HUB47 · AI TRANSFORMATION FOR BETTER BUSINESS · WORKSHOP 3
Power Up Your Business With AI
Automation and AI Agents for Entrepreneurs
Anh Nguyen · Founder, Applify Lab · Dubai
2
AGENDA
Five parts, two demos
01
Why agents get things wrong
and why context, not the model, decides it
02
The three foundations
Knowledge Base · Skills · Tools
03
Four ways to build
from the subscription you already pay for, to custom code
04
WhatsApp, specifically
what Meta actually allows in 2026
05
Multi-agent and the “one-person company”
what's real and what isn't
Goal: Understand AI Automation and implement AI in your business after this session
01
Why agents get things wrong
It isn't lying to you. It's guessing — because that's what it was rewarded for.
4
WHY IT MAKES THINGS UP
Your AI is a student who never leaves an answer blank
Think about how it was trained. Like an exam where a wrong answer and a blank answer score exactly the same: zero.
So guessing always wins. A blank gets you nothing. A guess sometimes gets you a point. Do that a few million times and you get something that never says “I don't know.”
It isn't lying to you. It has simply never been rewarded for admitting it doesn't know.
And it's fixable. The same researchers showed a model can be built to say “I don't know.” Most just aren't asked to.
The difference between a useful assistant and an embarrassing one is usually permission to say “I don't know.”
Kalai, Nachum, Vempala & Zhang, “Why Language Models Hallucinate,” OpenAI, Sept 2025
5
WHAT SCALE FIXES, AND WHAT IT NEVER WILL
Cats and birthdays
Patterns → solved by scale
Spelling. Grammar. Sentence structure. These follow consistent patterns, so errors shrink as models get bigger. Nothing you do is needed here.
Arbitrary facts → never solved
A pet's birthday can't be predicted from a photo — OpenAI's own analogy. No amount of training data fixes it, because there is no pattern to learn.
Your Tuesday price, your refund window, this patient's slot, that client's terms — all birthdays.
The rule: arbitrary facts must be GIVEN to the model, never RECALLED by it. That is what the rest of this talk is about.
OpenAI, Sept 2025
6
CONSEQUENCES
None of these were hard questions
Air Canada, Feb 2024
Its chatbot invented a bereavement-refund policy. The airline argued in tribunal that the chatbot was “a separate legal entity” responsible for its own answers. It lost.
Deloitte Australia, 2025
Fabricated references inside a A$440,000 government report. Partial refund, corrected version published.
Courts worldwide
Over 1,500 decisions by mid-2026 where a judge found a party filed AI-invented material.
Every one of these was a gap in context, filled with the most plausible-sounding thing available.
Moffatt v. Air Canada 2024 BCCRT 149 · Fortune, Oct 2025 · AI Hallucination Cases database
7
THE OBVIOUS FIX THAT ISN'T
More information is not better information
The instinct is to give it everything. Upload the whole drive and let it work the answer out.
Tested across 18 leading AI systems: the same question, answered from one page of the right material, beat the same question buried in three hundred pages that contained it.
One nearly-right document is enough to pull the answer off course. A few of them make it worse.
A bigger memory doesn't fix this. It moves the point where things start to slip. It doesn't remove it.
Giving it more to read is not the same as giving it the right thing to read.
Chroma Research, “Context Rot,” Jul 2025 · Anthropic Engineering, Sept 2025
8
DIAGNOSIS
Five ways your context fails
1
Missing
The answer simply isn't there — so it fills the gap
2
Conflicting
Two documents disagree and nothing says which one wins
3
Too much
You dumped the whole drive in and buried the answer
4
Stale
Last year's price list. Last quarter's policy. Nobody told it.
5
No exit
No permitted way to say “I don't know” or “let me get a human”
All five are engineering decisions. All five are yours to make — not the model vendor's.
“
You are not buying intelligence. Your competitor can buy the same intelligence tomorrow morning, for the same twenty dollars.
The whole of the next hour is about the other half of that sentence
You are supplying the context — your procedures, your prices, your customers, your tone. That part nobody can buy. The industry phrase for doing it well is “context engineering”: giving the model the smallest set of genuinely useful information, and nothing else.
02
The three foundations
Knowledge Base · Skills · Tools. Miss one and you have a demo, not a system.
11
THE ANATOMY
Every working agent is three things you supply
1
Knowledge Base
what it knows
Your prices, policies, services, past work. Lots of documents — the agent has to find the right one and quote it back.
2
Skills
how you do it
The way you do a job, written down. One right way, followed every time — the same thing you'd teach a new hire.
