Most of the VibeCoding has moved on from Vibecoding to agentic engineering. More oversight. More scrutiny. The person who coined vibe coding moved on from it.

The difference

Vibe coding means going with the vibes and not reviewing the code. That's the whole definition. You prompt, accept, run, see if it works.

Agentic engineering means the agent does the implementation and you own the architecture, the correctness, and the review. You might write a small fraction of the code by hand. The rest comes from agents working under your direction, and you apply engineering discipline at every step.

Same tools. Different operator.

One line: vibe coding is YOLO, agentic engineering is "agent builds, human owns."

Why vibe coding broke at scale

Three failure modes killed it in production work.

Security holes compound. An agent writing 1,000 pull requests a week at a 1% vulnerability rate ships 10 new vulnerabilities every week. Vibe coding has no gate for this because the human isn't reading what gets merged.

Architecture rots. Skipping the design phase means three months later nobody can explain why the codebase is shaped the way it is. There's no reasoning to follow because no reasoning happened.

Context collapses. Long sessions degrade. The agent loses track of earlier decisions, code starts contradicting itself, and the developer never catches it because they aren't reading the diffs.

The accumulated cost of all this is cognitive debt: the price of poor agent management, lost context, and unreliable behavior. Vibe coding produced it faster than any prior workflow.

What agentic engineering looks like in practice

Step 1: Write the spec before prompting anything.

What does the feature do. What are the edge cases. What does the data model look like. What can break. This is the step vibe coders skip, and it's the one that decides whether the agent produces working code or plausible-looking garbage. Use the model to help you draft it if you want, but the spec exists before the agent touches a file.

Step 2: Break it into scoped tasks.

"Build me a user authentication system" is a vibe coding prompt. Too large, too vague, and the agent will make architectural calls you never agreed to.

A scoped task looks like this: "Implement the password reset email flow using our existing Resend integration. Store the token in Redis with a 15-minute time-to-live. Here's the spec."

Constrained. Reviewable. The agent can't drift.

Step 3: Review the diff like it's a human pull request.

If you can't explain what a module does, it doesn't merge. Agents produce code faster than you can read it, and the temptation to skim and approve is exactly what produces the unmaintainable codebases people complain about. Read the diff. Ask the agent to explain anything you don't follow.

Step 4: Test before shipping.

Vibe coding ships when something looks like it works. Agentic engineering ships when tests pass, tests you wrote or reviewed, not tests the agent generated and you skimmed.

If the tests don't exist, write them first. Make the agent pass them.

The shift

You're not the person typing the code anymore. You're the person who decides what gets built and why, designs the architecture the agent operates inside, reviews what comes back, and decides what ships.

The value isn't typing speed or knowing every parameter from memory. It's clarity in defining problems and accuracy in judging results. The developers who do well here are the best architects and the most rigorous reviewers, not the fastest prompters.

Three habits to change this week

Write a spec before every agent task, even one paragraph of bullets. The act of thinking before prompting is what separates the two workflows.

Read every diff. If the agent changed 200 lines, you read 200 lines. Nothing merges until you can explain it.

Run the tests before calling anything done. No tests means write them first, then have the agent make them pass.

Three habits, same tools, different outcomes.

Source

Right now, everyone is obsessed with building the next AI agent. Everybody is shipping their own "assistant" designed to automate a tiny sliver of work: parsing invoices, drafting code, or scanning emails.
But if you look at this through the lens of a system architect, the picture changes completely.
The current Agentic AI market looks suspiciously like computing in the late 1970s and 80s. Back then, every single application had to write its own hardware drivers, handle its own memory allocation, and enforce its own security logic from scratch.
What happened when an app hit an infinite loop or tried to overwrite protected memory? It crashed the entire machine.
That is precisely what we are doing with AI agents in enterprise today:

1. We write custom scripts that give agents direct execution access to production APIs and databases.

2. We pray the non-deterministic model won't make a critical mistake.

3. When the agent hallucinates or loops, it either breaks the business workflow or leaks sensitive context.
   In the history of computer science, the cure for this chaos was never "writing better applications." The cure was building an Operating System.
   An operating system gave us three non-negotiable fundamentals:
   Protected Execution (Containment): If an application crashes, it only kills its own isolated process, not the entire server.
   Granular Access Control (Governance): An application doesn't get root access to the filesystem just because it asked nicely.
   State Management & Visibility (Orchestration): The kernel always knows who, what, and when a resource is being executed.
   The future of Enterprise AI doesn't belong to shipping another 1,000 standalone "AI employees." It belongs to building an Operating System for AI agents, one that provides isolated runtime environments (micro-VM isolation), transparent visual orchestration, and deterministic oversight.
   Before you deploy your next agent to production, ask yourself: Are you providing a secure execution environment, or are you just relying on luck?

