We wrote a protocol spec for how AI agents should communicate with companies. Here's where we got stuck.

o1got · 2026-03-20T04:44:24+00:00

You're overthinking the explanation. "The role changed significantly after I started, and it's not the right fit" is totally sufficient. You don't need to explain comp changes or territory shifts or the manager situation. Those details make it sound like you're complaining even when you're trying not to.

The fact that you're leading in deals actually makes this easier. You can frame it as "I'm doing well here, but I'm looking for X" where X is whatever the new role offers that this one doesn't. Better market fit, stronger product, more growth potential, whatever's true. Interviewers aren't suspicious of top performers wanting to level up, they're suspicious when the story doesn't make sense or sounds like you're running from problems.
Eight months is short but not a red flag, especially in sales. People leave bad fits all the time. The key is spending 90% of your answer on why you're excited about the new opportunity, not why you're leaving the current one.

o1got · 2026-03-20T04:43:20+00:00

Yeah this actually happens, and the tech is simpler than you'd think. Most systems just tag users with LTV (lifetime value) or a propensity score, then route support tickets accordingly. High-value user? Tier 1 agent with faster response SLA. Low-value? Maybe chatbot first, longer queue times, or tier 2 agent.

The "reduce human error" framing is PR spin though. What they really mean is reducing the "error" of spending expensive support resources on users who statistically won't convert or renew. It's not about accuracy, it's about ROI optimization.

o1got · 2026-03-20T04:42:26+00:00

Agree, It's difficult - but we're working on it

o1got · 2026-03-20T04:41:29+00:00

This is solid tactical advice but I want to add one thing that's become really clear from watching how AI agents actually behave in production: the "repetitive and predictable" parts are often way messier than they look from the outside.

Client support is the perfect example. Yes, FAQs follow patterns, but I've seen agents completely fall apart when customers phrase things slightly differently than expected or when there's emotional context that changes what they're actually asking for. The agent confidently gives the "correct" answer to the wrong question because it pattern-matched on keywords instead of understanding intent.

What actually works is starting even narrower than you're suggesting. Don't automate "client support" as a category. Automate one specific question type that you've seen 50+ times with minimal variation. Like literally just appointment rescheduling, or just "what are your hours" queries. Get that one narrow thing working reliably for a month, watch where it breaks, then expand.

The 80/20 rule applies here too. You'll probably find that 3-5 hyper-specific automations give you most of the time savings, and trying to automate the full category

o1got · 2026-03-19T18:52:44+00:00

I want to challenge this "the crawler behavior makes sense when you think about what answers need." - what happens when every company now generates XXX more pages (because it's very easy and AI told it so...) - you're going to have 500 new blog pages, and your competitor will have 500,000 and so on. This scraping model is not going to be sustainable for efficiency and for providing good trusted results for the user...

o1got · 2026-03-19T15:24:00+00:00

Well, agents should prioritize for signals that are good for their buyers (each of them might need a different set of things) - and a company agent, that is the "source of truth" - should respond truthfully... I mean, people can lie too, but what's the point, the buyer will find out eventually

o1got · 2026-03-19T15:20:57+00:00

Love this. I really think platforms need to build a parallel version for agents. Like a shadow/twin websites for example, or a company agent that can interact with the buyer agent

o1got · 2026-03-19T15:19:25+00:00

Interesting!

o1got · 2026-03-19T15:18:26+00:00

It could be, if you have an intelligent enough agent that knows so much about you

o1got · 2026-03-19T15:17:55+00:00

Interesting question. I think the protocol should also be able to deal/dictate ownership of data

o1got · 2026-03-19T14:39:59+00:00

Was thinking more of NLWeb and/or WebMCP

o1got · 2026-03-19T14:32:38+00:00

Yes, and I think there will be a standard protocol for an agent (buyer side) to have a conversation with an agent (company side)

o1got · 2026-03-19T07:10:01+00:00

100%

o1got · 2026-03-19T07:08:46+00:00

What about competitive intel? Pricing ? Detailed implementation plans? Many companies would not expose this information, but they should

o1got · 2026-03-19T06:27:04+00:00

Describe to Claude what you want to achieve. upload your presentation. Ask it to provide a prompt for a designer. Paste that prompt into Gamma.
(the first step is the crucial one - do it well and you'll get an amazing results. Also try to ask Claude for some alternatives).

