When I went to the first AI Engineer Summit in October 2023, AI agents were a joke. At that point, agents hadn’t proven they could consistently do even basic tasks. But fast forward just two years, and agentic technology has made big strides — albeit it’s still relatively unproven as completely autonomous software. Regardless, agents were the biggest development story of 2025 and “agentic” was the word of the year (again).

In terms of AI technologies in full blossom now, the Model Context Protocol (MCP) was everywhere in 2025 — running an MCP server has become almost as popular as running a web server. Other AI developments in 2025 included the rise of AI coding tools (and yes, “coding agents” are a trend now too), the massive influx of developers thanks to vibe coding, and AI infrastructure becoming developer-friendly.

Let’s take a closer look at five of the biggest AI development trends of 2025.

1. The Rise of Agentic Technology in Software Engineering

Although agents had been talked about last year, 2025 was when talk became action. It probably started when OpenAI launched Operator as a research preview on January 23, 2025. It was pitched as an AI agent capable of using its own web browser to perform tasks like filling in forms, making purchases, and scheduling appointments. Operator was followed in July by ChatGPT agent, which boasted of an internal “virtual computer” for task execution.

Meanwhile, enterprise IT departments began to get a handle on agentic technology. In February, I profiled a company called Orby that was promoting its Large Action Model (LAM). CTO Will Lu explained that LAMs take actions as input — giving as examples application screenshots, webpage HTML content, user interactions (such as mouse clicks and keyboard inputs). He told me that Orby’s LAM can use that context to automate complex enterprise workflows.

However, enterprises are being cautious about rolling out agentic systems. Brian Wald, who heads up GitLab’s Field CTO team and is in regular contact with enterprise IT departments, told me in May that enterprises are focused on structured implementations of agents, rather than open experimentation. Enterprises are not letting every developer freely use AI agents, he said. Instead, they’re forming centralized “AI enablement” teams, which often overlap with platform engineering or DevOps teams.

Wald added that enterprise IT teams typically have strict policies around data privacy, IP protection, and model hosting.

In terms of frameworks and tools to build agents, we saw a flurry of launches over 2025: OpenAI’s AgentKit and Agents SDK, Anthropic’s Claude Agent SDK, Google’s Agent Development Kit (ADK) and Vertex AI Agent Builder. We’re also seeing experiments happening with distribution — such as MIT’s decentralized Project NANDA and Microsoft’s Magentic marketplace.

2. MCP Becomes the Standard for LLM and API Integration

Last November, Anthropic launched the Model Context Protocol (MCP), an open source protocol designed to streamline how AI models access data, tools and services. The goal was to make MCP the universal method for AI agents to trigger external actions — and that’s basically what happened over the course of 2025, as nearly every company adopted the protocol.

With such broad support, this month Anthropic moved MCP into a newly founded open source foundation under the Linux Foundation: the Agentic AI Foundation (AAIF).

In March, I interviewed Speakeasy CEO Sagar Batchu, whose company provides a tool called MCP Server Generation to automate the creation of MCP-compatible servers. He pointed out that until MCP arrived, integrating an API with an AI model had been challenging. Many AI-based API integrations failed, he said, because models lacked the necessary schema information to make sense of API responses. MCP solves this by structuring API interactions in a way that AI can understand, making integrations more reliable.

That said, we have to also acknowledge the security risks of MCP. According to Gil Feig, CTO of agentic tool provider Merge, “developers learned the hard way that rapid adoption can pose serious security and reliability challenges, and no trend exemplified this more than the popularity of MCP servers.” He added that “MCP’s flexible architecture created a Wild West of potentially untrusted code, where community-published servers could be backdoored or abandoned, and blanket access to sensitive services like email and CRMs became common.”

3. The Evolution of AI Coding Tools into Coding Agents

As agents and MCP became the hot technologies this year, developer tools quickly adapted. By the end of the year, coding tools had moved on from “mere” autocomplete functionality to full-on agentic coding. Although, as my colleague David Eastman pointed out in his review of AI coding tools in 2025, “we still want to limit what an agent can do (especially on your machine) and where they can do it.” So agentic coding software isn’t fully trusted at this point.

Nevertheless, tools like Warp, Gemini CLI and Verdent (created by TikTok’s ex-algorithm chief) all aimed to convert developers to agentic systems this year. But even the people running these companies acknowledge that they won’t be replacing developers any time soon.

