u/watraders

I built an AI agent to manage Meta ads for my wife's small ecom store in Pakistan. Full access to the ad account via API. Monitor campaigns, track ROAS and ACOS, flag issues, recommend changes.

First real audit, it told me I'd spent 2.8M PKR in a period where Ads Manager showed 22k. Stated with total confidence, nicely formatted tables and everything. I only caught it because I know my own numbers.

Here's the part that changed how I think about agents. Instead of opening a workflow editor and tracing nodes, I challenged it in chat. Told it I didn't trust it. Then asked it to audit its own instructions and tell me what was missing. It came back with its own diagnosis: it was trusting raw API output without sanity-checking against my stated reality, reporting spend without campaign-level verification, and never flagging anomalies. It proposed adding a data integrity protocol and a "financial controller" role to its own system prompt. I approved, and that's now baked into its permanent instructions and memory, not just that one conversation.

It's not done. It still needs output validation before I'll trust a single number it gives me, and it fell over when I added browsing tools. But coaching an agent like a junior employee, and having the correction stick across sessions, feels fundamentally different from debugging a Zapier or n8n flow every time something drifts.

Question for people building agents on n8n, Make, or code: how do you handle an agent confidently reporting wrong numbers? Prompt-level guardrails, hard output validation against source of truth, or do you just keep a human in the loop forever?

I built an AI agent to manage Meta ads for my wife's small ecom store in Pakistan. Full access to the ad account via API. Monitor campaigns, track ROAS and ACOS, flag issues, recommend changes.

First real audit, it told me I'd spent 2.8M PKR in a period where Ads Manager showed 22k. Stated with total confidence, nicely formatted tables and everything. I only caught it because I know my own numbers.

Here's the part that changed how I think about agents. Instead of opening a workflow editor and tracing nodes, I challenged it in chat. Told it I didn't trust it. Then asked it to audit its own instructions and tell me what was missing. It came back with its own diagnosis: it was trusting raw API output without sanity-checking against my stated reality, reporting spend without campaign-level verification, and never flagging anomalies. It proposed adding a data integrity protocol and a "financial controller" role to its own system prompt. I approved, and that's now baked into its permanent instructions and memory, not just that one conversation.

It's not done. It still needs output validation before I'll trust a single number it gives me, and it fell over when I added browsing tools. But coaching an agent like a junior employee, and having the correction stick across sessions, feels fundamentally different from debugging a Zapier or n8n flow every time something drifts.

Question for people building agents on n8n, Make, or code: how do you handle an agent confidently reporting wrong numbers? Prompt-level guardrails, hard output validation against source of truth, or do you just keep a human in the loop forever?

I run a small SaaS and I have been building an agent to handle our social media on a schedule with no human in the loop. Yesterday was its first real unattended run on live accounts. I want to share the actual result, including what broke, because most "look at my AI agent" posts only show the happy path.

What it is supposed to do each run:

- check when it last posted so it does not double post

- pull a topic from our knowledge base and pick an angle and audience

- write the caption and generate an image

- publish to Facebook and Instagram

- read and reply to new comments

- pull the post analytics

- save what it did to memory so it does not repeat itself

- email me a report

What actually happened on the first run:

- It chose a solid topic on its own (early signs an email list is going stale) for the right audience.

- Instagram failed on the first publish attempt. It retried and the post went live.

- Our blog was not connected (it hit a 404), so it skipped that and used the knowledge base instead.

- The analytics step failed on both platforms with Graph API metric errors. It logged them in the report and kept going instead of crashing.

- The report emailer had an SMTP config gap, so it fell back to another email path and still delivered the report with the image attached.

- Both posts ended up live and confirmed. It finished all ten steps.

What I took away: the interesting part of agents is not the happy path, it is whether they degrade gracefully. This one hit four real problems and worked around all of them without me touching it.

Happy to answer questions on the setup, the guardrails I gave it so it does not post nonsense, or how I deal with it publishing with no human review. For transparency, I am the co-founder of the platform I built it on, so ask away and I will keep it straight rather than pitch you.