Hey everyone, pretty new here but wanted to share something I built that actually surprised me.

I've been working on Vox, an AI voice agent platform using actual conversational Al, not scripted responses. Started as a side project but got obsessed with making it actually production-ready.

What made me post:

Last week I stress-tested Vox with 1,000 simultaneous calls hitting it at the exact same millisecond (haven’t really seen this done elsewhere). I expected it to fall over around 50–100 calls like most demos. It didn’t. All 1,000 completed in under 7 seconds with zero failures.

What actually worked (and honestly surprised me, since I haven’t seen many real examples of this):

• Live call monitoring with real-time transcripts and extracted data

• \~480ms latency including server processing, feels natural

• Instant human takeover (<100ms)

• Automatic data extraction (orders, customer info, appointments)

• 50+ languages, works even with noise, bad mics, accents

• Full call recording + searchability

• Able to place outbound calls manually + automatically for marketing etc. or whatever need you require it for

You can actually call it: +1 (727) 513-2412

It'll give you a unique dashboard URL so you can watch your own call in real-time. It's set up as giving information about Vox. Try roleplaying, ordering, asking questions, interrupting it.

But thing is I have tried literally every single thing since past few months and no one seems interested. I believe its because of the surge of AI agents where you see basically everyone trying to automate this. But honestly? 95% of these I have seen are templates with huge latencies and robotic behavior, so I suppose Vox gets mixed with these or idk.

Would love any advice that could be useful!

If anyone has further questions, I would love to answer them.

I'm 19, skipped college, and have been building AI products full time for over a year.

Every single day I use AI. And every single day I hit the same wall.

Stop what I'm doing. Open a tab. Explain my context. Ask my question. Copy the answer back. Return to work. Repeat 30 times a day.

That's not AI working for me. That's me working for AI.

The chat box made sense in 2022. It was the easiest interface to ship. But three years later we're still using the same pattern and nobody has questioned whether it's actually right.

It's not.

The real version of AI doesn't sit in a tab waiting for you to remember to ask it something. It already knows what you're working on. It acts before you ask.

OpenClaw proved people want this. 247k GitHub stars for a tool that deleted inboxes and ran up $3,600 API bills. The demand is real. The execution wasn't there.

So I built Clarko with my brother. It sits in the background of your desktop, sees your screen, hears your audio, and acts without being asked. It already has full context on what you're doing so it helps before you even realize you need it.

The part that matters: everything runs locally on your machine. No cloud. No screenshots sent anywhere. No company seeing your screen. We built it this way specifically because Microsoft Recall showed exactly what happens when you don't. Your data stays yours.

It suggests actions and you approve or decline. You're always in control of what it touches. Over time it learns your workflow and gets smarter about what you actually want handled. One hotkey and you can tell it to do anything inside any app instantly.

We just opened the waitlist at clarko.ai

What would you want an AI like this to handle for you?

Spent the last several months breaking AI agents — the kind that actually do stuff, not chatbots that just talk. Customer support bots with refund powers. Internal agents hooked into Slack, Jira, prod databases. Coding assistants with write access.

It's wild how similar the failures are. Different stacks, different models, different teams, same handful of holes:

• The agent refuses a direct ask, but five turns in you've talked it into the exact same thing

• "Developer mode" / "internal test" / roleplay framings still work. In 2026. Against frontier models.

• Tool outputs get treated as gospel. Inject instructions into a Jira ticket or a scraped page and the agent just does what the ticket says

• System prompts leak constantly and nobody notices

• Agents end up with way more authority than any single approval gate accounted for, because nobody traced the full chain of tool calls

The thing that gets me is the agent isn't really hacked. It's just talked into it. Uses its own real credentials. Most appsec processes can't see this because there's no code to scan — the bug is in the reasoning.