3
Tools
what it can reach
Your calendar, your customer list, today's prices, what's in stock. The things that change by the hour and must be looked up, not remembered.
The model is the same one your competitor can buy. These three are not.
12
FOUNDATION 1 — KNOWLEDGE BASE
Where does each thing belong? One test.
If it looks like this…
…it belongs here
Example
There are lots of them, and it has to pick the right one
Knowledge base
Service descriptions, policies, FAQs, past proposals
One right way to do it, every single time
Skill
How we qualify a lead. How we handle a no-show. Our tone in Arabic.
It changes by the hour, and something else already tracks it
A live lookup
Today's price. This week's availability. Where this lead got to.
Anything with a per-customer or per-day value must never live in a document.
13
FOUNDATION 1 — THE HOMEWORK
The Knowledge Base Blueprint
1
General business info
Name, hours, address, contact channels, industry
2
Customer profiles
Who you serve best — and the tone to use with each group
3
Services & ecosystem
Core offerings, how they connect, what to recommend alongside
4
Packages & pricing
Tiers, starting prices, payment methods, cancellation terms
5
Brand & positioning
Mission, jargon to translate, pain points, USP, the action to drive
6
Operating guardrails
Allowed outbound hours, timezone rules, when a human takes over
The full question list as worksheet will be sent out after the workshop.
14
FOUNDATION 2 — SKILLS
A Skill is your SOP, written down, loaded on demand
It is a folder with one plain-English file in it. Not code. Anthropic shipped the format in October 2025; it became an open standard that December.
Two required fields: a name, and a description of what it does and when to use it. A vague description means the skill never triggers.
Loaded only when it's needed: the agent always sees the description, reads the rest only when the task matches. Everything else stays out of the way.
44 agent products now read the same format — including OpenAI, Google and Microsoft's tools. A standard, not one vendor's feature.
Analogy: “like putting together an onboarding guide for a new hire.”
Anthropic Engineering, 16 Oct 2025 · agentskills.io, accessed 9 Aug 2026
15
GETTING THE DISTINCTION STRAIGHT
Prompt · Skill · Knowledge Base
A prompt
One-off. Dies with the chat window. Quality depends entirely on who typed it and how much of a hurry they were in. Not a business asset.
A skill
A reusable procedure. Versioned, reviewable, improvable. Same output whoever runs it, on a Monday or a Thursday. This is the asset.
A knowledge base
The facts the procedure reads from. Separate on purpose: you update the price list without rewriting the procedure.
A prompt is a conversation. A skill is a process.
Which is why the question is never “which AI tool should I buy?” It's “which of my processes is written down?”
16
FOUNDATION 2 — CRAFT
What makes one work, and why they fail
WHAT MAKES ONE WORK
Write down the surprises. The things a new hire gets wrong on day one.
Show an example of the output. Better than describing it.
Be strict where it matters. Loose everywhere else.
WHY THEY FAIL
Too vague. The AI never realises it should use it.
Too long. It can't find the part that matters.
Written by an AI. It doesn't know your business. Looks finished, says nothing.
The test for every line: would it get this wrong without it? If no — cut it.
agentskills.io — best practices, accessed 9 Aug 2026
17
DEMO · REAL-ESTATE RESEARCH
One of these had the skill
WITHOUT the Skill
WITH the Skill
Left — no skill. Prose. Not wrong, but generic.
Right — with the skill. Ask question about your situation. Traffic lights, five checks, the yield you were told against the yield you'd get, stress tests, red flags.
It shows its working. Sources cited, and three items flagged as unverified.
Not a smarter AI.
The same AI, given a written procedure — what to check, in what order, and what the answer should look like.
18
WHAT A SKILL ACTUALLY LOOKS LIKE
Example 1: this is the whole file
name
grill-me
description
Interview the user relentlessly about a plan or design until reaching shared understanding, resolving each branch of the decision tree. Use when user wants to stress-test a plan, get grilled on their design, or mentions “grill me”.
Interview me relentlessly about every aspect of this plan until we reach a shared understanding. Walk down each branch of the design tree, resolving dependencies between decisions one-by-one. For each question, provide your recommended answer.
Ask the questions one at a time.
If a question can be answered by exploring the files, explore them instead of asking me.
19
WHAT A SKILL ACTUALLY LOOKS LIKE
Example 2: the skill behind this deck
one folder, shown in two halves
Same idea, more parts. A folder instead of one file: the instructions, plus the templates, brand assets and scripts they point at.
