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AI Agents are becoming easier to build. Making them reliable is the real challenge. š¤
I've been spending more time learning about AI agents and workflow automation with n8n, LLMs, APIs and Python, and one thing is becoming increasingly clear:
Building an agent that can do something is only the first step.
A production-ready agent also needs:
ā The right context and data
ā Controlled tool access
ā Human approval when necessary
ā Error handling and monitoring
ā Reliable workflows around the LLM
ā Clear boundaries for what the agent can and cannot do
I think the interesting part of AI automation isn't simply:
"Can AI do this?"
It's:
"How do we make AI do this reliably inside a real business process?"
That's the area I'm currently exploring through my own projects and experiments with AI agents + n8n + RAG + APIs.
Curious to hear from people already building AI agents in production:
What has been the hardest part for you ā reliability, integrations, data, cost, or something else?
#AIAgents #AIAutomation #n8n #GenerativeAI #Automation
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Behind the scenes on how our build process actually changed this month.
For most of this year we ran AI under a budget constraint, and that constraint quietly shaped everything. Which problems we pointed AI at. Which we did by hand. How much of a codebase we were willing to let a model read. Scarcity was the organising principle and we had stopped noticing it was there.
Then it lifted. Here is what actually broke.
The problem nobody warned us about. Freed from budget pressure, sessions got long and deep. A model will happily work a huge problem in one sitting. But the sitting ends ā timeout, full context, or you go to bed. And everything it worked out along the way vanishes. The dead ends it ruled out. The constraint it discovered in hour two. The reason it chose one approach over another.
When sessions were short, we held that context in our heads. When they got long, we couldn't. We started losing more to forgotten context than we had ever lost to rate limits.
What we built to fix it:
Exit interviews. Before closing any meaningful session, we prompt the model for a handover briefing ā what it did, what it decided, what the next instance needs. Not a commit message. An actual briefing. That output becomes the opening context next time. This is the single highest-leverage thing we changed and it took an afternoon to adopt.
Browser session stability. Long builds kept dying when whoever was driving switched tabs. Moving active sessions into the Chrome extension fixed it. Unglamorous. Recovered more hours per week than any clever prompting.
Logic only, no UI. We kept fighting the same battle ā strong architecture and logic coming back attached to cluttered, incoherent interfaces. So we stopped asking. We now prompt for logic and architecture and explicitly block frontend generation. Structural foundations go to a human who builds the interface natively. Restricting the model made the output better, which is not what I expected.
Two-stage handoff. Fast rough first pass to kill ambiguity, often straight from a client scope document ā one was 125 pages fed through in a single stretch. Then a different person takes the careful second pass.
The part I did not expect. More capacity meant more verification, not less. We increased the QA burden at the same time we removed the ceiling, deliberately, because an agentic system will always find something to report and the report will always sound like progress.
The tools were the easy part. The reorganisation was the work.
infiniteup.dev
The Bottleneck Moved: How We Rebuilt Our Workflow Around MCP and Fable - InfiniteUp
We removed our AI capacity ceiling expecting to get proportionally faster. Instead the bottleneck moved somewhere less convenient. How InfiniteUp rebuilt its development workflow around MCP and Fable.
budget shaping the whole process, good honest writeup
GrowMint AI - AI Marketing Automation Website
An AI marketing automation experience designed around the needs of marketing teams, growth marketers, and digital businesses.
The interface brings campaign management, customer journeys, analytics, and AI-driven optimization into a focused SaaS product experience, making complex automation easier to understand and navigate.
Nice design
Rope Street Tattoo, BraČov (fictional)
Problem solved: a solo tattoo artist loses tourist leads to midnight DM replies and a day's pay to every no-show. Now any message becomes a priced, deposit-paid booking in under a minute, and confirmations, reschedules and waitlist refills run on their own.
The client: Mara, one chair, around the corner from Strada Sforii. Gloves on 7 hours a day, so DMs wait until midnight. Every quote needs size, placement and colour: about 7 messages per booking. Tourists leave before she replies, and 1 client in 7 doesn't show up.
What I built in Lovable:
ā Front door: describe the idea in English, German or Romanian. AI reads it, but Mara's own rules set duration, price and deposit. Only times that fit the job and the customer's days in town. Stripe deposit, booked. Cover-ups and hands go to Mara instead.
ā Autopilot: confirm or move at 72 h, a second nudge at 48 h, unconfirmed slots released 24 h ahead and offered to the waitlist, prep reminders, online consent forms, aftercare and touch-ups.
ā Owner area: Today, Week, Needs you with drafted replies, paste-a-DM replies, and the numbers: in four weeks of demo data, 304 messages not written and 38 h 53 min saved.
ā A demo clock to fast-forward and watch it work.
Try it: https://rope-street-tattoo.lovable.app
Owner view: footer ā Studio ā Try the owner view ā Demo clock
Case study: https://rope-street-tattoo.lovable.app/story
A tourist who books two days ahead lands inside the 72-hour confirmation step. Does that booking skip the confirm-or-move message, or does it get the 48-hour nudge straight away?