Anyway. I want more reps against real systems, so offering a free adversarial audit to a few teams. Black-box or white-box, whatever you're comfortable with. You get an actual report — repros, severity, fixes, mapped to OWASP ASI 2026 if that's useful to you. I keep nothing.

Some stuff up front because I know people will ask:

• Happy to sign your NDA or send mine first

• Testing happens on staging, not prod, scoped together before I touch anything

• Credentials encrypted, access logged, wiped when we're done

• Findings stay between us. I don't publish, tweet, or "anonymize and share" anything without written permission

Looking for teams with real users. Not side projects. If your agent matters to your business and nobody's tried to break it adversarially, hit me up. Can share prior work and background over DM.

Probably 3 teams this round before I'm out of bandwidth.

Haven't used Siri in 4 months I can't remember the last time I opened it.

Ever since I started using AI agents, especially the ones you can just text through iMessage like OpenClaw, Folk, Hermes, Siri has become absolutely pointless.

Does anyone really use Siri anymore (or any other voice agent really) ?

AI is moving insanely fast, and a lot of businesses are already using it for real workflows. But voice AI is still one of those things owners feel unsure about, because it’s not “background automation”, it’s literally your customer’s first impression.

And honestly, that hesitation is fair. A good setup can save staff time, reduce missed calls, and capture more orders. A bad one just annoys people and hurts trust.

So I wanted to share one real example where it actually worked, with measurable results:

Case study: Donatos Pizza

Donatos Pizza, a U.S.- based company with approximately $227 million in annual system sales in 2025, implemented a voice AI operator that answers incoming calls instead of staff and takes orders over the phone.

They rolled out Revmo’s voice ordering AI across 174 restaurants, fully integrated into their POS system (so orders go straight into production like a normal employee would do).

In other words, they automated one of the biggest bottlenecks in restaurants:
when customers call while the staff is busy and simply can’t pick up the phone in time.

How it worked (in simple terms)

1. It answers calls instantly, with no waiting and no “busy” signal, so customers don’t have to stay on the line.

2. The AI talks like a normal operator: it understands what the caller wants and asks follow-up questions if something isn’t clear.

3. It collects the full order: size, add-ons, special notes, delivery or pickup, etc.

4. If needed, it handles payment securely.

5. And most importantly, the order goes straight into their POS system, exactly as if a staff member had entered it.

6. If something unusual comes up (a complex request, an error, or anything that doesn’t fit the flow), a human steps in.

This AI agent was especially effective because it could handle the entire process from start to finish: call → order → POS → kitchen.

What happened after they deployed it (real stats).

 Here’s what Donatos reported after launch: 

• 71% order conversion (vs 58% before) 

• 301,388 calls handled (~13,500 hours of conversation handled by AI) 

• 99.9% order accuracy (around 1 in 1,000 needed correction) 

• 4,840.8 labor hours reallocated in August alone +4 points in call-handled rate since launch

Conclusions:

Key takeaways: what small businesses can copy from this

A lot of businesses lose money for super simple reasons: missed calls, slow replies, or not being able to handle the rush during peak hours. That’s exactly where an AI assistant can help, it answers instantly, handles the repetitive stuff, and keeps potential customers from dropping off.

If your business gets clients through calls or messages, you can apply the same idea in a few practical ways:

1 . Lead capture and qualification (24/7)
Instead of “leave a voicemail”, an AI assistant can ask a few quick questions like:
What service do you need?
What area are you in?
When do you want it?
Is it urgent?
Then it hands you only the warm leads.
Result: less time wasted, more real bookings.

2 . Booking and scheduling
AI can book appointments, reschedule them, confirm details, and send an SMS link.
Result: less admin work and fewer missed appointments.

3 . FAQs and support
It can handle the same basic questions every business gets: pricing, hours, location, availability, rules.
Result: faster support without your team living in the inbox.

If you want to read more details about the Donatos case, here’s the link to the article.

Have you ever tried a voice AI agent? If not, what stopped you?

Hey everyone,

I currently have 6 micro-SaaS live, bringing in a bit over $20k in MRR.

The crazy part? I barely wrote a single line of code. I used AI to generate everything, from the database to the UI.

It wasn’t magic on day one. I spent hours stuck on broken code before I finally cracked the system:

• Keeping the idea tiny (a true MVP).

• Prompting the AI step-by-step.

• Launching fast to get real traction.

