, focusing on technology, business, the workplace, life and other fields, and mainly introducing new technologies, new ideas, and new trends from abroad.

Editor's note: Seniority is dead. Stop competing over framework APIs. Mastering these "primitives" is the key to Agent development in 2026. This article is a compilation.

New frameworks, new lists, and new "tenfold efficiency" announcements are released every day. The question is no longer "How can I keep up with the pace?" but rather: Which are the real signals, and which are just noise disguised as a sense of urgency?

Every roadmap becomes obsolete a month after its release. The framework you mastered last quarter is now "legacy." The benchmark scores you painstakingly optimized have long been inflated and replaced. They teach us to follow the traditional path: learn the technology stack step by step, accumulate years of work experience, and get promoted slowly. But AI has rewritten this blueprint. Now, anyone with precise prompts and good aesthetics can deliver in one development cycle (sprint) what used to take an engineer with two years of experience to complete.

Experience still matters. Nothing can replace the experience of witnessing a system crash firsthand, troubleshooting a memory leak at 2 a.m., or insisting on a prudent solution over a speculative one and being proven right in the end. This "aesthetic sense" can generate compound interest. What no longer generates compound interest as it used to is mastering the API interfaces of the latest framework of the week. Everything will change in six months. Those who will succeed in two years are the ones who chose enduring "primitives" early on and let the rest of the noise fade away.

I've been deeply involved in this field for two years, received multiple job offers with an annual salary of over $250,000, and currently serve as the technical leader of a startup in stealth mode. If you ask me "What should I focus on now?", this is my answer.

This is not a roadmap. There is no end in sight for the agent field yet. Top labs are iterating publicly, pushing out potentially degraded versions to millions of users, writing post-mortem analyses, and patching in real-time. If the team behind Claude Code can release a version with a 47% performance decline and only discover it after community feedback, then the idea of a "stable underlying map" is pure fiction. Everyone is feeling their way forward. Startups are thriving because even the giants are confused. Non-programmers are teaming up with agents and delivering on Friday what a machine learning Ph.D. thought was impossible on Tuesday.

The most interesting thing about this moment is its impact on "seniority." The traditional path optimizes your seniority: degrees, junior positions, senior positions, and the slow accumulation of ranks. This made sense in an era when the underlying technology remained unchanged. But now, the ground beneath everyone's feet is moving at the same speed. The gap between a 22-year-old who publicly releases an agent demo and a 35-year-old senior engineer is no longer a decade of accumulated technology stack mastery. The canvas of the 22-year-old is as blank as that of the senior engineer, and for both, what can truly generate compound interest is the willingness to deliver and that small portion of "primitives" that won't become obsolete within a single quarter.

This is the foundation of the entire article. The following content will provide you with a way of thinking to help you identify which primitives are worth paying attention to and which announcements can be skipped. Take what you need.

Truly Effective Filters

You can't keep up with the weekly announcements. Nor should you try. What you need is a filter, not an information stream.

In the past 18 months, five testing criteria have withstood the test. Use them to screen any new technology before adding it to your technology stack.

Will it still be important in two years? If it's just a wrapper for a cutting-edge model, a CLI parameter, or a "Devin in a certain field," the answer is almost no. If it's a primitive (such as a protocol, memory model, or sandbox solution), the answer is often yes. Wrapper applications have a short half-life, while primitives have a half-life of several years.

Have people you respect used it to create real things and documented it honestly? Marketing copy doesn't count; post-mortem analyses do. A blog titled "We tried X in a production environment, and these things broke" is worth ten release announcements. In this field, the truly valuable signals are always written by those who have dedicated an entire weekend to it.

Does adopting it require you to discard your existing link tracing, retry mechanisms, configurations, or authentication? If so, it's a framework trying to become a platform. The mortality rate of such "framework platforms" is as high as 90%. A good primitive should be embedded in your existing system like a plug-in, rather than forcing you to make a complete relocation.

What's the cost of skipping it for six months? For most announcements, the cost is zero. You'll understand it better in six months, and the winning version will be clearer. This test can help you filter out 90% of the announcements without any anxiety, but most people refuse to do it because they think skipping means falling behind. That's not the case.

Can you measure whether it really helps the agent? If not, you're just guessing. Teams without an evaluation set (evals) operate based on intuition and end up delivering degraded versions. Teams with an evaluation set can let the data speak and tell them which is better for a specific task this week, GPT-5.5 or Opus 4.7.

If you can only develop one habit from this article, it's this: Whenever a new thing is announced, write down "What do I need to see to believe it's important in six months?" Then come back and check later. In most cases, the problem will be solved, and you'll have spent your energy on things that can generate compound interest.

