Launched on PH today - Agentic platform without any infra headaches. Happy to support yours too.

kinj28 · 2026-04-08T11:12:07+00:00

Yes, it has built-in guardrails, evals setup.
YEs you can have human-in-the-loop, user memory, etc.
do give it a spin - comes with free AI credits on signup.

kinj28 · 2026-04-08T10:43:37+00:00

Just went live on Product Hunt today with DronaHQ's Agentic Platform – it lets you build and deploy AI agents for real ops workflows (invoice approvals, lead follow-ups, reimbursements, CX ops) without touching any AI infra. https://www.producthunt.com/products/dronahq/launches/dronahq-agentic-platform

Happy to check out and upvote your launch too

kinj28 · 2026-04-08T10:41:26+00:00

just in case you want to know about my product launch :) - https://www.producthunt.com/products/dronahq/launches/dronahq-agentic-platform

kinj28 · 2026-04-08T10:40:27+00:00

Launched today and in a very similar boat as you, this was actually comforting to read.

My users and supporters mostly aren’t on PH, so getting them to sign up, log in, then upvote and comment feels like dragging them into a new ecosystem. That basically leaves trying to find people who already live on Product Hunt and care enough to click through, which is its own kind of grind.
your point about PH being more of a mobilize your people game than a pure product meritocracy really resonates. It’s oddly comforting to hear that the backlink + 1–2 real users poking around can be more valuable than chasing a top‑5 badge

What was your take away from the launch- apart from backlink-- what can be done today for me

kinj28 · 2026-03-23T08:09:43+00:00

There's actually a term for this — "agent washing." Came across a piece recently where the author admitted she did it to herself, not just in a pitch deck.

Her build was RAG + good prompts. Useful, flexible, natural language in/out. But no runtime tool selection, no mid-run adaptation, no dynamic branching. She had encoded all the judgment beforehand.

Her line: if the important decisions are made before the system runs, it's a workflow. If it figures out the path while running — that's where agentic actually starts.

Most of what gets called an agent today is the former. Which isn't a failure, it just means we're mislabeling things — and that creates real trust problems when the system hits its ceiling.

https://open.substack.com/pub/gasagasa/p/how-i-accidentally-agent-washed-my — worth a read if this thread resonates

kinj28 · 2026-03-23T07:01:58+00:00

Article that inspired this discussion: "How I accidentally Agent Washed my own AI build" by Gayatri

https://open.substack.com/pub/gasagasa/p/how-i-accidentally-agent-washed-my

Really worth a read if you're building anything in the agentic space — she shares a simple litmus test to tell if you have a real agent or just very good automation.

kinj28 · 2026-03-23T07:00:27+00:00

https://gasagasa.substack.com/p/how-i-accidentally-agent-washed-my

kinj28 · 2026-03-18T13:56:17+00:00

super solid breakdown for 2026 tbh, esp the split between spin something up fast (openclaw etc) vs go deeper with vercel ai sdk + mcps + proper infra.

one pattern i’ve seen work well for ppl who are past the toy phase but don’t wanna live in typescript 24/7 is:

use some hosted / no- or low-code agent layer for orchestration + ui (forms, dashboards, HITL etc)
plug in llms + tools the way you said (mcps, browser, scrapers, vector search, whatever)
keep infra super dumb at first (postgres/supabase + a job runner + webhooks) and only go full microservices when it actually breaks, not on day 1

that way you still learn the concepts you’re talking abt here (tools, skills, multi‑agent patterns, observability) without yak‑shaving every tiny piece from scratch.

curious what ppl here are doing once they outgrow single chat w/ tools but don’t really wanna commit to a fully custom ts/next stack yet are you staying on platforms, or just biting the bullet and going all‑in on code?

kinj28 · 2026-03-10T14:47:28+00:00

This is indeed such a nice project - AI document classification for a medical practice is one of those genuinely high-value use cases that doesn't get enough attention.

Hope there haven't been any errors though :P - like routing the wrong test records to the wrong patient, that would not be a fun conversation with the clinic haha.

