Introducing Remote Agent - cloud-based dev agents with full-codebase context, deep IDE integration, and full toolchain access. Available in VS Code.

PS: That’s why we built Prompt Enhancer —it auto-pulls context from your codebase and rewrites your prompt for clarity. Available now in VS Code & JetBrains: just click ✨ and ship better prompts, faster.

TL;DR:. 🛠️ Tools help.📁 File paths help.🧠 Rationale helps.📌 Examples help. Agents don’t need perfect prompts. They need complete ones.

Examples work too—but only if they’re scoped:. ❌ “Implement tests for ImageProcessor.” .✅ “Implement tests for ImageProcessor.Follow the pattern in text_processor.py.”. Now it knows what “good” looks like.

A high-context prompt answers the silent questions:. - Where should I look?.- What else is relevant?.- Are there examples I can follow?.- What’s the user really trying to do?. Agents don’t ask. So you have to pre-answer.

Here’s what a low-context prompt looks like: “Enable JSON parser for chat backend.”. Sounds fine, right?. Until the Agent:.- Picks the wrong file.- Misses the LLMOutputParsing class.- Uses a config that doesn’t exist. Because you didn’t ground it.

Prompting isn’t about being clever. It’s about giving the Agent everything it needs to not guess. That means:.- Clear goals.- Relevant files.- Helpful examples.- Precise constraints. Agents don’t hallucinate randomly—they hallucinate from missing context.

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 don’t need praise. They need signal. So next time something’s close? Don’t just delete and retry. Nudge. Reinforce. Keep building.

Why this works:. - Encouragement makes prompts more grounded.- Corrections become more specific.- The model anchors on what not to change. It’s the difference between rewriting from scratch…. vs. iterating like a teammate.

You can also course-correct gently: “Almost right. But this loop doesn’t handle timezones. Try again with that in mind.”. The agent takes the correction and retains the confidence. That balance matters.

When something works, say so: “Nice — this covers the main use case. Let’s just handle the null edge case too.”. This encourages continuation, not correction. It keeps momentum high — and quality stable.

Agents don’t “learn” like humans. But they do respond to signals in the prompt history — especially in long sessions. That means your feedback helps shape what comes next.

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 👇.

If you're building with agents — or trying to — it's a must-watch. 🔗

🎥 We just dropped a candid conversation between @guygr and Howie Xu (@SiliconANGLE & @theCUBE with @NYSE ) on exactly this. They cover:.→ How the dev role is evolving.→ What multi-agent systems mean for teams.→ How agents are writing mission-critical code today.

This shift isn’t speculative. It’s how Augment builds software every day. If you're wondering what this actually looks like in practice. .

To really harness agents, you have to:. – Treat them like junior devs, not magic boxes.– Break down tasks, prompt clearly, review deliberately.– Think like a tech lead, not just a contributor.

So why are so many teams still on the sidelines?. Not because the tools aren't good enough. Because using them well requires a new mindset.

Agents today can:. ✅ Understand your repo.✅ Write features, fix bugs, add tests.✅ Investigate alerts across GitHub, Notion & Linear.✅ Help ship production code — at scale. This isn't the future. It's now.