Augment Code and Opencode are now available in Zed. 🎉 Both agents run natively through ACP. Install them in seconds from the extensions store and use them alongside Claude Code, Gemini CLI, or Codex.

Today we announced an upcoming change to how Augment Code is priced. Starting October 20, 2025, plans will move from user messages per month → to a pool of credits that can be spent across activities. 📖 Read more on our blog → www.augmentcode.com/blog/augment...

Claude Sonnet 4.5 from Anthropic is now the default model for Augment Code. We’re rolling it out to all customers over the next 24 hours, where it will be available alongside Sonnet 4 (for a limited time) and GPT-5 in the model picker.

Tomorrow, Friday, August 29. 10am PT. AMA on r/webdev RSVP and leave your questions: reddit.com/r/webdev/com...

The Auggie CLI is now available for everyone. Auggie brings Augment’s industry-leading context engine to every part of your stack—terminal, CI, and beyond. Getting started is simple: npm install -g @augmentcode/auggie www.augmentcode.com/product/CLI

Our industry has moved from ‘still defining an agent’ at the start of 2025 to ‘building things that can actually build things’ by mid-year. Here are some of the agentic demos that impressed me over the year, including @augmentcode.com @kiro.dev and @systeminit.com redmonk.com/rstephens/20...

Most people think devs use AI to write code from scratch. But we analyzed 81 million developer chats — and that’s not what’s happening. Here's what we found 👇

Most Agent failures aren’t about bad models. They’re about bad prompts. Here’s how to write prompts that actually work—based on thousands of real dev-Agent interactions 👇🧵

With Augment Rules, your software agent can build just like your team does.

Prompt. Wait. It messes up. Start over? Not if you build a feedback loop. Here’s how to make Agents actually useful 👇

Most Agent failures aren’t model problems. They’re context problems. If you give vague or incomplete info, the Agent will fill in the blanks—and usually get them wrong. Here’s how to write high-context prompts that actually work 👇

Guess now we can predict Asia's lunch time 💀

Agents respond to feedback — just not how you think. The fastest way to improve output quality over time? ✅ Reinforce what works ✅ Redirect what doesn’t ✅ Keep the feedback loop tight Here’s how smart feedback makes agents smarter 👇

Big codebases aren’t just a size problem. They’re a speed problem. We just shipped a system that shrinks search time for 100M+ LOC by over 40% — while preserving 99.9% accuracy. Here’s how we scaled context retrieval without breaking performance: 🧵

Prompting can take you a long way. But there’s a point where better prompts stop producing better results. Here’s how to recognize when you’ve hit that wall — and how to move forward👇

We outperformed the top models on CoIR. But that wasn’t the goal. The goal was to help engineers find answers in their code, not ours. So we built a benchmark that’s harder, messier, and more honest. 👇

It feels natural to write "If X, then Y" when giving an instruction. For a language model, that pattern is unreliable — and it leads to subtle failures you might not notice at first. Here’s why it happens, and how to build prompts that actually work 🧵👇

We’re the first AI coding assistant to get ISO/IEC 42001 certified — the new international standard for AI risk, governance, and data handling. If you’ve ever had to explain “how your AI tool handles data,” this changes that conversation. Here’s what it unlocks 🧵

🆕 Mermaid diagrams in Augment Agent Agent reads your codebase, understands structure, and renders a Mermaid diagram—directly from your prompt.

Agents don’t crash. They drift. At first, it’s subtle. Then it’s a mess. Here’s how to catch drift early — and reset before you lose control: 👇

The secret to building reliable AI agents? Context is everything 🔥 We just shared 11 battle-tested prompting techniques that turn AI into a disciplined coding teammate. The biggest game-changer: The model's entire worldview comes from your prompt - make it complete and consistent.

Most AI benchmarks measure performance on synthetic puzzles. But developers don’t work on puzzles. They debug real, messy, cross-file, undocumented codebases. Here’s how we built a benchmark that reflects that 👇

Most people use Agents to write code. Fewer use them to understand it. Even fewer use them to think. But that’s where they quietly deliver the most value. Here’s how 👇

❌ “Write better prompts.” ✅ Just click Enhance ✨

Most Agent mistakes happen before the first line of code is written. Why? Because the task was too vague—or way too big. If you’re delegating work to an Agent, here’s how to do it right 👇

Working with legacy Java code? Upgrading a decade-old project doesn’t have to mean untangling build errors or rewriting everything by hand. Watch how the agent spiders through the codebase and upgrades a real-world Minecraft-related project, step by step: www.youtube.com/watch?v=MQRT...