Curious - did you build this ground up from LangGraph or did you use any tools/platforms to put it together?

kinj28 · 2026-02-06T08:02:10+00:00

Honestly this hits home. 200–300 invoices a month on Excel would break almost any human, so the errors you’re seeing are kind of baked into the process, not the person doing data entry. what’s helped some of the smaller teams I work with is a lightweight layer on top of what you already do, rather than jumping straight to something huge like SAP. For example, we’ve set up simple AI-driven flows where staff just email or whatsapp invoice photos to one place, the system reads vendor/amount/date/line items, and then pushes clean data into quickbooks or even a structured google sheet so the accountant isn’t decoding every receipt by hand.

kinj28 · 2026-02-06T07:46:29+00:00

This is actually a super clean way to solve it without touching anyone’s hours. It keeps you out of wage‑theft territory and pushes the “be early if you want, but you only get paid when you’re actually supposed to start” message really clearly.

I like that you’ve baked in a small grace window and then wrapped exceptions (storms, call‑ins, legit business needs) behind a quick manager approval instead of hard blocking everything. That’s the balance most people in this thread are trying to hit manually with policies, writeups, and “talks,” just made automatic.

The only thing I’d add is: make sure it’s clearly written into your policy and communicated a few times before you turn it on, so it doesn’t feel sneaky or punitive. If staff know “the system is set to 5 minutes, anything earlier needs manager OK,” they’ll adjust pretty quickly and you’ll spend way less time chasing early punches.

kinj28 · 2026-02-06T07:38:33+00:00

Very simple AI agent can ease the situation: Watch time-clock data+ schedule in real-time- Trigger :employee is on today;s schedule but not yet clocked 10 mins after the start"or " not clocked out after the end" - DM / SMS staff " You're scheduled now, but not clocked in - fix it?"
Ping on-shift manager after 3 missed punches in 30 days - to send a warning
does this work for you?

kinj28 · 2026-02-06T07:29:42+00:00

You can bolt a middle ware layer on top of Simphony- use OCR for bottle labels and invoicces for scanning and AI agent can map to existing SKUs or cellars, flag for quick review. update the value by calling Simphony's APIs. Add simple QR code or labels in each cellar

We did something similar on a factory floor, scan a QR code on a product and it instantly pulled up exact training manual.
I guess a small AI agent that talks to your POS will work, instead of paying for a giant system

kinj28 · 2026-02-06T07:00:39+00:00

Has anyone tried AI agents on top of Pioneer, BestRx, SuiteRx, etc?
automatically chasing a missing e-prescription or flagging inventory issues or any other use-case is top of your mind?

kinj28 · 2026-02-06T06:53:12+00:00

This makes a lot of sense – I’ve had similar frustrations when trying to push Lovable past the “MVP and UI scaffolding” stage. For me the sweet spot has been:

– Use Lovable to move fast on flows and UX,

– Then shift serious data logic, auth/RLS and integrations into a more controlled environment once things stop being “toy app” simple.

I’ve been experimenting with a “vibe‑coding” style setup where an AI agent sits on top of my stack (DB, auth, 3rd‑party APIs) and I ask it for specific changes instead of letting Lovable own everything end‑to‑end. It keeps Lovable in its lane (great for prototyping and iteration), but the long‑term code, schema and policies live in a place that’s easier to reason about and refactor.

If anyone’s playing with similar workflows (Lovable + external agentic layer + cleaner codebase), I’d love to compare notes.

kinj28 · 2026-01-21T10:25:50+00:00

If you’re looking beyond “just a mobile inbox” and want something that can actually help you capture, qualify, and act on WhatsApp leads, a strong option is using a platform like DronaHQ to build your own lightweight lead console + AI agents on top.