“This was supposed to be the year AI replaced developers, but it wasn’t even close,” Warp CEO Zach Lloyd told The New Stack. “What actually happened is developers became orchestrators of AI agents — a role that demands the same technical judgment, critical thinking, and adaptability they’ve always had. Prompt engineering doesn’t cut it.”

Bob Walker, Field CTO at Octopus Deploy, added that AI coding tools are no excuse for lack of development expertise. “Developing a critical thinking skill set has become more important than ever,” he told us. “The same is true with understanding the fundamentals of how the language or framework of choice works.”

4. The Influx of New Developers Known as ‘Vibe Coders’

The fourth trend somewhat undercuts the points made by Lloyd and Walker. Vibe coders aren’t necessarily skilled programmers — in fact, the whole idea of vibe coding is to let the AI system do the coding for you.

Yet both Vercel and Netlify (two of the leading web developer platforms) have let it be known that their user bases have massively increased this year. And it’s all because of vibe coders. What’s happened over 2025 is that the definition of a “developer” has expanded to include people who rely on prompting rather than programming.

One key problem with vibe coding is that the code generated isn’t necessarily reliable. When OpenAI launched GPT-5 in August, the company claimed it “excels at front-end coding.” But GPT-5 didn’t necessarily live up to the hype for developers. In an update to its State of Code Report on LLM personalities, the code security company Sonar concluded that GPT-5 was not, according to its tests, the leader in coding performance. It noted that GPT-5 generates a “larger and more complex volume of code than any other model,” which makes it “a serious challenge to review and maintain.”

The other issue that GPT-5 highlighted was its tendency to default to React code, simply because it’s the most popular frontend framework. But React code is notoriously bloated and complex — meaning vibe coders are even less likely to understand it.

5. The AI-ification of DevOps and Developer Infrastructure

DevOps tooling also adapted well to the AI era this year, with new AI serverless products, container technologies, and other “AIOps” solutions.

In January, I wrote about a company called Replicate, which sells a solution that wraps AI models into containers. One of the founders was previously the creator of Docker Compose, Ben Firshman. A key benefit of Replicate is that it allows developers to customize, fine-tune and tinker with open source LLM models. As Firshman explained on the Latent Space podcast, “the whole point of open source is that you can tinker on it and you can customize it and you can fine-tune it and you can smush it together with another model.”

In November, Replicate was acquired by Cloudflare. So, consolidation of AI devops tooling has already started to happen.

We’ve also seen middleware enterprise solutions emerge for agents. In August, I interviewed Oren Michels, CEO of a new AI company called Barndoor. Michels’ bet is that managing AI agents is the new API management — and he should know, as he was previously founder of Mashery, an API management company during the Web 2.0 era.

Conclusion

A lot happened in the relatively new field of AI engineering in 2025, but there’s also a sense that the tools and development practices are a little fragile and immature. From MCP’s security risks to the not entirely believable claims that fully autonomous agents are just around the corner, AI development still has a lot to prove.

It’s also unclear at this point how viable “vibe coding” will be in the long term, with issues around code quality and code maintainability leaving plenty of room for skepticism.

All that said, AI clearly became the biggest disruptor in software engineering over 2025 — and the ructions will continue well into 2026.

The post AI Engineering Trends in 2025: Agents, MCP and Vibe Coding appeared first on The New Stack.

The 2025 Typed Python Survey, conducted by contributors from JetBrains, Meta, and the broader Python typing community, offers a comprehensive look at the current state of Python’s type system and developer tooling. With 1,241 responses (a 15% increase from last year), the survey captures the evolving sentiment, challenges, and opportunities around Python typing in the open-source ecosystem. In this blog we’ll cover a summary of the key findings and trends from this year’s results.

Who Responded?

The survey was initially distributed on official social media accounts by the survey creators, and subsequently shared organically across further platforms including Reddit, email newsletters, Mastodon, LinkedIn, Discord, and Twitter. When respondents were asked which platform they heard about the survey from, Reddit emerged as the most effective channel, but significant engagement also came from email newsletters and Mastodon, reflecting the diverse spaces where Python developers connect and share knowledge.

The respondent pool was predominantly composed of developers experienced with Python and typing. Nearly half reported over a decade of Python experience, and another third had between five and 10 years. While there was representation from newcomers, the majority of participants brought substantial expertise to their responses. Experience with type hints was similarly robust, with most respondents having used them for several years and only a small minority indicating no experience with typing.

Typing Adoption and Attitudes

The survey results reveal that Python’s type hinting system has become a core part of development for most engineers. An impressive 86% of respondents report that they “always” or “often” use type hints in their Python code, a figure that remains consistent with last year’s Typed Python survey. 