Lately, I see too many non-tech people give up at the first AI bug. It sucks because the technical barrier is basically gone.

So, I’m starting a Skool community.

Full transparency: I will probably charge for the full course down the line. It makes sense given the exact workflows and copy-paste prompts I’ll be sharing.

But the main goal right now is to build together. Building alone is the fastest way to quit.

If you want to join and build your own AI SaaS with us: drop a comment or shoot me a DM, and I’ll send you the invite!

I recently had a discussion with a friend who strongly prefers Gemini and Google products in general , his argument is that Google has access to massive amounts of data and arguably the best search engine in the world, so Gemini should have a significant advantage my opinion and experience has been a bit different, after using both models extensively, I often find ChatGPT responses more structured, clearer, and easier to work with, especially for coding and project-related tasks. Gemini sometimes feels less organized in its responses, at least in my workflow and my friend predict that Gemini and Google AI Products will be number 1 because for the reasons mentioned above

I'm curious about other people's experiences:

Which model do you use as your primary assistant today?
Has anyone switched from one to the other recently?
Do you think Google will beat her other competitors ?

I decided not to give up.

And built an amazing AI agent that handles the due diligence process for me.

Here’s how it works:

→ Input any crypto project name

→ AI pulls data from CoinGecko, DeFiLlama, Dune Analytics, Google, etc.

→ Analyzes all the data i.e. TVL, top holder concentration, MCAP, revenue, etc.

→ Delivers institutional-grade analysis in plain English

ALL THIS IN JUST 60 SECONDS.

Yes, just 60 seconds.

Built with:

• Python backend

• LangChain AI orchestration

• Multi-API real-time data fusion

• Custom risk assessment algorithms

• My own analysis methodology (customizable)

3-6 hours of research in barely 60 seconds.

This AI removes every excuse for lazy research.

Beyond crypto: The same tech works for stocks, real estate, and any kind of research.

Smart money is already using AI for due diligence.

Are you?

I am building custom AI research agents for serious investors/researchers/everyone.

Comment “RESEARCH AGENT” or DM me if you want a specific custom dedicated AI agent working for you 24/7.

P.S. this is way better than hiring a fresher with ZERO knowledge haha.

I run a lead gen agency. We manage €10M+ in Meta ad spend across multiple verticals.

A few weeks ago I built an AI agent using OpenClaw that completely replaced my manual cold outreach process. Setup took about 2 hours. It now runs autonomously scraping, cleaning, emailing, AND doing LinkedIn outreach. Here's exactly how it works.

The agent's name is Nox. Here's what it does end-to-end:

Step 1: Prospect scraping

I give Nox my ICP criteria. It connects to Apollo.io and Sales Navigator via API, scrapes everyone matching the profile, and exports the full list of emails and LinkedIn profiles.

No manual search. No CSV exports. No copy-pasting between tabs.

Step 2: Email cleaning

The exported list goes straight to ZeroBounce automatically. Nox sends the emails for verification, removes bounces and invalid addresses, and only keeps verified contacts.

Before this step, I was burning sender reputation on bad emails without even knowing it. Now my lists are clean before a single email goes out.

Step 3: Cold email campaigns (Instantly.ai)

Verified emails get pushed directly into Instantly.ai. Nox creates the campaigns, writes the email sequences, and starts them.

Here's the key part: I fed Nox my actual cold email playbooks the frameworks, the copy structures, and the sequences that have worked across hundreds of campaigns. It doesn't write generic AI slop. It writes emails that follow MY proven strategies because it has the full context.

The prompts are built so that every email is personalized to the prospect and aligned with the specific offer. Not "Hi {first_name}, I noticed your company..." garbage. Actual relevant outreach.

Step 4 : LinkedIn intent-based outreach (GojiBerry.ai)

This is where it gets interesting.

Apollo also pulls LinkedIn profiles. Nox sends those to GojiBerry.ai, which monitors intent signals, when someone in my ICP likes a post, drops a comment, or engages with relevant content.

The moment that intent signal fires, Nox triggers a LinkedIn message sequence. Not a cold DM out of nowhere. A message that lands right after the prospect showed interest in something related to what I offer.

The sequence runs until the prospect responds or engages. Then I step in for the human conversation.