It builds every deck we send. Fonts, colours, layouts, the checks that catch a broken slide. Including this one.
Nobody starts here. This began as a single page. The rest got added the fifth time I explained the same thing.
Start with the one-pager. Turn it into a folder once you've explained the same thing three times. Continuously improve it
20
FOUNDATION 3 — TOOLS
The hands: how the agent reaches your systems
MCP is a standard plug. One connector shape, so an AI can reach your calendar, customer list or accounting system instead of you copy-pasting.
Nobody owns it. Anthropic built it, then handed it to the Linux Foundation — the same neutral home as the software behind most of the internet.
Over 10,000 of these connectors already exist, and ChatGPT, Claude, Microsoft Copilot and Gemini all accept them.
One question for your developer: the standard changed in July 2026. If something was built for you before then, ask whether it still needs updating.
Skills are the instructions. Tools are the hands. Neither is much use without the other.
modelcontextprotocol.io · Linux Foundation press release, 9 Dec 2025
DEMO
One instruction. No hands on the keyboard.
I asked for a list of prospects. It opened the browser, searched LinkedIn, read the profiles, and came back with the research done.
22
FOUNDATION 3 — GOVERNANCE
Six rules before you connect anything
1
Read-only first
Let it read before you let it write. That removes most of the damage, not all of it.
2
Give it the narrowest key
Access to one thing, for one job. Never the master key to everything.
3
Approve changes properly
You have to see exactly what it's about to do, not a summary of it. That's how real attacks got through.
4
Check what you plug in
What you approved on Monday can quietly change on Tuesday. Re-check anything you didn't build.
5
Connect fewer things at once
The more it can see in one session, the more ways there are for one of them to mislead it.
6
Treat customer input as hostile
A ticket, a message, a CV, a review — all text a stranger wrote. Agents follow instructions hidden in it.
“Untrusted content” isn't hackers. It's a ticket, a review, a CV — anything a stranger can type.
03
Four ways to build
Same three foundations at every level. Only the machinery around them changes.
24
THE LANDSCAPE
Four routes, honestly compared
Route
Who it's for
Typical cost
Where it breaks
1 · Your existing subscription
Claude Cowork · ChatGPT Work
You, on your own work
$20–125
per seat / month
One person's identity. No SLA, no retries.
2 · Drag-and-drop tools
n8n · Make · Zapier
Work that must run without you
$20–667 / month
plus build time
Hard to fix, and it fails quietly.
3 · Run-it-yourself agents
OpenClaw · Hermes Agent
Tinkerers and technical founders
Free, plus what the AI costs
and a server
Security. You are the support team.
4 · Custom code
Mastra · LangGraph · Agent SDKs
Products, regulated work, promises you signed
Build + ongoing
upkeep
Too early, nine times out of ten.
Nobody moves up a level because the tool is better. They move up because they hit a wall they can name.
25
ROUTE 1
The subscription you already pay for
Cowork inside Claude, Work inside ChatGPT. Built for everyday office work rather than coding — and included in plans you may already be paying for.
You give it a job, not a question. “Draft the partner announcement.” “Turn this contract into a template.” It goes away and does it.
It carries on when you don't. Work continues after you close the laptop, and it stacks up what's ready for you to look at.
It stops and asks. Approve it, answer its question, or send it back. Nothing goes out without you.
Where the majority of AI automation use cases can be accomplished.
26
ROUTE 2
n8n and the drag-and-drop tools
The trigger to move here: “I want it to run without me.”
This one runs every morning. It checks what people are searching for, picks a topic, researches it, writes the post, publishes to three channels, and logs the result.
Eight steps, no code. Every box is something you drag in and fill out.
Where it breaks: big files, no proper history of changes, and no way to test before you ship.
Require a bit of a learning curve.
27
ROUTE 3 — OPENCLAW · HERMES AGENT
An agent that lives in your chat app
Telegram
Slack
Your gateway
one always-on process, on a machine you control
The model you pick
any provider, or one on your own box
Your tools
files · calendar · browser · posting
One always-on program you host yourself. A small server, or a spare laptop that never sleeps.
It answers inside apps you already use. WhatsApp, Telegram, Slack — same thread, same phone.
You pick the brain and the hands. Any model, and only the tools you choose to connect.
What people run on it: scheduling posts, chasing invoices, a morning summary, turning a photo into a finished listing.
It's yours — and so is the security. Fine for your own work. Don't put a client's data behind it.