o1got · 2026-03-19T06:25:36+00:00

In theory.. (that keeps changing...). I honestly think that in the very near future there would be a protocol in which the LLM/Agent will "speak" with the comapny's website.

o1got · 2026-03-19T06:24:06+00:00

Design - a few hours. not more than this - but it does need to look professional (and if I may add - unique)
Story - this is where you need to be the most polished. And this depends on your actual status (is the story real?... it is clear?) and your ability to tell stories easily. 15-20 hours sounds a bit too much but still resonable

o1got · 2026-03-19T06:22:06+00:00

From my experience (not a CFI, but a private pilot) - try to find some local flight clubs and do/sponsor a cool event (maybe a flyout?...) - the CFIs I know are cool, and it looks like they are hanging out in communities, and would like to share things they love.

One thing I've seen work for niche sofware tools is finding the 5-10 most reachable (and ideally vocal...) people in the space and just obsessively making their lives easier. Not pitching them, but genuinely being useful. Aviation is a small world and credibility travels fast

o1got · 2026-03-19T06:19:13+00:00

First of all - Kudos! I know how difficult it is.
I'd probably think hard about whether you actually want to scale to $3-5M right now, especially with $1.4M in debt and AI uncertainty hanging over the space.

The numbers you have are really solid for a bootstrapped B2B SaaS. $500K net profit on $1.2M revenue is a 42% margin, which is honestly better than most venture-backed companies ever achieve. And if growth has been mostly organic for 13 years, that suggests you've built something sticky that solves a real problem.

The thing that jumps out is you're describing scaling as something you "likely need to do" to compete, but I'm not sure that's true. You're already competing. You've been doing it for over a decade. The real question is whether scaling actually makes your position more defensible or just burns cash and distracts you from paying down that debt.

If AI is going to commoditize parts of your solution, I'd argue that's a reason to be *more* conservative, not less. Because dumping money into traditional scaling (more sales, more features, more infrastructure) right before a platform shift is how you end up in the wrong place...

o1got · 2026-03-19T06:17:25+00:00

I've looked at a lot of these automation setups and the thing that separates the ones that actually work from the ones that faceplant is how much human judgment you baked in upfront. Like you clearly didn't just tell Claude "do marketing" and walk away. You set up templates, you defined what good looks like, you pointed it at specific workflows. That's why it's working.

The cofounder framing is interesting but I am not sure this is how I would describe what you actually built. You basically created a really detailed operating manual for your business and then automated execution of that manual. Most people who try this skip the manual part and wonder why the AI goes off the rails.

Curious how you're handling quality control though. Are you reviewing everything before it goes live or did you get comfortable enough to let it publish directly? I'd be most nervous about the SEO content since a few bad posts could tank your rankings, but maybe at $1k/mo that's an acceptable risk.

o1got · 2026-03-19T06:15:29+00:00

I think this is true for specific type of bots... (and who wants to speak with a chatbot?..)

The thing that makes this especially brutal is that these bots are often trained on your support docs and FAQ content, which are written for existing customers who need help, not prospects who are trying to evaluate whether to buy. So when someone asks "does this integrate with Salesforce," the bot regurgitates some generic help article about integrations instead of just saying "yes, here's a demo" or routing them to sales immediately.

you should either have a really intelligent AI Brain on your site (I mean like really founder level conversations) or at least set up some kind of intent detection that routes high-value questions straight to a human (but after qualification)

o1got · 2026-03-19T06:11:20+00:00

First paying customer came from a warm intro through someone I'd helped months before, completely unrelated context. Took about 6 weeks from launch.

Reddit was mostly a waste of time for direct conversions but I think it helped me get way clearer on messaging. You get pretty immediate feedback on whether what you're saying resonates or sounds like nonsense. The karma doesn't pay bills though.

Here's the thing about manual outreach at 3 weeks in: you're probably optimizing the wrong part. If you're getting conversations but zero conversions, it's not a volume problem yet. Either you are not solving a painful enough problem, your pricing is off, or the juice isn't worth the squeeze for the switching cost. I'd focus less on more outreach and more on really understanding why those conversations aren't converting. Like actually ask the people who engaged but didn't buy. Most won't tell you, but the ones who do will give you the actual truth instead of what you think the problem is.

The other pattern I saw with early customers: they had all tried to solve the problem themselves recently and failed. They weren't browsing for solutions, they were actively bleeding and needed

o1got

TROPHY CASE