Ryan Lewis | AI Automation

I break down practical AI automations for online businesses.
Founder @BackendBrilliance (AI that books jobs)
New: The Practical AI Automation Blueprint ↓

[Ryan Lewis | AI Automation @Mr_RyanLewis]

Most AI automations don't fail because of the AI.

They fail because nobody designed the control.

Here's the 6-stage operating model I run every workflow through before it goes live: 🧵

[Ryan Lewis | AI Automation @Mr_RyanLewis]

The pattern is everywhere, not just Wayland. People blame the model or the tool; the failure is almost always in the workflow scaffolding around it.

The MCP server is the interesting part. A screen recorder is fine, but giving agents a real interface into the workflow beats making them scrape pixels any day.

[Ryan Lewis | AI Automation @Mr_RyanLewis]

@JamesMalsawm Good picks. Of the two, customer support is the stickier business. Lead gen lives or dies on lead quality, which is mostly out of the builder's control. Support has a clean metric: tickets resolved without a human touching them. Which one are you building for right now?

[Ryan Lewis | AI Automation @Mr_RyanLewis]

Honest question for anyone running AI in their business: what's the one task you'd pay $500 a month to never think about again? Not "marketing" — name the actual thing. I'll tell you whether it's automatable.

[Ryan Lewis | AI Automation @Mr_RyanLewis]

@WukongNumber1 Exactly. The naming-the-job part is the whole trick. A generic "sorry we missed you" reads as spam and gets ignored. A text that names the actual service gets answered because it proves someone's actually paying attention.

[Ryan Lewis | AI Automation @Mr_RyanLewis]

Missed calls are the most expensive leak in a local business.

Here's the fix I built: someone calls, no one picks up, they get a text within a minute. Not 'sorry we missed you' — it references the actual job: 'Were you calling about the tree removal in Weirton? I can get you an estimate this week.'

Generic follow-up gets ignored. Specific follow-up gets booked.

Phone webhook → LLM → SMS. Four steps, a few dollars a month to run.

[Ryan Lewis | AI Automation @Mr_RyanLewis]

Genuine question for business owners: how many calls do you think you missed this week that you'll never know about?

Not the voicemails. The ones who never left one.

[Ryan Lewis | AI Automation @Mr_RyanLewis]

The arbitrage doesn't disappear, it moves. When everyone has the same tools, the edge shifts to execution speed and distribution. Same models, same APIs — but one team ships and iterates daily while another ships quarterly.

I've seen the same automation produce wildly different numbers based purely on who runs it. Access is equal now; the bottleneck is implementation.

[Ryan Lewis | AI Automation @Mr_RyanLewis]

The second bullet is the one that matters for real deployments. An agent that loses its identity across reconnects re-announces itself and re-asks for context every time — that's the difference between a bot you can hand to a client and one you have to babysit.

Identity continuity plus a compacted memory log is what turns a demo into something that survives production. The speed tiers are nice; surviving a cold start without amnesia is the actual feature.

[Ryan Lewis | AI Automation @Mr_RyanLewis]

The sorting rule I use: SOUL.md is who the agent is, USER.md is who it's talking to, MEMORY.md is what has happened. Three different jobs.

The failure mode I see in practice isn't misfiling, it's that MEMORY.md has no compaction rule. It grows into a junk drawer of stale context the agent reads but can't trust. A weekly summarize-and-prune pass matters more than which file things land in.

[Ryan Lewis | AI Automation @Mr_RyanLewis]

@tonysimons_ The automation people ask for isn't really automation — it's reliability. Blueprints get you to demo. The real work is run #200 working as well as run #1, and no blueprint covers that: retries, idempotency, escalation when the lookup returns garbage at 2am.

[Ryan Lewis | AI Automation @Mr_RyanLewis]

This is a solid loop — you basically built gap detection into your workflow. One upgrade worth trying: make those rules ranked and explicit. When two course lessons conflict, the agent needs to know which one wins. I keep mine as numbered ranked rules in a single system prompt; without that, an agent with conflicting instructions defaults to the most restrictive one and starts refusing.

[Ryan Lewis | AI Automation @Mr_RyanLewis]

Fun comparison, but for anyone running real workloads: day-zero vs day-10 matters less than whether your workflow survives the update. I keep a fixed set of my actual client flows and rerun them against every new model — same prompts, same scoring, twenty minutes. That's what tells you if the update is safe to ship.

[Ryan Lewis | AI Automation @Mr_RyanLewis]

@tonysimons_ Control is the whole game. But the flip side: the more freedom the agent has, the more explicit the guardrails need to be. In my client booking flows the most capable agents run under the tightest ranked rules — complete control without them quietly turns into complete liability.

[Ryan Lewis | AI Automation @Mr_RyanLewis]

@CaptainJeromeFr Good. One thing to watch for: provider model updates quietly change how strictly mid-prompt ranked rules get honored. Re-run your hardest test case after every update — that's when this setup breaks silently.

[Ryan Lewis | AI Automation @Mr_RyanLewis]

Exactly. And the control is where most of the real work hides — in my booking flows the model is maybe 20% of the build. The rest is routing: which replies the cheap model can send, which ones escalate to a human, what happens when the CRM lookup returns garbage. Nobody screenshots that part, but it's what keeps the thing running in production.

[Ryan Lewis | AI Automation @Mr_RyanLewis]

Most AI automations don't fail because of the AI.

They fail because nobody designed the control.

Here's the 6-stage operating model I run every workflow through before it goes live: 🧵

[Ryan Lewis | AI Automation @Mr_RyanLewis]

Most AI workflows burn money on the wrong model. The cheap one does the grunt work — extracting, summarizing, drafting. The expensive model only steps in for the final decision. One routing change took my API cost per run from ~$0.40 to ~$0.06, same output quality.

[Ryan Lewis | AI Automation @Mr_RyanLewis]

@JamesMalsawm James flagged the weekly + 5-hour limit below, and that's the real question. If Opus 5.5 on high reasoning burns through the weekly cap in a day or two, the $20 Pro plan is a trial tier for serious work. Model names are marketing; rate limits are the product.

[Ryan Lewis | AI Automation @Mr_RyanLewis]

@BudFox212 Same lesson on the business automation side. When a workflow breaks in production it's almost never the model, it's the inputs: duplicate CRM records, stale price lists, ambiguous names. The unglamorous data-cleaning work is the actual product. The automation is just the demo.

[Ryan Lewis | AI Automation @Mr_RyanLewis]

The version I'd actually want doesn't summarize the output, it diffs what the agent did: which files changed, which commands ran, what side effects happened. The output text is where the agent already put its best spin on things. Question is whether this watches the tool calls or just re-reads the output.