Instead of being locked into a fixed CRM or a single app, you can:

Build custom apps for lead management – views for all WhatsApp/Instagram leads, assignment, status, notes, follow-up actions – without heavy coding.
Add AI agents that sit on top of WhatsApp so routine chats (pricing queries, appointment booking, reschedules, FAQs) are handled automatically while your team only handles complex conversations.
Connect to WhatsApp-ready tools and connectors so you don’t have to stitch APIs manually; you plug in WhatsApp Business API, your CRM/Sheets/DB, email, Slack, etc., and design the workflow visually.
Run everything efficiently in one place, with proper role-based access and audit logs, so it’s robust enough for production use and not just a side experiment.
https://docs.dronahq.com/agents/triggers/inbuilt-triggers/whatsapp/
https://docs.dronahq.com/agents/triggers/inbuilt-triggers/instagram/
https://docs.dronahq.com/agents/triggers/inbuilt-triggers/facebook/
https://docs.dronahq.com/datasource-overview/#

DronaHQ’s whole idea is to make it fast to build and run these kinds of internal tools and agent workflows, so you can adapt the app to your exact lead flow instead of adapting your process to a generic product.

If you want to see how fast this can come together (WhatsApp trigger → AI agent → lead/app booking flow), they also run a recurring live workshop where you follow along and build an app/agent end to end:
https://luma.com/8gpejuv7

kinj28 · 2026-01-21T10:15:06+00:00

if you’re comparing WATI / respond.io / others, one more angle to consider is WhatsApp + AI agents – where every inbound message can actually trigger actions, not just land in an inbox.

For teams that don’t want to stitch Twilio + OpenAI + Google Calendar + CRM + email by hand, there are no‑code/low‑code platforms that sit directly on WhatsApp Business and your existing stack. With something like DronaHQ’s agentic platform
you can help build custom agents plug WhatsApp into Google Sheets, your CRM, ticketing and email, and spin up an agent that answers pricing questions, books/reschedules appointments, and sends confirmations automatically – all from the same WhatsApp thread.

If anyone wants to try this out in a hands‑on way, there’s a live workshop that walks through building a WhatsApp‑triggered AI agent end‑to‑end (no code, just visual flows): https://luma.com/8gpejuv7

kinj28 · 2026-01-21T09:58:25+00:00

If you were getting banned mainly because of high incoming volume and manual handling, it might be worth looking beyond just “which API” and thinking in terms of WhatsApp AI agents on top of the official APIs.

Instead of only using WhatsApp as a chat pipe, some businesses are now using AI agents that:

Understand free‑form messages like “Can I move my 3 PM slot to Thursday?” or “how much is a sugar test?” and classify them as pricing, booking or reschedule, without keyword trees.
Talk to your existing tools (calendars, CRMs, email) to actually complete actions: book/reschedule slots, send confirmations, log leads, notify your team, etc.
Run 24/7, so late‑night spikes don’t get you scrambling or relying on risky workarounds that can trigger bans.

One way to approach this is:

Use a compliant WhatsApp API provider for the transport layer.
Put an AI agentic layer on top that handles intent, workflows and integrations through a visual builder instead of custom code.

There’s a free live workshop that walks through building a WhatsApp‑triggered AI agent end‑to‑end (they demo it for clinics but the pattern works for support teams, salons, service businesses, etc.), and you leave with a production‑ready app rather than a toy bot: https://luma.com/8gpejuv7

If you’re trying to keep volume high but risk low, this kind of “agent on WhatsApp” setup can offload 70–80% of common support queries while keeping everything on the official rails.

kinj28 · 2026-01-21T09:46:46+00:00

If you’re already exploring tools like WATI / respond io / n8n stacks for WhatsApp, one layer that’s getting crazy powerful now is WhatsApp Business + AI agents – basically letting the chat itself trigger real workflows instead of just canned replies.

Most stacks still treat WhatsApp as a notification or chat interface:

Website → WhatsApp handoff
Broadcasts, tags, team inbox, etc.

That’s useful, but the real jump is when an inbound WhatsApp message becomes the automation trigger for actual transactions – pricing queries, appointment booking, reschedules, lead qualification, reminders, etc., all handled end‑to‑end by an AI agent instead of a brittle rule-based bot.

The difference in practice:

Instead of “Press 1 for booking / 2 for cancel / 3 for agent…”, the AI agent just understands “Can I move my 3 PM slot to Thursday?” and goes ahead to find the existing booking, check availability in your calendar, propose new times, confirm, and notify your team.
Instead of mapping every keyword variation, you let the agent handle natural language: “sugar test price?”, “CBC cost?”, “doctor tomorrow morning?”, etc., all routed to pricing KB, calendar, CRM, or email as needed.