The core skill that underpins these tests is actually hard to name, which is: being willing to seem "uncool" in the face of technologies you didn't choose. The framework that goes viral on Hacker News this week will have a large group of followers in the next two weeks, and everyone will sound knowledgeable. Six months later, half of these frameworks will be unmaintained, and those followers will have moved on to new favorites. Those who didn't participate saved their attention for the "boring" things that remain stable after the hype fades. This attitude of restraint, observation, and saying "Let's see in six months" is the real professional skill in this field. Everyone can read release announcements, but almost no one can stay calm and unaffected.

What to Learn

Concepts, patterns, and the outlines of things. These are the ideas that can bring compound interest returns. They can withstand model changes, framework iterations, and paradigm shifts. If you understand them deeply, you can pick up new tools on any weekend; if you ignore them, you'll always be relearning superficial mechanisms.

Context Engineering

The most important rebranding in the past two years is that "prompt engineering" has become "context engineering." This transformation is substantial, not just a superficial modification.

The model is no longer the object for which you write clever instructions, but the object for which you assemble a "working context" at every step. This context includes system instructions, tool definitions (schemas), retrieved documents, previous tool outputs, staging area states, and compressed historical records. The behavior of the agent is an "emergent" property of all the information you put into the window.

Internalize this: context is state. Every irrelevant noise token will degrade the reasoning quality. "Context rot" is a real production issue. In a ten-step task, by the eighth step, the initial goal may have been buried in the tool outputs. Teams that deliver reliable agents actively summarize, compress, and prune. They version-control tool descriptions. They cache static parts and refuse to cache changing parts. They view the context window in the same way a senior engineer views memory (RAM).

Here's an intuitive way to experience it: Select any agent in a production environment and enable full tracking logs. Look at the context at the first step and then at the seventh step. Count how many tokens are still in use. You'll be ashamed the first time you do this. Then you'll fix it, and the reliability of the same agent will significantly improve without changing the model or prompts.

If you can only read one related article, read Anthropic's "Effective Context Engineering for AI Agents." Then read their post-mortem analysis of multi-agent research, which uses data to show how important context isolation is at scale.

Tool Design

Tools are the intersection of agents and your business. The model selects tools based on names and descriptions; retries based on error messages; and determines success or failure based on whether the "contract" of the tool matches the LLM's strengths in expression.

Five to ten well-named tools are better than twenty mediocre ones. Tool names should read like English verb phrases. Descriptions should include when to use and when "not" to use the tool. Error messages should be feedback that the model can act on. Feedback like "Exceeded the maximum limit of 500 tokens. Please try summarizing first" is far more effective than "Error: 400 Bad Request." A team in a public study reported that they reduced the retry loop by 40% just by rewriting error messages.

Anthropic's "Writing Tools for Agents" is a good starting point. After that, add monitoring to your tools and observe real-world invocation patterns. The biggest breakthroughs in agent reliability almost always occur on the tool side. People always keep fine-tuning prompts but ignore the real leverage points.

Orchestrator-Subagent Pattern

The debates about multi-agents in 2024 and 2025 finally reached a consensus, which is the solution that everyone is delivering now. The "naive multi-agent system" where multiple agents write to a shared state in parallel will fail miserably due to error accumulation. The scalability of a single-agent loop is stronger than you think. In a production environment, only one form of multi-agent system works: an orchestrator agent delegates narrowly scoped, read-only tasks to isolated subagents and then aggregates their results.

This is how Anthropic's research system works, and it's also how Claude Code's subagents work. Spring AI and most production-grade frameworks have now standardized this pattern. Subagents receive a small and focused context. They cannot change the shared state, and only the orchestrator has the write permission.

Cognition's "Don't Build Multi-Agents" and Anthropic's "How we built our multi-agent research system" may seem completely opposite, but they are actually describing the same thing in different words. You can read both.

Use a single agent by default. Only when a single agent encounters real bottlenecks - such as context window pressure, delays in serializing tool invocations, or when task heterogeneity really requires a focused context - should you consider the "orchestrator-subagent" pattern. Building a multi-agent system before feeling these pain points will only add unnecessary complexity.

Evaluation and Gold Datasets

Every team that delivers reliable agents has its own evaluation system (evals). Teams without an evaluation system will definitely have unreliable agents. This is the most leveraged habit in this field and also the most under-invested aspect among all the companies I've seen.

Effective practices: Collect tracking data from the production environment, label failure cases, and treat them as a regression test set. Add new failure cases whenever they occur. Use "LLM judgment" for subjective parts and exact matching or programmatic checks for the rest. Run this test before making any changes to prompts, models, or tools. Spotify's engineering blog mentioned that their judgment layer rejects about 25% of agent outputs before delivery. Without it, a quarter of the poor results would be directly presented to users.

