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Hot take: most "AI agents" being sold right now are just a prompt with a for-loop.
That's not a bad thing. But calling it an agent sets the wrong expectations.
What actually makes AI useful in a business:
• It does one boring job really well (not 20 jobs badly)
• It knows when it's unsure and asks a human
• It checks its own output against something real, like totals, a database or a rule
• Someone can see what it did and why
The flashy demo gets the client. The boring guardrails keep them.
Unpopular opinion? Or do you see the same thing with your clients?
#AI #automation #freelancertips
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This year’s YC Fall batch is for us creatives. 🎯
As a product designer, I’ve never felt like our field was changing as rapidly as it is right now. Tools emerge, you adapt, they disappear. Processes that took hours can now be largely automated, and overall, I feel like a big part of the fun gets lost because of a communication issue: I struggle to tell the tools exactly what I want in enough detail.
I recently stumbled across a few interesting and promising startups that might bring some of that joy back and take the pain out of parts of our work.
Proto is building an IDE specifically for product designers, bringing design and AI workflows closer together. 👩🏻💻
Simulithic uses AI agents to test your app across devices, browsers, and account types, automating test creation and maintenance. 🤳🏼
Composal uses swarms of AI agents to explore product flows and edge cases, finding what breaks before release. 🕵🏻♂️
Native helps marketers decide what to create by ranking ideas based on brand context, performance, trends, and creative feedback. 🦖
The edge case testing sounds promising.
An AI customer support chatbot for a fictional Nigerian food delivery business, Lagos Fresh Kitchen. It runs on n8n Cloud and uses Claude Haiku 5.5 as the AI model. The workflow is three nodes: a chat trigger receives the customer’s message, an AI Agent processes it, and the Anthropic Chat Model writes the answer.
How it behaves
The AI Agent has a system message that sets the rules. It must answer only from the business information, never invent prices, areas, hours or policies, and use a fixed fallback line when the answer isn’t there: “I don’t have enough information to answer that accurately. Please contact our support team.”
The business information it knows
Open Monday to Saturday, 9 AM to 8 PM, closed Sundays
Delivers to Lagos Mainland (Ikeja, Yaba, Surulere, Maryland, Ojota, Gbagada) and Island areas (Victoria Island, Ikoyi, Lekki), not outside Lagos
Delivery takes 30 to 60 minutes, with fees from ₦1,000
Menu of jollof rice, fried rice, rice and stew, pounded yam with egusi, grilled chicken, suya and soft drinks
Payments by card, transfer and cash on delivery
Cancellation allowed before cooking starts
No Sunday orders or Sunday delivery, and no event catering
Refunds, complaints and order status go to support
good work
From Manual Follow-Up Chasing to an Agent-Run Follow-Up Queue
Before / After: follow-ups, from manual chasing to an agent-run queue
Before: every follow-up lived in someone's memory. The founder checked the inbox, guessed who was waiting, and sent reminders by hand. Leads went quiet not because they said no, but because nobody followed up in time.
After: an agent now runs the follow-up queue. Each lead has a next step and a due date, drafts wait for review, and every reply is logged with its source. The founder reviews the queue once, approves, and moves on.
What changed was not the tool. It was the handoff: one clear owner for each step, a runbook anyone can follow, and a log anyone can audit.
If follow-ups are the bottleneck, fix the handoff first — then automate it.
"Fix the handoff first" is the part most teams skip. I see the same thing in email automation: a flow nobody owns keeps firing to the wrong segment for months because no one is checking the log. When the founder is away, who approves the drafts waiting in the queue?