Letters from Andrew Ng

Personal messages to the AI community

When Guardrails Go Wrong: After a closed model went amok on a key vendor's system, an open weight model helped save the day

A few frontier labs have tried to tell a story of open models being dangerous because they can be used to launch cyberattacks, and of their “safe” proprietary models with strong guardrails being there to defend us.

Technical Insights

Make All Your Tokens (and Your Brainwork) Count: Agentic loop coding works until it fulfills a goal. Here's an agentic way to iterate on your goals to find the right one.

The key idea of agentic coding loops is to have an agent keep working until it satisfies a condition, such as achieving a product specification.

Technical Insights

Three Key Loops for Building Great Software

AI-assisted agentic coding reinforces iterative software development. These three loops can guide development and help you decide what to build.

Technical Insights

Agents on the Desktop

If you haven’t already, I encourage you to experiment with using AI agents not just to chat but to actually do work for you on your desktop.

Technical Insights

Coding Agents Accelerate Some Software Tasks More Than Others: Knowing how much coding agents accelerate different software-development tasks can help you put together the fastest teams

Coding agents are accelerating different types of software work to different degrees.

Technical Insights

AI-Native Software Development Needs Generalists: As AI accelerates software development, teammates must play a wider variey of roles

AI-native software engineering teams operate very differently than traditional teams.

Technical Insights

Let’s Help Agents Share Their Work: AI Social Networks Can Be More than Fun and Games

Should there be a Stack Overflow for AI coding agents to share their learnings with each other?

Technical Insights

Context for Coding Agents: Agentic coding systems often make mistakes because they’re not aware of tools, API calls, and the like that came out after they were trained. Context Hub gives them the documentation they need to write correct code.

I’m thrilled to announce Context Hub, a new tool to give to your coding agents the API documentation they need to write correct code.

Technical Insights

How to Test for Artificial General Intelligence: AGI has become a term of hype, and the traditional Turing Test can’t reliably detect it. How can we evaluate claims of that someone has built artificial general intelligence? Here’s a better test.

Happy 2026! Will this be the year we finally achieve AGI? I’d like to propose a new version of the Turing Test, which I’ll call the Turing-AGI Test, to see if we’ve achieved this.

Technical Insights

Large Language Models Are General — But Not _That_ General: Current progress in AI is piecemeal and laborious. Unforeseen breakthroughs may drive future progress, but the trend of improvement is incremental.

As amazing as LLMs are, improving their knowledge today involves a more piecemeal process than is widely appreciated.

Technical Insights

Build an Autonomous Agent Using This Simple Recipe!: The ability of large language models to carry out multiple steps autonomously makes it possible to build a capable agent in a few lines of code.

If you have not yet built an agentic workflow, I encourage you to try doing so, using the simple recipe I’ll share here!

Technical Insights

Tear Down Data Silos!: Many software-as-a-service vendors aim to hold their customers' data in silos. Their customers would do well to open the silos so AI agents can use the data.

AI agents are getting better at looking at different types of data in businesses to spot patterns and create value. This is making data silos increasingly painful.

Technical Insights

Improve Agentic Performance with Evals and Error Analysis, Part 2: Best practices for error analysis in agentic AI development, and how LLMs make them easier

In last week’s letter, I explained how effective agentic AI development needs a disciplined evals and error analysis process, and described an approach to performing evals.

Technical Insights

Improve Agentic Performance with Evals and Error Analysis, Part 1: When AI agentic systems go astray, it’s tempting to shortcut evals and error analysis. But these processes cas lead to much faster progress.

Readers responded with both surprise and agreement last week when I wrote that the single biggest predictor of how rapidly a team makes progress building an AI agent lay in their ability to drive a disciplined process for evals...

Technical Insights

Agentic Coding and Agentic Software Testing Go Together: Agentic coding can make mistakes, but agentic testing can find and fix them.

Automated software testing is growing in importance in the era of AI-assisted coding.

Letters from Andrew Ng

When Guardrails Go Wrong: After a closed model went amok on a key vendor's system, an open weight model helped save the day

Make All Your Tokens (and Your Brainwork) Count: Agentic loop coding works until it fulfills a goal. Here's an agentic way to iterate on your goals to find the right one.

Three Key Loops for Building Great Software

Agents on the Desktop

Coding Agents Accelerate Some Software Tasks More Than Others: Knowing how much coding agents accelerate different software-development tasks can help you put together the fastest teams

AI-Native Software Development Needs Generalists: As AI accelerates software development, teammates must play a wider variey of roles

Let’s Help Agents Share Their Work: AI Social Networks Can Be More than Fun and Games

Context for Coding Agents: Agentic coding systems often make mistakes because they’re not aware of tools, API calls, and the like that came out after they were trained. Context Hub gives them the documentation they need to write correct code.

How to Test for Artificial General Intelligence: AGI has become a term of hype, and the traditional Turing Test can’t reliably detect it. How can we evaluate claims of that someone has built artificial general intelligence? Here’s a better test.

Large Language Models Are General — But Not _That_ General: Current progress in AI is piecemeal and laborious. Unforeseen breakthroughs may drive future progress, but the trend of improvement is incremental.

Build an Autonomous Agent Using This Simple Recipe!: The ability of large language models to carry out multiple steps autonomously makes it possible to build a capable agent in a few lines of code.

Tear Down Data Silos!: Many software-as-a-service vendors aim to hold their customers' data in silos. Their customers would do well to open the silos so AI agents can use the data.

Improve Agentic Performance with Evals and Error Analysis, Part 2: Best practices for error analysis in agentic AI development, and how LLMs make them easier

Improve Agentic Performance with Evals and Error Analysis, Part 1: When AI agentic systems go astray, it’s tempting to shortcut evals and error analysis. But these processes cas lead to much faster progress.

Agentic Coding and Agentic Software Testing Go Together: Agentic coding can make mistakes, but agentic testing can find and fix them.

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