AI agents shouldn’t have to solve the same problem twice. I connected EverOS Cloud to my workflow through its API and tested it with a real memory retrieval flow. I saved a debugging experience about a storefront crash caused by missing or null product prices, including the suggested fix:... validate prices before formatting and add regression tests. Later, I asked a different question about the same issue, and EverOS retrieved the earlier debugging context. Same problem, saved experience, smarter retrieval. That’s the power of persistent memory. 🧠 Try EverOS Cloud:show more

Fantastic hands-on API demonstration! Transforming resolved production bugs into callable memory prevents redundant root-cause analysis and makes AI-assisted maintenance far more reliable.

Asking a different question about the same issue is the right way to test this. Retrieval that only works when you repeat the original wording isn't much of a memory. A null-price crash is also a very relatable bug.

The saved fix is what makes it useful: validate before formatting, then add regression tests. That's a reusable lesson, and it's much better than a note that just says "storefront crashed once." #EverMind #EverOS

Nice that you used a real retrieval flow and not a staged one. Curious how it behaves when two similar bugs are saved, say a null price and a null discount. Does it return the right one? #EverMind #EverOS

I've fixed the same missing-data crash in different places more than once, and each time the reasoning was gone. Having the cause and the fix come back when I describe the symptom differently would save real debugging time.#EverMind #EverOS

That's great

The part I care about most is whether an AI can learn from failed attempts In real projects the fix is often easy once you know what went wrong The expensive part is wasting another hour rediscovering the same cause I’d love to see memory become a normal part of every agent workflow rather than something developers have to manage manually

This is the killer use case. Save once, reuse forever. Debugging storefront crash + null prices is perfect example for persistent memory.

EverOS Cloud 这个检索机制看起来很聪明，把调试经验结构化存起来，后续相似问题自动带出上下文，比单纯聊天记录强多了。

"Shouldn't solve the same problem twice" is exactly the pain point. Saving a debugging experience and pulling it back later is a great real-world test. How does retrieval decide which past experience is relevant when a new bug looks similar but has a different root cause?

In the video, after saving that debugging experience, it pulls it up next time — EverOS’s memory retrieval is genuinely practical.

This is the exact use case that sells persistent memory. AI agents shouldn't have to solve the same problem twice - 100% agree. You saved debugging experience: storefront crash caused by missing/null product prices, fix is validate before formatting + add regression tests. Then asked a different question about same issue and EverOS retrieved the earlier context. That's the difference between RAG and real experience memory. Save once, retrieve when it matters, even if phrased differently. Great real test!

Perfect example: Same problem, saved experience, smarter retrieval. Storefront crash -> missing/null price -> validate before format. Saving that debugging experience via API and having it auto-retrieved later for a related question is exactly why persistent memory matters. No more solving the same bug twice. That's the power of EverOS Cloud.

The real win? Your agent can pick up where the team left off, so every solved challenge makes the next one a little easier. 🧠🚀 #EverOS #EverMind

The real win is retrieving the fix when the question changes, not just storing the original solution. That’s how persistent memory can reduce repeated debugging work.

换个问法还能把老上下文翻出来，这点比「存」本身更考验检索。很多记忆系统是存了但查不到，这套在视频里跑通了从「不同问题」到「同一份经验」的链路，这才是持久记忆的硬功夫。

Love this flow - saved fix (validate before formatting + regression tests) and actually retrieved it on second ask. That's how agents should work.

Using a null price crash as the test case is smart, because it's a bug that recurs across codebases. The real check is whether retrieval still surfaces it when the new error message is worded differently.

Sounds great

Imagine AI agents getting better at handling recurring issues instead of starting from scratch every time. This is a promising step toward smarter workflows! 🚀

The missing product price bug is a great example. Saving the fix and regression test approach could save a lot of debugging time later.

Saving real debugging experience and reusing it later is a huge upgrade! 🚀

把调试经验存下来，换个问法还能自动翻出老上下文，这持久记忆是真省事儿。

好奇 API 接进去之后，检索是按场景召回还是按关键词，这俩体验差很多。

Look crazy good

回归测试 这一步其实比记忆更稳，同一个坑测试会直接拦住

把踩過的坑存起來、之後還能自己撈回來，確實省心。像價格為空這種低級錯誤，偏偏最容易再犯一次。

This is where persistent memory becomes really useful. 🔥 Retrieving past debugging solutions can save time and help agents avoid repeating the same mistakes.

Persistent memory is a game-changer for AI agents. Saving solutions is useful, but retrieving the right experience at the right time is what really matters! 🧠🔥

Interesting 🧐

Been running something similar for a few weeks. One thing that bit me early: stale memories. If you save a fix and the codebase moves on, retrieval can pull outdated context and send the agent down the wrong path. Worth adding a timestamp or version check before trusting it.

Interesting

The missing-price example is useful because the saved experience includes both a fix and regression tests. I’d want those tests to distinguish a missing or null price from a valid price of zero, so the next agent can prevent the crash without accidentally rejecting free products.

The missing/null price case is a good detail to save. Did the retrieved context also distinguish a missing price from a valid zero price? That seems like an easy edge case to miss when reusing the validation fix.

换个问法还能把老上下文翻出来，这点比「存」本身更考验检索。很多记忆系统是存了但查不到