Introducing Remote Agent - cloud-based dev agents with full-codebase context, deep IDE integration, and full toolchain access. Available in VS Code. Learn more: youtu.be/yp262nUnBLU?...

Introducing Remote Agent - cloud-based dev agents with full-codebase context, deep IDE integration, and full toolchain access. Available in VS Code. Learn more: youtu.be/yp262nUnBLU?...

AI coding tools can expose your IP. Before integrating one, ask these 4 questions 👇

APIs taught software how to talk. MCP teaches it how to act. This isn’t a small technical upgrade. It’s an architectural shift that redefines how agents interact with tools.

Is left vs right panel chat placement the new tabs vs spaces? Drop your take in the comments

Most AI agent devs start with the model and bolt on tools later. That’s backwards. Here’s a better way to structure agents with tools from day one—using MCP as your foundation 👇🧵

Most people talking about MCP can’t explain what it actually is. Here’s a simple breakdown of the Model Context Protocol—what it does, why it matters, and how it quietly became the new standard for AI agent tooling 🧵

Don’t trust the Agent? Good. You shouldn’t—yet. We summarized thousands of Agent sessions into 4 ways devs build trust—without risking their codebase 👇

We spent the past few months building a production-grade AI coding agent from scratch. Along the way, we learned what actually matters—and open-sourced the whole stack. Here’s what we’d want to know if we were starting today 👇

“Do I still need this code?” Claude-3.7: “Nope—deleted it, opened a PR, assigned a reviewer.” The jump from 3.5 to 3.7 shows how tuning is all about balance—helpful, but not too passive or too aggressive. Our co-founder, Guy Gur-Ari, explains more on the latest Modern CTO episode.

Every Agent will mess up, including ours. Here’s how to handle it without derailing your flow 👇

UI’s broken. It’s obvious on screen. But tracking down the cause? Hours of inspecting styles and guessing at layout logic. Drag the bug into our agent. It analyzes the UI, finds the root cause, and proposes a fix — with context.

Using an Agent isn’t just about what you prompt. It’s about how you work while it runs. Here’s the progression we’ve observed across tens of thousands of user interactions 🧵

Customer Managed Keys are now live for Enterprise Customers. Manage your own symmetric encryption key. Revoke it, and Augment loses access to your data—immediately. Cloud-native AI, now with full enterprise control 🧵

We built an AI agent that solves real GitHub issues. Along the way, we hit every wall you’d expect — and a few you wouldn’t. Here are 5 surprising lessons we learned (that might help you build your own) 👇

𝐆𝐏𝐓-𝟒.𝟏 𝐚𝐥𝐦𝐨𝐬𝐭 𝐭𝐨𝐩𝐬 𝐂𝐥𝐚𝐮𝐝𝐞 𝟑.𝟕 𝐨𝐧 𝐜𝐨𝐝𝐢𝐧𝐠?! New eval dropping using our #1 SWE-bench coding agent! - GPT-4.1 beats Gemini 2.5 Pro and almost tops Claude 3.7 Sonnet! - Even GPT-4.1 mini matches Claude 3.5 Sonnet V2 performance. It was the top model just 2mo ago!

LLaMa 4 beats Deepseek-R1 and V3 on coding?! Fresh evaluation using our #1 SWE-bench coding agent and >200,000,000 tokens of production-grade code: Claude 3.7 Sonnet > Gemini 2.5 Pro > LLaMA 4 Maverick > DeepSeek-V3 > Deepseek-R1

Testing @augmentcode.com this afternoon, and so far it seems to be better at understanding issues and thinking through them. Still early days but I am liking it. It's UI is also much more organised and easy to understand what's going on. Pros to the team! #codingagent #agenticIDE

Agents are powerful—but still new. We’ve learned a lot from early users. Here are 10 key takeaways to help you get the most out of them 🧵👇

Augment Agent is now live. Designed for large-scale codebases — with 200K context tokens, persistent memory, and deep tool integrations, Augment Agent is available in VS Code and JetBrains

For the love of code.

🧵 We just released the #1 open-source agent on the SWE-bench Verified leaderboard by assembling the best of Claude Sonnet 3.7 and O1. Open-source repo here: github.com/augmentcode/... Here's how we achieved 65.4% success rate on the hardest coding benchmark in the industry: 🧠👇

Thank you @nathanlabenz.bsky.social for having us on The Cognitive Revolution podcast! Our co-founder, Guy Gur-Ari, breaks down why enterprise codebases aren’t just “bigger projects”—and what it takes to build AI that can actually handle them. 🎧 Listen here: youtu.be/LYWkB6apLfw?...

Most code retrieval benchmarks optimize for synthetic problems. But professional software engineers don't solve artificial puzzles--they navigate large, often messy codebases.