​

پھر اسی موڑ پر خود کو کھڑا پاؤں

یقین الفت کو پھر سے لرزتا پاؤں

اپنائیت و بےتکلفی میں دراڑ ہو جیسے

دل مضطرب پھر بیقرار ہو جیسے

تیرے فراق میں صبح شام کا انتظار

قیامت خیز ہے میرے قلب کا انتشار

غیر مستحقم ہو، بيقینی ہے، بد اعتمادی ہے کیا؟

میرے اسلوب میں دیکھی نئی کوئی خرابی ہے کیا؟

اتنی سرد مہری و بے اعتنائی کے وجہ کیا ہے؟

کیا ہے جرم کیا اور میری سزا کیا ہے؟

نہ تائید، نہ درستگی نہ مخالفت، نہ الزام

غیر محسوس انداز سے سوالوں کو نظر انداز کرنا

خامشی اور تنہائی کے دامن کے پس پردہ جناب

کچھ نہ کہہ کر میرے جذبات کو باطل کرنا

خنجر زنی سے تیری، چھلی ہے بسمل دل فگار

وقیل صفائی بھی تیرا ،خود ہی ڈھونڈتا پھرتا ہے جواز

Assalam o Alaikum everyone,

I wanted to share one small Urdu / Roman Urdu piece here for honest feedback.

I actually have some old writing collected on a personal blog, but I am not sharing the link here because I don’t want this to come across as self-promotion or advertising. I would rather share one piece directly and learn from people who understand Urdu better than I do.

I fell in love with Urdu poetry back in boarding school. We were O Level students but were being taught Urdu literature meant for FSC students, and honestly I used to struggle with it a lot. Then a new Urdu teacher came in, and he completely changed the way I looked at Urdu, poetry, expression, and language.

I started writing secretly in my teenage years. A lot of those early pieces got lost. Some survived. Some I wrote later. I never really had the confidence to share them openly.

Now that I have crossed 40 and have enough grey hair, I thought maybe it is time to stop hiding everything in old notebooks and folders.

I am not posting as someone who claims to be a poet. I would genuinely appreciate honest feedback, correction, critique, or encouragement if the writing deserves it.

Here is one piece:

Khamooshi Ke Khanjar

tumharey Paas Wajah bhi nahi
taweel lambhi bey itnai ki
acahanak ghaib ho janaa
aur maheenon judai ki

yahan yeh aalam hai ab key
main kuch kehna bhi chahon to
ajnabhi ajnabi lagta hia aur
main jhijak sa jataa hoon

faraq raton main mainey
sadiyan bitain hai
qarb e intazar aiysa,
jaisey hadiyan galaain hon

sawalon main kuredi hain,
tumharey man ki aashaa
mili bus khamashi key
khanjiron sey faskh ki bhasha

kabhi kuch kehna bhi chahon
to yeh ihsaas hota hia .
tumhari tarf sey jaisey
bus ab inkaaar hota hia

kabhi is ravaiye per hairat zada tha main
ghamgeen o afsurda , haal e shikasta main

phir jaisey waqt ik danaa tabeeb ki tarhan
meri bechainiyon ko aasoodgi key dawa sey bharta

abhi bhi aksar tumhari yaad aati hai
man hi man muskurtaa hoon
tumhain likhna bhi chahta hoon

Thank you to anyone who takes the time to read it.

I recently reviewed some old life insurance / investment-linked policies that I started many years ago.

One of them is almost 18 years old.

And honestly, it was a very humbling personal finance lesson.

When I started these policies, I was young. I was ambitious. I wanted to build something for rainy days, retirement, family security, and future goals.

But I was also naive.

I did not understand personal finance properly.

I thought if I was disciplined, paid premiums every year, and stayed invested long term, I would automatically be making a smart financial decision.

Now, after looking at the numbers properly, I realize that is not always true.

On paper, the policies did grow.

In Pakistani rupee terms, the total value is almost double the total premiums paid over the years.

The annualized return came out roughly in the 6% to 10% range depending on the policy. Combined, around 7.5% per year in PKR terms.

At first glance, that does not sound terrible.

But then I asked the real question.

Did this actually grow my wealth?