For the first time this year the survey also asked participants to indicate how many years of experience they have with Python and with Python typing. We found that adoption of typing is similar across all experience levels, but there are some interesting nuances:

• Developers with 5–10 years of Python experience are the most enthusiastic adopters, with 93% reporting regularly using type hints.
• Among the most junior developers (0–2 years of experience), adoption is slightly lower at 83%. Possible reasons for this could be the learning curve for newcomers (repeatedly mentioned in later survey questions).
• For senior developers (10+ years of experience), adoption was the lowest of all cohorts, with  only 80% reporting using them always or often. Reasons for this drop are unclear, it could reflect more experienced python developers having gotten used to writing Python without type hints before they were supported, or possibly they are more likely to work on larger or legacy codebases that are difficult to migrate.

Overall, the data shows that type hints are widely embraced by the Python community, with strong support from engineers at all experience levels. However, we should note there may be some selection bias at play here, as it’s possible developers who are more familiar with types and use them more often are also more likely to be interested in taking a survey about it.

Why Developers Love Python Typing

When asked what developers loved about the Python type system there were some mixed reactions, with a number of responses just stating, “nothing” (note this was an optional question). This indicates the presence of some strong negative opinions towards the type system among a minority of Python users. The majority of responses were positive, with the following themes emerging prominently:

• Optionality and Gradual Adoption: The optional nature of the type system and the ability to adopt it incrementally into existing projects are highly valued, allowing flexibility in development.
• Improved Readability and Documentation: Type hints serve as in-code documentation, making code clearer and easier to read, understand, and reason about for both the author and other developers, especially in larger codebases.
• Enhanced Tooling and IDE Support: The type system significantly improves IDE features like autocomplete/IntelliSense, jump-to-definition, and inline type hints, leading to a better developer experience.
• Bug Prevention and Code Correctness: It helps catch errors and subtle bugs earlier during development or refactoring, increasing confidence and leading to more robust and reliable code.
• Flexibility and Features: Respondents appreciate the flexibility, expressiveness, and powerful features of the system, including protocols, generics (especially the new syntax), and the ability to inspect annotations at runtime for use with libraries like Pydantic/FastAPI.

Challenges and Pain Points

In addition to assessing positive sentiment towards Python typing, we also asked respondents what challenges and pain points they face. With over 800 responses to the question, “What is the hardest part about using the Python type system?” the following themes were identified:

• Third-Party Library/Framework Support: Many respondents cited the difficulty of integrating types with untyped, incomplete, or incorrect type annotations in third-party libraries (e.g., NumPy, Pandas, Django).
• Complexity of Advanced Features: Advanced concepts such as generics, TypeVar (including co/contravariance), callables/decorators, and complex/nested types were frequently mentioned as difficult to understand or express.
• Tooling and Ecosystem Fragmentation: The ecosystem is seen as chaotic, with inconsistencies between different type checkers (like Mypy and Pyright), slow performance of tools like Mypy, and a desire for an official, built-in type checker.
• Lack of Enforcement and Runtime Guarantees: The fact that typing is optional and is not enforced at runtime or by the Python interpreter makes it harder to convince others to use it, enforce its consistent use, and fully trust the type hints.
• Verbosity and Code Readability: The necessary type hints, especially for complex structures, can be verbose, make the code less readable, and feel non-Pythonic.
• Dealing with Legacy/Dynamic Code: It is hard to integrate typing into old, untyped codebases, particularly when they use dynamic Python features that do not play well with static typing.
• Type System Limitations and Evolution: The type system is perceived as incomplete or less expressive than languages like TypeScript, and its rapid evolution means syntax and best practices are constantly changing.

Most Requested Features

A little less than half of respondents had suggestions for what they thought was missing from the Python type system, the most commonly requested features being:

• Missing Features From TypeScript and Other Languages: Many respondents requested features inspired by TypeScript, such as Intersection types (like the & operator), Mapped and Conditional types, Utility types (like Pick, Omit, keyof, and typeof), and better Structural typing for dictionaries/dicts (e.g., more flexible TypedDict or anonymous types).
• Runtime Type Enforcement and Performance: A significant number of developers desire optional runtime type enforcement or guarantees, as well as performance optimizations (JIT/AOT compilation) based on the type hints provided.
• Better Generics and Algebraic Data Types (ADTs): Requests include features like higher-kinded types (HKT), improved support for TypeVarTuple (e.g., bounds and unpacking), better generics implementation, and official support for algebraic data types (e.g., Result, Option, or Rust-like enums/sum types).
• Improved Tooling, Consistency, and Syntax: Developers asked for an official/built-in type checker that is fast and consistent, a less verbose syntax for common patterns like nullable types (? instead of | None) and callables, and better support/documentation for complex types (like nested dicts, NumPy/Pandas arrays).
• Handling of Complex/Dynamic Patterns: Specific missing capabilities include better support for typing function wrappers/decorators (e.g., using ParamSpec effectively), being able to type dynamic attributes (like those added by Django/ORMs), and improved type narrowing and control flow analysis.