28
ROUTE 3 — A REAL ONE
One message, and the post is scheduled
Sent from Telegram, on a Saturday. A photo of the meeting flyer and one line: “schedule an Instagram post for tomorrow morning, add a caption.”
It worked the rest out on its own. Read the flyer, wrote the caption, uploaded the image, converted the time to Dubai, and queued the post.
Then it reported back. Account, post ID, status, scheduled time and the exact caption it used.
No dashboard, no laptop — on the same phone you already answer messages on.
29
ROUTE 4
Custom code — when it is unavoidable
WHERE THE OTHER ROUTES STOP
Route 1 stops at “it's you”. One login, one job at a time. If it fails at 2am nobody is told, and no customer can sit behind your account.
Route 2 stops at branching. Fixed boxes in a fixed order. No version history, and no way to test a change before it goes live.
Route 3 stops at security. Your keys, your server, your support rota — and nothing separating one customer's data from another's.
Not one of those says the tool isn't clever enough. They are the same shape: it has to run as the business, not as a person.
SO WHERE IS CODE UNAVOIDABLE?
You sell the agent, not just use it. Many customers on one system, each one's data walled off from the next, and an uptime promise in a contract.
The data isn't allowed to leave. Patient records, case files, banking. Where it is processed is set by a regulator, not by preference.
Every case takes a different path. A claim, a dispute, an application. The agent decides what happens next — a decision tree, not a diagram.
Money or safety moves at the end. A payout, a prescription, a contract term. Every step recorded, replayable and reversible.
Microsoft, in its own agent framework docs: “If you can write a function to handle the task, do that instead of using an AI agent.”
04
WhatsApp, specifically
The channel that matters most here — and the one with the most rules.
32
OPTION A OR OPTION B
WhatsApp is just a doorway
1
Knowledge Base
Services, prices, policies, hours — in both languages, authored not translated.
2
Skills
How you qualify. How you book. How you handle a no-show or a complaint.
3
Tools
Calendar, CRM write-back, payment link, handover. The parts that change hourly.
Option A — Meta runs it
Customer
→
→
Meta
Business Agent
Meta hosts the agent. You configure it through their API.
Option B — you run it
Customer
→
Cloud API
→
Your
agent
→
Your
systems
Meta just carries the message. The logic and the data are yours.
33
META BUSINESS AGENT, UNDER THE HOOD
Meta named the same three things
In this talk
In Meta's own API
What it holds
Knowledge base
Agent Knowledge —
Business Info · FAQs · Files · Websites
Hours, locations, policies · Q&A pairs · uploaded files · your crawled website
Skills
Agent Skills
“System instructions that shape how it responds.” Meta's words, not mine.
Tools
Connectors + Connector Tools
“An external API the agent can call, so it can do more than answer questions.”
Guardrails & handoff
Settings · Allowlist · Thread Control
Persona, language, handoff policy; who it may talk to; when a human takes over
Measuring it
Agent Test + Agent Eval
The with-and-without discipline from part two, shipped as an endpoint
The minimum to switch an agent on is Onboarding plus Settings. Knowledge and connectors are optional — which is exactly how you end up with a bad agent.
Meta for Developers — Meta Business Agent Platform, “Get started”, updated 3 Aug 2026
34
OPTION A — WHEN NOBODY FILLS IT IN
The same agent, without the setup
one conversation, left then right
The first half went well. It asked the right questions — property type, building, paint colour, furnished or empty. Straight from the knowledge base.
Then the customer typed “Human”. Three times. The agent: “I'm not able to help with that. Is there something else I can help you with?”
Nothing was wrong with the AI. Category six of the blueprint was empty. Nobody told it when to hand over, or to whom.
Meta requires a route to a human. It doesn't write one for you — and this is what missing it looks like.
35
OPTION B — YOUR OWN AGENT
The architecture, end to end
Customer
WhatsApp message
→
Cloud API
Meta webhook.
No provider required.
→
Your agent
Skill + knowledge base
+ guardrails
→
Your systems
Calendar · CRM ·
prices · payment link
→
Reply
…or handover
to a human
You do not need a solution provider. Direct Cloud API signup is open. New accounts start at 250 unique recipients per 24 hours and ladder up.
A provider buys you three things: credit terms, faster verification, and throughput above 80 messages a second. The inbox and the AI are software, not provider status.
Meta has billed in AED since April 2026, so a UAE business can be invoiced directly in dirhams.
The one extra box is the whole difference: it's where your knowledge base, your skill and your tools live.
