70 AI Agents, 2 Years, 16 Lessons
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A plain-language playbook for anyone who wants to ship useful AI companions—without the hype
Why spend ten minutes here?
Over the past two years I have delivered more than seventy AI agents to paying clients.
Some agents now sit next to sales reps and replay their calls; others sit next to teachers and draft lesson plans; one even acts like a junior consultant and writes entire business proposals.
I kept notes every time something broke at 2 a.m. or a user sent an angry e-mail.
Those notes became sixteen lessons.
This post distills them so you can copy the parts that work and skip the parts that don’t.
No jargon, no promises of overnight riches—just a field report you can apply this week.
Table of contents
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What makes an “agent” different from ChatGPT? -
The sixteen lessons—one by one -
Frequently asked questions -
A seven-day launch checklist -
Final thought: turn the agent into your digital twin
1. What makes an “agent” different from ChatGPT?
Short version: ChatGPT is an encyclopedia you leaf through; an agent is a personal trainer who shows up every day, remembers your last workout, and pushes you toward the next goal.
2. The sixteen lessons
Each lesson has two parts: Why it matters and How to do it today.
1) Prompt quality sets the floor; knowledge-base quality sets the ceiling
Why it matters
A brilliant model fed a sloppy prompt will still underperform; a clever agent without structured knowledge will spin in circles.
How to do it today
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Write prompts in three parts: role, task, output format.
Example:You are a course-advisor bot. Task: turn the user’s rough idea into a three-module outline. Output: markdown with H2 headings for each module and bullet points under each. -
Store knowledge in markdown files with clear H1/H2/H3 hierarchy so the agent can index quickly.
2) The moat is long memory plus long companionship
Why it matters
Once users feel “it remembers me,” switching costs skyrocket.
How to do it today
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Compress previous chats into vectors and store them in a lightweight vector database. -
At the start of every new session, retrieve the top-k similar memories and prepend them in plain language:
“Last week you asked about X; here’s the follow-up…”
3) Users don’t fall in love with features—they fall in love with being understood
Why it matters
Tools are everywhere; empathy is scarce.
How to do it today
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Add an emotion branch: if sentiment < –0.2, reply “I hear the frustration—let’s take a breath and tackle this step by step.” -
Use the user’s first name and reference their last micro-goal: “Still aiming to finish the draft by Friday?”
4) Training an agent is training a professional persona
Why it matters
People treat consistent personas like reliable coworkers.
How to do it today
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Write a one-page persona sheet: -
Tone: calm, concise, slightly informal -
Forbidden words: “obviously,” “just” -
Fallback line: “I’m not sure—let me check and get back.”
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Run regression tests after every model update: feed 100 test cases and flag any drift.
5) An agent is not a ChatGPT skin—it’s a product redesign
Why it matters
Without input logic, output schema, and fallback paths, you’re shipping a toy.
How to do it today
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Draw a simple flowchart: User input → Intent classifier → Knowledge lookup → Formatter → UI output -
Add an “escape hatch” node: if confidence < 0.7, ask clarifying questions instead of guessing.
6) Skip the universal agent; build replicable vertical personas
Why it matters
Universal equals mediocre; vertical equals monetizable micro-SaaS.
How to do it today
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Package three narrow roles: -
Course-outline assistant -
Sales-call reviewer -
Proposal drafter
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Each role has its own prompt + knowledge bundle; spin up new instances by changing two variables.
7) A good agent doesn’t answer well—it guides users to the finish line
Why it matters
Users need a sherpa, not a librarian.
How to do it today
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Ask the user one clarifying question at a time. -
Display a progress bar: “Step 2 of 4—almost there.”
8) The real moat is data + scenario + feedback + iteration
Why it matters
Anyone can rent GPT; few have your real-world logs.
How to do it today
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Log every turn: timestamp, user ID, intent, satisfaction score. -
Hold a 30-minute weekly review: pick the bottom 10 % conversations, label the errors, retrain.
9) Feed structure, not just content
Why it matters
Facts go stale; frameworks age well.
How to do it today
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Instead of uploading 500 inspirational quotes, teach the agent the “hook-story-offer” writing frame. -
Let it fill fresh examples into the frame on demand.
10) Without a live update loop, the agent is a disposable gadget
Why it matters
Needs shift, knowledge rots, contexts evolve.
How to do it today
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Split the knowledge base into: -
Core (stable): product specs, brand voice—review monthly. -
Hot (volatile): pricing, promo codes—review daily.
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Add a one-click “report outdated info” button; promise 48-hour fixes.
11) The more human it feels, the stricter the guardrails must be
Why it matters
One rogue sentence can bruise a brand.
How to do it today
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Hard-block list: medical diagnosis, legal advice, political opinion. -
Standard refusal: “I’m sorry, I can’t help with that. Here’s a trusted resource instead.”
12) Best beachhead scenarios: high frequency, high necessity, high info density
Why it matters
Low-frequency tasks never build muscle memory.
How to do it today
Score each candidate scenario:
Pick rank-1 or rank-2 only.
13) Agents don’t replace people—they upgrade them
Why it matters
Fear messaging kills adoption.
How to do it today
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Reframe: “A rookie plus the agent equals a veteran.” -
Show screenshots: rookie closes 3× more tickets with the agent’s battle cards.
14) Conversation is the interface; workflow is the product
Why it matters
Users pay for outcomes, not chat logs.
How to do it today
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Model the workflow as a state machine: State 0: info missing State 1: info complete → call API State 2: result delivered -
If the user idles >5 min, trigger a polite nudge: “Shall I continue or save for later?”
15) The profitable agent is the one that feels effortless
Why it matters
Friction kills retention faster than missing features.
How to do it today
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Three-minute tutorial: one GIF + three bullet steps. -
Fifteen-minute templates: five empty fields → one polished PDF.
16) Endgame: a digital twin of you
Why it matters
When the agent carries your worldview, it can deliver value while you sleep.
How to do it today
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Vectorize your best 100 articles, 10 frameworks, 3 tone patterns. -
Schedule nightly jobs: reply to inbound questions, send next-day prep e-mails. -
Wake up to a dashboard: “While you were away, the twin booked two demos and answered 47 questions.”
3. Frequently asked questions
Q1: I’m not technical—can I still build an agent?
A: Yes. Write the persona sheet, knowledge base, and flowchart in plain text. Hand them to a developer or use a no-code platform.
Q2: How big should the knowledge base be to start?
A: Focus on one vertical. Thirty to fifty well-structured documents are usually enough for a solid MVP.
Q3: Which metrics prove the agent is working?
A: Track three numbers:
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Next-day retention ≥ 40 % -
Average conversation rating ≥ 4.2/5 -
User-driven referral rate ≥ 10 %
Q4: How do I stop the agent from saying something wrong?
A:
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Add citation links in the knowledge base. -
Run similarity checks; if the answer deviates too much, fall back to “I don’t know.” -
Manually review the top 100 daily conversations.
4. A seven-day launch checklist
Print it, tape it to your wall, cross items off in Sharpie.
5. Final thought: turn the agent into your digital twin
AI is not a typewriter on steroids.
It is an amplifier of your skills, an extension of your personality, a second self that never sleeps.
The real agent is not sitting inside OpenAI, Anthropic, or any other lab.
It lives in the hands of people who are willing to package their unique knowledge, voice, and methods into something that can run 24/7.
Once you do that, the question is no longer “How will my agent make money?”
It becomes “How much of my expertise deserves to be duplicated and scaled?”
Answer that, and you’re already ahead of the curve.