That is where the picture changed.

Because in Pakistan, you cannot just look at rupee value.

You have to ask:

What happened to inflation?

What happened to the rupee against the dollar?

What happened to gold?

What was the opportunity cost?

When I compared the policies against inflation, the real return was weak.

When I compared them against the dollar, the result was worse. In dollar terms, the policies were roughly behind what I would have had if I had simply converted the same yearly premiums into USD over time.

And when I compared them against gold, the gap was painful.

The same yearly contributions into gold would have been several times higher than the current value of these policies.

That was the part that hit me.

The rupee number made me feel like I had gained.

But the purchasing power story was very different.

To be very clear, this is not an attack on EFU or any specific insurance company.

Insurance companies are businesses. They sell products. Some products have a place. Life insurance can absolutely be useful when the goal is protection.

My issue is with how most ordinary investors understand these products.

And honestly, how many agents sell them.

Over the years, I spoke to many insurance representatives.

Almost every conversation was focused on selling a policy.

Very few, if any, actually helped me evaluate my real future needs.

Nobody seriously sat with me and asked:

How much protection does your family actually need?

What is your retirement number?

What return do you need after inflation?

What happens if the rupee loses 50% or 70% of its value?

How much should go into insurance, and how much should go into other assets?

Should this be term insurance plus separate investments?

What is the surrender value?

What is the actual IRR after charges?

What portion of the premium is actually invested?

What are the mortality charges, admin charges, fund charges, and rider costs?

That kind of conversation rarely happens.

Instead, people are shown projected future values.

Sometimes at 10%, 12%, 15%, even 18% assumed returns.

Yes, there are disclaimers.

But for an average person, those projections create a very rosy picture.

And most people do not know how to question them.

I did not know either.

If I knew then what I know now, I would have done things differently.

I would have separated insurance from investment.

I would have bought protection for protection.

And invested separately for wealth creation.

A more balanced approach would have included some combination of cash, gold, USD exposure, mutual funds, stocks, business assets, maybe real estate, depending on the person’s goals and risk tolerance.

Thankfully, later in life I did diversify.

But looking back, I can clearly say this:

Using insurance policies as a primary investment vehicle was one of the least attractive financial decisions I made.

Not because insurance is useless.

But because I used the wrong tool for the wrong job.

My lesson for anyone considering these policies:

Do not ask, “How much will I get after 20 years?”

Ask better questions.

What is the guaranteed value?

What is not guaranteed?

What is the actual surrender value?

What is the IRR if I pay every year?

What happens after inflation?

What happens in dollar terms?

What happens if I compare it with gold?

How much of my premium is actually invested?

What are the yearly charges?

What is the real insurance cover?

Is this for protection, saving, investment, tax benefit, or emotional comfort?

Because each goal needs a different tool.

Insurance is protection.

Investment is wealth creation.

Forced saving is discipline.

Do not confuse the three.

My biggest lesson:

Starting early is not enough.

Being disciplined is not enough.

You also need financial literacy.

Otherwise you can spend 15 to 20 years doing the “responsible” thing and still find out that your money did not really grow in real terms.

This is not financial advice.

Just a lesson I learned the hard way.

We are hiring confident and disciplined Outbound SDRs / Appointment Setters in Pakistan for a North American AI receptionist campaign.

LeanPBX is an AI receptionist and business phone system for modern businesses. We help businesses answer missed calls, capture caller details, qualify leads, route calls, and follow up faster.

Your role will be to call business owners in the US and Canada, explain how missed calls and voicemail can cost them customers, introduce the LeanPBX AI Receptionist, and book qualified demos with our founder.

This is a remote, long-term role with base salary plus commission.