Tooling Trends

The developer tooling landscape for Python typing continues to evolve, with both established and emerging tools shaping how engineers work.

Mypy remains the most widely used type checker, with 58% of respondents reporting using it. While this represents a slight dip from 61% in last year’s survey, Mypy still holds a dominant position in the ecosystem. At the same time, new Rust-based type checkers like Pyrefly, Ty, and Zuban are quickly gaining traction, now used by over 20% of survey participants collectively.

When it comes to development environments, VS Code leads the pack as the most popular IDE among Python developers, followed by PyCharm and (Neo)vim/vim. The use of type checking tools within IDEs also mimics the popularity of the IDE themselves, with VS Code’s default (Pylance/Pyright) and PyCharm’s built-in support being the first and third most popular options respectively.

How Developers Learn and Get Help

When it comes to learning about Python typing and getting help, developers rely on a mix of official resources, community-driven content, and AI-powered tools, a similar learning landscape to what we saw in last year’s survey.

Official documentation remains the go-to resource for most developers. The majority of respondents reported learning about Python typing through the official docs, with 865 citing it as their primary source for learning and 891 turning to it for help. Python’s dedicated typing documentation and type checker-specific docs are also heavily used, showing that well-maintained, authoritative resources are still highly valued.

Blog posts have climbed in popularity, now ranking as the second most common way developers learn about typing, up from third place last year. Online tutorials, code reviews, and YouTube videos also play a significant role.

Community platforms are gaining traction as sources for updates and new features. Reddit, in particular, has become a key channel for discovering new developments in the type system, jumping from fifth to third place as a source for news. Email newsletters, podcasts, and Mastodon are also on the rise.

Large language models (LLMs) are now a notable part of the help-seeking landscape. Over 400 respondents reported using LLM chat tools, and nearly 300 use in-editor LLM suggestions when working with Python typing. 

Opportunities and Next Steps

The 2025 Python Typing Survey highlights the Python community’s sustained adoption of typing features and tools to support their usage. It also points to clear opportunities for continued growth and improvement, including:

• Increasing library coverage: One of the most consistent requests from the community is for broader and deeper type annotation coverage in popular libraries. Expanding type hints across widely used packages will make static typing more practical and valuable for everyone.
• Improving documentation: While official documentation remains the top resource, there’s a strong appetite for more discoverable and accessible learning materials. Leveraging channels like newsletters, blog posts, and Reddit can help surface new features, best practices, and real-world examples to a wider audience.
• Clarify tooling differences: The growing variety of type checkers and tools is a sign of a healthy ecosystem, but can also reflect a lack of consensus/standardisation and can be confusing for users. There’s an opportunity to drive more consistency between tools or provide clearer guidance on their differences and best-fit use cases.

To learn more about Meta Open Source, visit our website, subscribe to our YouTube channel, or follow us on Facebook, Threads, X, Bluesky and LinkedIn.

Acknowledgements

This survey ran from 29th Aug to 16th Sept 2025 and received 1,241 responses in total.

Thanks to everyone who participated! The Python typing ecosystem continues to evolve, and your feedback helps shape its future.

Also, special thanks to the Jetbrains PyCharm team for providing the graphics used in this piece.

The post Python Typing Survey 2025: Code Quality and Flexibility As Top Reasons for Typing Adoption appeared first on Engineering at Meta.

Drew Hagen, the release lead for Kubernetes 1.35, discusses the theme of the release, Timbernetes, which symbolizes resilience and diversity in the Kubernetes community. He shares insights from his experience as a release lead, highlights key features and enhancements in the new version, and addresses the importance of coordination in release management. Drew also touches on the deprecations in the release and the future of Kubernetes, including its applications in edge computing.

Do you have something cool to share? Some questions? Let us know:

- web: kubernetespodcast.com

- mail: kubernetespodcast@google.com

- twitter: @kubernetespod

- bluesky: @kubernetespodcast.com

Links from the interview

• Kubernetes v1.35: Timbernetes (The World Tree Release)