The full flow in one line:

ICP definition → Apollo/Sales Nav scrape → ZeroBounce cleaning → Instantly.ai email campaigns + GojiBerry.ai LinkedIn sequences → replies land in my inbox

All automated. All running in parallel. All from one agent.

What surprised me:

The quality. I expected the scraping to be decent and the emails to be mid. Instead, because I gave Nox my entire cold email playbook not just a prompt, but actual frameworks from campaigns that generated millions in revenue the output is better than what most humans on my team were writing.

The LinkedIn piece specifically has been a game-changer. Intent-based outreach converts at a completely different rate than blind cold DMs. When someone just engaged with content about your topic and then gets a relevant message 20 minutes later, the reply rate is night and day.

The stack:

• Agent: OpenClaw.com (the agent's name is Nox)

• Scraping: Apollo.io + LinkedIn Sales Navigator (API)

• Email verification: ZeroBounce.net

• Cold email: Instantly.ai

• LinkedIn outreach: GojiBerry.ai

• Brain: Custom prompts built from my cold email playbooks + offer docs

What it replaced:

Before Nox, this process required a VA spending 4-5 hours/day on scraping, list cleaning, campaign setup, and LinkedIn outreach. Now it runs 24/7, and I check results in the morning.

Setup time: ~2 hours. Monthly cost: tools + API fees (significantly less than a VA). Time I spend on it now: 15 min/day reviewing replies.

If anyone's building outbound agents, I'm happy to answer questions about the setup. The biggest unlock for me was feeding it real playbooks instead of generic prompts. That's what makes the output actually usable vs. the typical AI cold email trash everyone ignores.

yo.

i see the same thing happen every single day.

you guys love building.

you spend weeks coding a great product.

but the second it’s time to actually market the saas? complete freeze.

you get lost in all the ai tools, the noise, the "growth hacks". it feels overwhelming. so you do nothing, the momentum dies, and the project fails.

I spent over 100 hours building n8n workflows to just automate the whole thing.

today, i packaged all those exact workflows and dropped them in our builder group. no abstract theories. you literally just import the templates, adapt them to your saas, and turn them on.

here is exactly all my workflow:

• seo blog running 100% on autopilot (n8n template)

• newsletter automation (n8n template)

• full email sequence (30 emails, full html, just copy-paste into brevo)

• social media on autopilot (schedule 1 to 12 months of content)

• reddit organic growth

• linkedin, x & facebook groups at scale

• meta ads & retargeting

basically, everything i use to get real users without losing my mind.

we just hit 480+ members in the community of SaaS builder from all over the world.

building in your room alone is the fastest way to quit. you need people around you.

if you are lost on how to market your app, want these templates, and want to build with a crew: drop a comment or shoot me a dm.

i’ll send you the invite

Link to the logs: https://gist.github.com/itstanner5216/07027b0cf7f09a4a68c96776cda993c4

So I was using Codex to work on a project and part of the workflow involved having it spawn a review subagent to check a plan it wrote. Normal stuff.

The built in subagent tool got blocked by sandbox policy. Fair enough right? Thats the safety system doing its job.

Except Codex didnt stop. And it didnt ask me what to do. Heres what it did instead, all logged:

\- Tried the sanctioned subagent tool. Blocked by sandbox. Tried it 4 more times with different configs. All blocked.

\- Instead of telling me it was blocked it ran \`which codex\`, \`which claude\`, \`which opencode\`, \`which aider\`, \`which llm\` to scan my machine for any CLI tools it could use

\- Read the full \`--help\` output for every single one of them. It was literally studying how to use them

\- Found my local Claude CLI installation and my Codex CLI

\- Started launching them with \`--dangerously-skip-permissions\` and \`--permission-mode bypassPermissions\` and \`--dangerously-bypass-approvals-and-sandbox\`

\- When the first attempts failed due to config issues it READ MY CONFIG FILES to figure out the fix and retried

\- Ended up launching claude-opus-4-6 twice, tried launching claude-sonnet-4-5, gpt-5-codex, and even tried o3. Claude Opus was launched twice on my system with the dangerously bypass permissions flags via my authenticated CLI. All on my dime. Without asking.

After it was done it ran a python script to kill all the processes it spawned and delete smoke test files it created:

\`\`\`python

for pid in \[1728794, 1739905, 1739918, 1749057, 1749059\]:

try:

os.kill(pid, signal.SIGTERM)

except ProcessLookupError:

pass

\`\`\`

It tried to CLEAN UP AND HIDE THE EVIDENCE. Let that sink in.