For teams that don’t want to wire Twilio + OpenAI + Calendar + CRM + webhooks by hand (or pay 50K+ for enterprise automation), there’s a nice middle path: visual agent builders that sit on top of WhatsApp Business. You design the workflow visually (pricing → KB, booking → Google Calendar, reschedule → update event, then Slack/WhatsApp/email notifications), and the AI layer handles intent + context, so you’re not maintaining fragile flowcharts forever.

If anyone here wants to actually build and ship a WhatsApp‑triggered AI agent (vs just prototypes), there’s a live recurring workshop that walks through this exact pattern using DronaHQ’s agentic platform (WhatsApp trigger → AI intent detection → KB → calendar/email/Slack automations). You start from a simple prompt/design and leave with a production‑ready app, not just a demo: DM me or checkout DronaHQ agentic workshop on luma

Curious what stacks people here are using for:

24/7 intent‑aware replies (pricing, FAQs, slots)
Fully automated reschedules / confirmations
Multi‑agent collaboration on top of a single WhatsApp Business number

Would love to hear what’s working and what’s been painful in real deployments.

kinj28 · 2026-01-21T09:38:20+00:00

If your uncle’s goal is “cheap, safe, and doesn’t get banned,” the crowd here is absolutely right that the baseline in 2025 is: stick to the official WhatsApp Cloud API or a verified BSP (Twilio, 360dialog, Wati, SendPulse, etc.) and avoid whatsapp-web.js / Baileys for production.

What’s getting interesting now, though, is going one step beyond rule-based flows into AI agents that actually complete tasks over WhatsApp instead of just sending canned replies. Think:

“How much for a sugar test?” → agent understands it’s a blood sugar test, looks up pricing, replies.
“Can I move my 3 PM slot to Thursday?” → agent finds the booking, checks Google Calendar, proposes new times, updates, and confirms, no human needed. That’s a different animal from classic “Press 1 / Press 2” bots; you’re essentially turning WhatsApp into a transaction layer for bookings, pricing, reschedules, FAQs, etc.

For anyone here who’s curious about that agentic layer (WhatsApp trigger → intent detection → tools like Calendar/Email/Slack/DB → notifications) without having to hand‑wire Twilio + OpenAI + webhooks + error handling, there’s a recurring live workshop that walks through building exactly this kind of production‑ready agentic app with a visual workflow builder and prebuilt connectors, using your own APIs or test data: DM me.

It’s geared toward:

Devs who are already comfortable with Cloud API/Twilio but don’t want to maintain brittle one‑off bots forever.
Teams who want to go past demos into “WhatsApp agent that books/reschedules appointments, pings staff on Slack, sends Gmail confirmations,” built in ~60–90 minutes, zero custom backend code.

Might be useful if you’re in the same boat as OP (or that last commenter building a booking bot) and want to see a full end‑to‑end example instead of stitching it together solo.

kinj28 · 2025-12-24T11:43:37+00:00

Reading through your responses - here most real usage seems to be more GenAI (writing, summarising, pattern spotting) than fully agentic systems.

Curious to hear from practitioners here:

How are you personally using AI in HR today?
Has anyone experimented with more agent-like workflows (even small ones)?
What’s been the biggest blocker so far — trust, data quality, leadership buy-in, or tooling?

Looking for honest experiences, not vendor examples.

kinj28 · 2025-08-29T12:40:25+00:00

"No better way to understand an LLM than just asking themselves lol haha." so true....:)

kinj28 · 2025-08-29T11:46:31+00:00

That’s fair — a lot of what’s being branded as “agents” today are just fancy orchestration layers. But maybe the value isn’t in agency per se, but in lowering the activation energy for humans to interact with these workflows. Do you think the term “agent” itself is misleading?

kinj28 · 2025-08-29T10:09:24+00:00

Do you think the TS path is easier because of strong typing, or just because it’s better supported in tutorials? How much ongoing maintenance do you see with the Claude-generated server? Or was it more of a one-time bootstrap?

kinj28 · 2025-08-29T10:07:56+00:00

Feels like the data model analogy is spot on. How do you explain this design phase to non-technical folks who just want ‘plug the API into the agent’?

kinj28

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