Meta for Developers — Cloud API onboarding, messaging limits and billing, accessed 9 Aug 2026
05
Multi-agent and the one-person company
Where this is going — and how much of it is true today.
37
ORCHESTRATION
When one agent becomes several
The shapes have names, and vendors will use them at you: one agent managing others · a chain where each hands to the next · several working at once · one writing while another checks · one that stops and asks you.
The upside is real: Anthropic's research system beat a single agent by 90.2% — and used roughly fifteen times the tokens of a chat. Same paragraph, both numbers.
The downside is also real: Berkeley tested seven popular multi-agent frameworks and found failure rates between 41% and 87%.
The field changed its mind in public: Cognition published “Don't Build Multi-Agents” in June 2025, then “What's Actually Working” in April 2026.
One agent holds the pen. The others read, review and advise.
Anthropic Engineering, Jun 2025 · Cemri et al., arXiv:2503.13657 · Cognition, Jun 2025 & Apr 2026
39
ECONOMICS
Not every step needs the smartest model
Model
Cost to read
$ per million words
Cost to write
$ per million words
What it's for
Claude Opus 5
10.00
50.00
Hardest reasoning you have
GPT-5.6 Sol Pro
5.00
30.00
Frontier work, long chains
Claude Sonnet 5
2.00
10.00
Everyday drafting
Gemini 3.5 Flash Lite
0.30
2.50
High-volume summarising
GPT-5.6 Luna
0.10
0.60
Classify, extract, route
DeepSeek V4 Flash
0.09
0.18
Bulk tagging and cleanup
Top to bottom of that table is roughly a hundred times the price. Same job, wildly different bill — and most steps belong near the bottom.
OpenRouter list prices, 9 Aug 2026. Priced per “token” in the industry — roughly 750 words per 1,000 tokens; the table converts it to words.
40
THE IDEA WORTH STEALING
The “one-person company”
1
What people mean by it
A business that reaches serious scale without hiring — because software, and now agents, absorb the work a department used to do. Altman put a billion-dollar version of it in a bet in 2023.
2
Why it isn't real yet
He revised it to “two or three people” in late 2025. The company he later called proof has two staff, an FDA warning letter and a class action. Real revenue, and a mess nobody had time to catch.
3
Why it still matters to you
Treat it as a design principle, not a destination: before you add a person or a process, ask what would have to be true to not need one. Then automate that.
Solo businesses adopt AI at about half the rate of businesses with employees — 15% versus 26%. In a room like this, that gap is the opportunity.
Fortune, Feb 2024 · Conversations with Tyler, Nov 2025 · JPMorgan Chase Institute, 2026 (bank payments data)
41
THE CONSTRAINT THAT DOESN'T GO AWAY
Generating is cheap. Checking is not.
AI improves fastest at things that are easy to check — maths, code that either runs or doesn't. It improves slowest where the answer needs judgement. That's most of your business.
41% of knowledge workers report AI output that “looks finished but lacks substance” — about two hours of rework each time.
A randomised trial of experienced developers found they were 19% slower using AI while believing they were 20% faster. Small study — the confidence gap is the point.
Delegate → Review → Own. A named human is accountable, whatever produced the output.
Budget review time explicitly. If you don't, quality slips quietly and you'll blame the model.
Wei, Jul 2025 · BetterUp Labs & Stanford via HBR, Sept 2025 · METR randomised trial, Jul 2025
42
RECAP
Three things, if you remember nothing else
1
It's the context, not the model
Missing, conflicting, stale, buried in noise, or no permitted way to say “I don't know.” None of it is solved by switching vendor.
2
Automate a process you already have
If you can't do it manually and describe it, you cannot automate it. The written procedure is the asset — the tool is interchangeable.
3
You still own the review
Never auto-execute money, contracts, external sends or deletions. Design for wrong, not for perfect.
Everything else in this talk was implementation detail.
43
WHAT TO DO MONDAY
One page. That's the whole assignment.
1
Pick one task you do twice a week
Repetitive, rules exist, low damage if it's wrong once.
2
Write it down like training a new hire
Inputs, steps, edge cases, and what “good” looks like.
3
Add your gotchas
The things that defy an outsider's assumption. This is the valuable half.
4
Write two real examples
And the exact output you wanted. Real ones, not invented ones.
5
Run it with the page and without
That comparison is your baseline. Now you can evaluate any vendor honestly.
That single page is the input to every tool in this talk — the subscription, the workflow, and the custom build.
Questions
Anh Nguyen · Founder, Applify Lab info@applifylab.com · www.applifylab.com · +971 553110959