Job Responsibilities

• Call business owners in the US and Canada

• Make outbound cold calls daily

• Speak with receptionists, gatekeepers, and decision-makers

• Start conversations and identify pain points

• Explain how missed calls can cost businesses customers

• Introduce the LeanPBX AI Receptionist in simple language

• Get prospects interested in testing or booking a demo

• Book qualified appointments with the founder

• Send follow-up messages or emails where required

• Update CRM records after every call

• Track calls, conversations, follow-ups, and booked demos

• Follow daily targets and report your activity

Requirements

• Strong spoken English

• Clear pronunciation

• Confidence on phone calls

• Experience in cold calling, outbound sales, appointment setting, telemarketing, or call center campaigns

• Comfortable speaking with US / Canadian business owners

• Ability to handle rejection professionally

• Good follow-up discipline

• Basic CRM or spreadsheet discipline

• Reliable internet, headset, laptop/computer, and quiet work setup

• Serious work ethic and performance-driven mindset

Preferred Experience

• US or Canadian outbound campaigns

• B2B sales or appointment setting

• Service business, agency, real estate, insurance, medical, or local business campaigns

• Cold email, LinkedIn outreach, or lead generation

• CRM tools like GoHighLevel, HubSpot, Zoho, Salesforce, Pipedrive, or similar

• Dialers, VoIP tools, or call center software

Working Hours

Evening / night shift Pakistan time
Monday to Friday
Expected timing around 6:00 PM to 2:00 AM PKT
Exact hours will be discussed during the interview

Compensation

Base salary plus commission.

Commission will be paid for qualified demos booked and attended. Additional bonus may be offered when a booked demo converts into a paid customer or pilot.

Strong performers can earn PKR 150,000 to PKR 250,000+ per month based on performance.

This is not a fixed-salary-only role. We are looking for people who want to grow, earn commissions, and build a long-term career with an AI voice technology company.

What We Offer

• Remote work opportunity

• Sell a real AI voice product with a live demo

• Training directly from the founder

• Base salary plus commission

• Long-term growth into senior sales, team lead, or campaign manager roles

How to Apply

Send your CV and a short 60-second voice or video pitch answering this:

“Imagine you are speaking to a service business owner in the US. Explain why missed calls and voicemail may be costing them customers, and why they should test an AI receptionist.”

You may use Loom, Google Drive, YouTube unlisted, or any shareable link.

Email: hr@leanpbx.com
Subject: Sales Agent Application - Your Name

Applications without the short recording may not be shortlisted.

I’ve been working on an AI receptionist inside our phone/CRM platform, and one thing became obvious pretty quickly:

Getting AI to answer a call is not the hard part anymore.

The hard part is making sure the business actually gets something useful after the call.

A lot of AI receptionist demos sound impressive for 30 seconds, but then the call ends and the business still has to figure out:

who called

what they wanted

whether they were new or returning

what details were captured

whether someone needs to call back

whether the CRM was updated

whether the lead should be routed somewhere

what happened on the last call

So we started building the agent around the workflow after the conversation, not just the voice conversation itself.

The latest version lets you enter a business name and website.

The system researches the website, reads the public pages, and creates a starter AI receptionist setup:

suggested agent role

greeting

business context

likely caller types

intake questions

knowledgebase

special instructions

editable prompt and behavior

You can review and change everything before saving it.

Once saved, the agent can answer inbound calls, ask intake questions, search the knowledgebase, transfer the caller, create or update CRM records when configured, add call notes, and send structured summaries to the team.

We also added post-call details like transcripts, call summaries, caller info, sentiment, and full CDR history.

The part I’m most excited about is the tool layer.

You can paste API docs or integration notes, and the system helps build and test an API tool that the agent can use during or after the call. That opens up use cases like email verification, address checks, booking workflows, pipeline updates, lead creation, webhook triggers, database lookups, order lookups, and similar workflows.

It is still not “magic.” Some integrations need review and guardrails. But it is getting much closer to what I always wanted:

An AI receptionist that does not just talk.

It captures the call, remembers context when useful, updates the system, and helps the team follow up with the right information.

I’m testing this now with call-driven businesses like HVAC, contractors, windows and doors, florists, med spas, restaurants, plumbers, electricians, and similar teams where missed calls or messy intake can cost real money.

Curious how other founders would think about positioning this.

Would you lead with:

AI receptionist built from your website

AI receptionist connected to CRM and follow-up

Missed-call / after-hours lead capture

Post-call automation and workflow tools

I’m leaning toward #2 because answering the call is only half the job.

Leanpbx.com