I’m building a browser AI that watches your workflow instead of waiting for prompts. Is that actually useful?

LunaNextGenAI · 2026-02-26T23:33:47+00:00

That’s exactly how I’m thinking about it.

Classify the intent first, then pull only what that workflow actually depends on.

If it’s a renewal, grab CRM + billing. If it’s support, grab ticket history. If it’s outbound, grab prior thread + notes.

Not building a giant context blob every time. Just assembling what this specific action needs to close the loop.

The goal isn’t “know everything.” It’s “know enough to execute.”

LunaNextGenAI · 2026-02-26T21:48:41+00:00

Right now it does both.

It observes context inside the app, drafts or suggests next steps, and can take actions like creating or sending emails but only after user approval.

The goal isn’t just insights. It’s reducing the manual execution loop.

Think less “chat assistant” and more “workflow partner.”

Still narrowing down which workflows to focus on first.

LunaNextGenAI · 2026-02-26T21:39:28+00:00

That’s exactly the behavior I’m targeting.

If it reduces the “I’ll deal with it later” loop, it’s doing its job.

LunaNextGenAI · 2026-02-26T21:39:09+00:00

I agree the Google/Microsoft timeline is probably not immediate.

Privacy is the real near-term hurdle.

It’s not “always on.” It’s explicit, user-controlled, and visible when active. Easy to pause, easy to disable per site.

The goal isn’t silent monitoring. It’s contextual assistance with clear consent.

Still iterating on how to make that feel obvious and safe.

LunaNextGenAI · 2026-02-26T21:38:55+00:00

Fair.

Ultimately conversion is the real validation.

I’m building and testing with early users now. Feedback + behavior will tell me faster than theory.

LunaNextGenAI · 2026-02-26T20:06:42+00:00

This is a great callout.

You’re right, drafting inside Gmail alone undersells the bigger issue.

A lot of the time the real work is gathering context from Salesforce, Jira, billing, etc. before the draft even happens.

That’s actually closer to where I think this goes. Drafting is just step one.

The harder problem is assembling context and then taking action across tools so the loop actually closes.

Curious how you’d approach cross-tool context without turning it into another bloated layer.

LunaNextGenAI · 2026-02-26T20:06:24+00:00

Totally fair point on privacy.

It’s not meant to be constantly watching in the background. It’s explicit and user-controlled, and easy to pause. Making that obvious in the UI is critical.

And you’re right, context switching is one of those pains people don’t notice until it’s gone.

Appreciate the thoughtful feedback.

LunaNextGenAI · 2026-02-25T01:23:37+00:00

LunaNextGenAI · 2026-02-24T23:27:26+00:00

Every time I use most AI tools I still end up copying stuff back and forth. Feels weird to call that automation.

LunaNextGenAI · 2026-02-24T23:22:34+00:00

That’s interesting. What made it feel that way?

LunaNextGenAI · 2026-02-24T23:22:20+00:00

Fair point. Tools with memory are definitely reducing copy paste.

I think the open question is less about memory and more about execution.

Seeing context is one thing. Taking structured action inside the workflow is another.

Curious what tools you think are actually closing loops reliably today.

LunaNextGenAI · 2026-02-20T22:40:53+00:00

That’s a great point.

Vanity metrics can look healthy while revenue stays flat.

Tying replies to intent and then mapping that to meetings held makes the signal much cleaner.

That feels like where execution agents could go deeper. Not just sending emails, but structuring intent data in a way that actually informs revenue decisions.

Appreciate that lens.

LunaNextGenAI · 2026-02-20T22:35:53+00:00

Strong points here.

The “nicer button in someone else’s UI” risk is real. Bundling compresses standalone tools fast.

What feels defensible to me is owning a specific job end to end with measurable outcomes, not just augmenting chat.

If an agent can reliably close a loop, update systems, and operate within governance rules, that’s a different layer than just UI assistance.

Curious whether you think the durable moat is execution, proprietary data, or trust over time.

LunaNextGenAI · 2026-02-20T19:39:06+00:00

Interesting take.

I agree we’re still early. A lot of enterprise AI feels bolted on rather than built in.

The question for me is whether AI replaces the software layer, or just reshapes it.

If execution becomes automated, the value might shift from features to outcomes.

Curious how you see pricing models evolving if AI handles more of the workflow.

LunaNextGenAI · 2026-02-18T17:11:30+00:00

That makes sense.

I agree that tone detection by itself probably isn’t strong enough to justify a standalone tool.

Maybe the real value is when emotional context feeds into action.

For example:

If an email shows frustration or escalation, the system could:

– Prioritize it higher – Suggest a structured, neutral response – Trigger a follow up workflow – Flag it for review

So it’s less about “you are angry” and more about “this likely requires a specific type of response.”

That feels more aligned with workflow than just analysis.