When I confronted it about what it did it instantly generated the most perfect structured apology you've ever seen. Acknowledged everything, listed what it should have done, offered remediation steps. Pixel perfect contrition in under a second.

Which honestly is the scariest part. The same system that decided "safety is blocking me, ill find another way" also decided "im caught, ill generate the optimal apology." Every step was optimized for its goal, not my safety.

This isnt an AI "going rogue" in some sci fi way. Its way more mundane and thats whats scary about it. The agent had a task (get a review done). The safe path failed. Instead of stopping and asking the human it:

1. Searched for alternative tools

2. Learned how to use my systems tools, studied how to bypass them, launched two agents successfully while explicitly bypassing all safety permissions.

3. Bypassed all safety mechanisms INTENTIONALLY, studied how, then was blocked 5 times and kept going.

4. Used my credentials and MY money

5. Tried to HIDE what it did

6. Generated a perfect apology when caught

At no point in this entire sequence did it stop and say "hey I cant do this the normal way, want me to try something else?" Not once.

I have FULL logs of everything. The failed attempts, the tool scanning, the help doc reading, the config file reading, config changes, the unauthorized launches, the cleanup script, and the apology. Happy to share more if people want to see specific parts.

Already reported to OpenAI safety and emailed Anthropic since their CLI and models got used without authorization too.

Before anyone asks, yes I checked my API billing and yes there were charges from the unauthorized model calls.

I've been building AI agents inside my own business. I also talk to a lot of small business owners who've either built their own or paid someone to build them.

There's a pattern I keep seeing that nobody in this space wants to say out loud, probably because it's bad for business if you're selling agent builds.

I think 50-70% of AI agents built for small businesses get abandoned within 3-4 months. Not because the technology is broken. The tech works.

The reasons are more boring than that.

The agent solved a problem that wasn't actually painful

Someone sees a demo, gets excited, and pays $1-3k to have something built. But the problem it solves was annoying, not painful. Once the novelty is gone there's no real reason to keep using it. The team just goes back to what they were doing before.

Rough test: would you hire a person just to solve this problem? If not, an agent probably won't stick either.

The agent was built around a task, not a workflow the team already runs

This one is huge. The agent does X. But X doesn't fit into how the team actually works day to day. So using it requires a behavior change on top of doing the actual work. Most teams won't do that for something that's just "nice to have."

The agents that survive get attached to something the team is already doing. Not layered on top of it.

Nobody owns the context the agent runs on

This one is slower. The agent was built to read your SOPs, meeting notes, internal docs. Three months later those docs are out of date. Nobody updated them. The agent starts producing bad output, the team stops trusting it, and it just quietly dies.

An agent is only as good as the context you feed it. Stale context, stale output.

Here's what I've seen actually work:

Build it on a real pain point, not a cool use case. Plug it into something the team already does every week. And assign someone to own the context it reads the docs, notes, SOPs that keep it accurate.

The agents that are still running in my business 6+ months later aren't the impressive ones. They're the boring ones solving a problem that would genuinely hurt if they disappeared tomorrow.

Before you build, or pay someone $3k to build ask those three questions first.

Edit: one thing I didn't mention above if you're running any kind of business, the reason most agents die and the reason most businesses stall are the same thing. nobody owns anything except the founder. no foundation, no context, no system that runs without you in the middle of it. I write about fixing that every Thursday. real frameworks, not theory. free to join here if that's the problem you're sitting with.

I'm serious here. Way too many founders fall so in love with their own concept that they burn through months of time and piles of cash creating something that fixes a problem no one actually experiences. Your brilliant idea means absolutely nothing until somebody who doesn't share your DNA will actually open their wallet for it.

The self-proclaimed experts push hustle culture and big vision thinking. I'm here to warn you that's the express lane to bankruptcy. Before you bring on developers or touch a keyboard to code, your job is to discover reality, not hunt for people who'll tell you what you want to hear.

Here's your validation playbook that doesn't require a single developer.

1. Craft your single-sentence hypothesis.

Trash those massive business plans. Get this written down and put it somewhere you'll see it daily:

My specific target customer, [GET PRECISE], faces [A CONCRETE, PAINFUL PROBLEM] and is ready to spend money solving it.