The mental model to make this concept sink in is: evaluation is like unit testing, which ensures that the agent remains "honest" when everything at the bottom changes. When the model is updated, the framework releases a breaking change, or the vendor abandons an interface, your evaluation system is the only thing that can tell you whether the agent is still doing its job. Without it, the correctness of the system you write will depend on whether a moving target still has "good intentions."

Evaluation frameworks (such as Braintrust, Langfuse evals, LangSmith) are all great, and none of them are bottlenecks. The real bottleneck is to have a labeled dataset first. Start building it from day one, and do it before scaling to any size. You can manually label the first fifty cases in an afternoon. There's no excuse not to do it.

File System as State and the "Think-Act-Observe" Loop

For any agent that performs real multi-step tasks, the enduring architecture is: think, act, observe, and repeat. Use the file system or structured storage as the source of truth. Every action is recorded and can be replayed. Claude Code, Cursor, Devin, Aider, OpenHands, goose. Everyone eventually ends up in the same place, and it's no coincidence.

The model is stateless, but the runtime architecture (harness) must be stateful. The file system is a stateful primitive that every developer can understand. Once you accept this framework, all the runtime specifications (such as setting checkpoints, recoverability, subagent verification, sandbox execution) will naturally emerge as you take this pattern seriously.

The profound lesson behind this is: in any production-grade agent that justifies the computing bill, the runtime architecture does more work than the model. The model is responsible for choosing the next action, while the runtime architecture is responsible for verifying the action, running it in a sandbox, capturing the output, deciding the feedback content, deciding when to stop, when to set checkpoints, and when to generate subagents. Replace the model with one of similar quality, and a good runtime architecture can still deliver stably; but if you replace the runtime architecture with a poor one, even the best model in the world will create an agent that often forgets what it's doing.

If what you're building is more complex than a simple single-step tool invocation, then the runtime architecture is where you should invest your energy. The model is just one component.

Understanding the Concept of MCP

Don't just learn how to call the MCP server; learn its model: the clear separation between agent capabilities, tools, and resources, as well as the underlying scalable authentication and transmission schemes. Once you understand this, any other "agent integration framework" you see will seem like an inferior version of MCP, which will save you time in evaluating them.

It is now managed by the Linux Foundation, and major model vendors are also supporting it. The metaphor of "the USB-C interface in the AI world" now sounds more like a statement of fact than a sarcasm.

Sandbox as a Primitive

Every production-grade code agent runs in a sandbox. Every browser agent has suffered from indirect prompt injection. Every multi-tenant agent has had permission scope bugs at some point. Treat the sandbox as infrastructure, not a feature to be added when customers request it.

Learn the basics: process isolation, network egress control, key scoping, and authentication boundaries between agents and tools. Teams that rush to make up for security issues after a customer security review usually lose orders; while those that build it in from the first week can easily pass enterprise procurement audits.

Technology Selection

These are specific recommendations as of April 2026. These may change, but at a slow pace. Choose the "boring" but robust solutions.

Orchestration

LangGraph is the default choice for production environments. About one-third of large companies running agents are using it. Its abstraction method matches the real form of agent systems: typed states, conditional edges, persistent workflows, and human-machine collaboration checkpoints. The downside is that it's verbose, but the upside is that this verbosity corresponds to the details you really need to control in a production environment.

If you mainly use TypeScript, Mastra is the de facto choice. It has the clearest mental model in this ecosystem.

If your team loves Pydantic and wants to make type safety a first-class citizen, then Pydantic AI is a reasonable choice. It released v1.0 at the end of 2025 and is gaining momentum.

For native features of specific vendors (such as computer use, voice, real-time interaction), use the Claude Agent SDK or OpenAI Agents SDK within LangGraph nodes. Don't try to make either of them the top-level orchestrator for a heterogeneous system. They perform best in their respective niches.

Protocol Layer

MCP, nothing else. Build your tool integrations as MCP servers and consume external integrations in the same way. The MCP registry has developed to the point where you can almost always find an existing server before you start building. In 2026, building a custom tool pipeline is just a waste of money.

Memory

Choose based on autonomy, not popularity.

Use Mem0 for conversational personalized recommendations to handle user preferences and lightweight history. For production-grade conversational systems with evolving states and entity tracking requirements, choose Zep. When an agent needs to maintain consistency over days or weeks of work, choose Letta. Most teams don't need these, but for those that do, they are essential.

A common mistake is to introduce a memory framework before encountering memory problems. Start with what the context window can hold plus a vector database. Only add it when you can clearly describe a failure case that can be solved by a memory system.

Observation and Evaluation

Langfuse is the default choice in the open-source field. It's self-hostable,