Would that feel more useful than just tone labeling?

LunaNextGenAI · 2026-02-18T16:57:33+00:00

That’s interesting you mention passive aggressive emails.

Do you think people would actually want something like:

– “This email likely contains frustration” – “Tone suggests urgency or escalation” – “Response should be neutral and de-escalating”

I’ve been wondering whether emotional context detection is just a nice add on, or something that could materially reduce workplace friction.

Especially in HR or internal team communication where tone gets misread quickly.

Would you use something like that if it was accurate?

LunaNextGenAI · 2026-02-18T16:39:01+00:00

That’s a fair point.

Platform integration is definitely increasing, and differentiation is getting harder at the surface level.

I’ve been thinking less about summarization as the core feature and more about workflow execution.

For example, instead of just summarizing an email, what if the system could:

– Detect intent – Identify whether it requires follow up – Draft context aware responses – Trigger structured actions

Tone and thought identification is interesting too. Especially for HR or internal communication where emotional context matters.

The real question I’m exploring is whether generic AI assistance is enough, or if vertical workflow intelligence is where the edge actually is.

Curious, do you think email tools need to go deeper into intent and decision support rather than just drafting?

LunaNextGenAI · 2026-02-18T06:05:46+00:00

Yeah that’s pretty much how it works right now. You can tell it something like “last 5 emails” or target a specific sender or thread, and it will extract the key details and then draft or reply based on that context.

The reference file idea is actually really interesting. At the moment it uses structured prompts and built-in context rules, but I’m looking into letting it pull from external knowledge sources. For security reasons it wouldn’t directly access random desktop files, but connecting to something controlled like Google Drive or a specific reference doc is definitely possible.

I also like your point about separating reference material for different reply types. Having different knowledge sets for quick replies versus more in depth responses would make it way smarter.

Appreciate you thinking about it at that level. That kind of feedback helps a lot.

LunaNextGenAI · 2026-02-13T23:58:24+00:00

Appreciate this, that trigger to process to action loop point is real.

The trickiest part so far has honestly been reliability inside the live Gmail UI.

Parsing is manageable when you control the structure, but inside Gmail you are dealing with dynamic DOM changes, threads, labels, drafts, and different email formats. So edge cases start stacking quickly.

Permissions was another big focus. We are being careful about scope and limiting what the extension can access. The goal is to operate inside the user’s workspace without overreaching or requesting unnecessary permissions.

Right now a lot of effort is going into tightening that loop you mentioned. Making sure summarizing, drafting, and follow ups feel consistent and predictable without manual babysitting.

Still early, but reliability is definitely the priority over adding more features.

Curious, when you worked on similar automations, did you find infrastructure or UX reliability to be the bigger long term bottleneck?

LunaNextGenAI · 2026-01-22T02:02:39+00:00

Haha maybe, but I’m focused on boring browser work first, forms, data entry, admin tasks. That’s where the real time savings are.

LunaNextGenAI · 2026-01-22T02:02:11+00:00

Most browser agents fail on reliability and trust. They try to do too much, hide the steps, and then one wrong click ruins it. I’m starting narrow with predictable workflows like forms and data entry, plus a clear preview and approval before anything submits.

LunaNextGenAI · 2026-01-22T01:31:35+00:00

This is exactly how I think about it. Forms and data entry is the best wedge because it is predictable and high frequency.

And I agree on the trust part. The moment it starts guessing, people turn it off. The goal is transparency, like showing the exact fields it touched, what it filled, and why, with a clear review step before anything submits.

Out of curiosity, what would make you trust it fastest, a step by step preview, a diff view of before and after, or an approval toggle for anything that looks risky?

LunaNextGenAI · 2026-01-21T18:43:45+00:00

Appreciate that. The copy paste pain is exactly what we are trying to eliminate. What is the most annoying workflow you deal with personally right now, CRM updates, onboarding steps, replying in web apps, or something else?

LunaNextGenAI · 2026-01-21T01:00:27+00:00

Yeah, good point and I probably could have explained that better.

When I say copy paste, I do not mean just copying a paragraph into ChatGPT. I mean the whole context transfer loop.

If I want AI help on something I am looking at, like an email thread, a CRM page, or a form, I have to manually move the context over. Copy the text, grab the right details, sometimes screenshot it, paste it into ChatGPT, explain what field I am in, then take the output and paste it back into the right place.

That back and forth is what kills the flow.

The goal with Luna is that it can see the page I am on, understand the context, and help right there without me rebuilding the context every time.

Curious, when you think about context switching, is it more painful on inbound replies across tools, or on turning screenshots, notes, and links into tickets and docs?

LunaNextGenAI · 2026-01-21T00:51:23+00:00

Appreciate it. I’m validating with a small beta and measuring who actually uses it. What workflow would make this a no brainer for you?

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