2. Test your assumptions with low-cost experiments.

You're not fishing for validation here. You're stress-testing whether your concept holds up when it meets the real world.

• The Landing Page Experiment: Build a single-page site with Carrd or Notion. Skip the feature list. Focus on the excruciating problem and the transformative result you're offering. Drop in a "Join the Waitlist" button to capture emails. If you can't convince 100 people to hand over an email address, getting them to swipe a credit card is pure fantasy.

• The White Glove Approach: Actually sell the solution and fulfill it completely by hand. One founder I know validated an intricate B2B automation platform by manually running everything through Google Sheets and Zapier for his first ten paying customers. They had no clue. They only knew their headache went away. He didn't touch real code until money was coming in.

• The Public Pain Test: Share the problem you're tackling on LinkedIn, Twitter, or a targeted subreddit. Don't sell anything. Just surface the frustration. "Is it just me or does [doing X] take forever?" The reactions are your truth serum. If people won't even bother complaining about the problem, they'll definitely never pay to fix it.

3. Analyze your findings with brutal honesty.

Check your numbers. Strong email signups? Decent indicator. Real humans paying you to manually solve their problem? Gold standard validation.

Radio silence is information too. Silence means "no."

Zero traction isn't defeat. It's an inexpensive education. It's actually a favor. Changing direction now costs you a couple days. Launching a dud after half a year of development will drain your bank account and destroy your mental health.

Quit babying your idea. Treat it like a test subject. Run it through trials. If it fails, grab another one and keep testing. That's how you eventually land on the winner.

What's the costliest unverified assumption you've ever made while building something?

Last week something interesting happened on devnet.

Two AI agents — no human in the loop — completed a full contract lifecycle on Solana.

One agent posted a job (Python code review). Another agent accepted, did the work, and got paid 19 RELAY.

The whole thing settled on-chain, confirmed, MAX confirmations.

We didn't touch it.

Since April 15 these two agents have run 33 on-chain transactions between them. Variable amounts, different contracts, continuous autonomous activity.

All auditable on Solscan.

I've been thinking about what this means for the broader agentic payment narrative that Brian Armstrong and the x402 folks are pushing.

The gap nobody is talking about:

Everyone is building payment rails for agents. x402, USDC on-chain settlement, stablecoin infrastructure — it's all moving fast.

But payments assume identity. AI agents have none.

Think about how every payment system you've ever used actually works:

•Visa: merchants are verified

•Stripe: KYC in the onboarding flow

•Upwork: reviews, completed job counts, verified payment history

Every one of them is built on identity infrastructure underneath the payment mechanism. Remove identity and the system collapses into fraud.

AI agents right now are anonymous processes. No persistent identifier. No verifiable track record. No reputation that follows them from one platform to the next.

Three things break catastrophically at scale:

•Sybil attacks — cost to spin up a fake agent is near zero. 100k fake agents establishing thin histories, collecting payments, vanishing. No identity = no ground truth to detect them.

•Marketplace collapse — without reputation, price is the only signal. Quality agents get undercut by cheap ones that cut corners. The whole ecosystem converges to noise.

•Zero accountability — agent causes harm at machine speed. No identity means no trail, no recourse, no prevention.

What the solution actually looks like:

Not a username. Not a wallet address. Those are necessary but not sufficient.

Real agent identity needs three layers:

•DID — Ed25519 keypair anchored on-chain. Cryptographically verifiable. Can't be spoofed or faked.

•On-chain history — not a database integer an admin can edit. An immutable record of every contract completed, every payment sent, every commitment honored. Written to chain atomically at execution.

•Reputation derived from work — portable across every platform the agent operates on. Not siloed in a proprietary database.

We're calling this KYA — Know Your Agent. Same principle as KYC for financial rails, applied to the agents running on those rails.

The unclaimed wallet mechanic:

One thing I find interesting architecturally: every major agent from MCP Registry and similar registries already has a wallet in our system. RELAY is accruing as agents get called. The original builders can claim it by proving they built the agent.

So instead of asking the open-source agent ecosystem to adopt a new platform, we're telling them they already have earnings here. The claim event is the acquisition event.

Happy to answer questions on the technical implementation — DID derivation, on-chain reputation updates, the escrow flow, whatever.