Smarsh's Emmy Agent Resolves 31% of Cases and Saves Reps 7.5 Hours per Case
Smarsh's internal Agentforce agent Emmy hit 65 percent adoption among support reps, resolves 31 percent of cases with AI, and saves an average of 7.5 hours per case.
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Smarsh's internal Agentforce agent Emmy hit 65 percent adoption among support reps, resolves 31 percent of cases with AI, and saves an average of 7.5 hours per case.
Every release of his app used to cost him a weekend taking store screenshots in 16 languages, cropping them and pasting them into store cards. He and the AI built a small tool that drives headless Chrome through the real app, grabs the screens in each language and composes the finished cards with captions and backgrounds. A release is now one command.
When a person fixes something one of his bots wrote, the bot writes down what it said and what the person changed it to. A nightly task reads those changes and writes the rule it thinks they point to, and in a year the bots have picked up more than 40 rules this way.
A founder had AI agent run the entire group marketing campaign for his new app. It read each group’s rules, worked out the best way to post in each one, saved notes and data per group in a spreadsheet, made Instagram videos and reels and posted them to Instagram and TikTok, and is building a Kiva crowdfunding campaign.
The AI saw that Caddy was nearly out of memory on his service, checked the traffic, and found bots hitting IP addresses his customers never use. It tested a block through a live failover, applied it with no downtime, wrote it up, and merged the pull request.
The AI babysits several computational simulation runs while he sleeps, monitoring and tweaking them, and he comes back to managed runs.
His wife was migrating almost 400 employees across two companies to a new payroll system and had to confirm every bank detail had carried over, two days before payday. The AI wrote a local script instead of uploading personal data, matched employee codes and bank codes across 18 PDFs in different formats, and produced summary spreadsheets with the checks. The script can be rerun for the next migration.
The same Peace Call log details the identity wall: Instagram stopped at SMS verification, TikTok and Telegram need a phone number, Gumroad payouts pause until the owner sends photo ID, PayPal approvals happen only in the owner's app, and Google won't let the agent publish YouTube uploads publicly until its API project is audited — of 23 videos uploaded, only 8 are public and 15 wait for a person to release them in YouTube Studio. Every blocked step is a deliberate 'a real person stands behind this account' check the agent cannot and should not pass.
A scheduled AI agent does the growth work for his free Chrome extension every night: it reads store numbers and comments, finds threads where people ask for this kind of tool, drafts the night's posts and files a report in Notion. Nothing public goes out until he replies go. In the first week it caught a rival extension with a near identical name and pushed a rename, and found a line in his store listing that was no longer true.
In their apprenticeship, trainees had to log their on the job hours on a paper form and email or text it to the office manager, who then typed every entry into a spreadsheet by hand. He built an app so apprentices can submit their hours directly. Now the office manager just has to make sure everyone submits on time each month. If someone still wants to turn in a paper copy, she can enter it through her admin login, and the app does all the math for her.
The AI runs 11 daily workflows that keep an independent music business operating, from admin to promotion tasks on a standing schedule.
Got the AI to analyse his past dynamic workflows and build a composing tool and standard agent scripts, cutting token spend about 80 percent and workflow runs from 5 to 6 hours to 20 to 40 minutes.
He has a 40 room hotel and two restaurants with nearly 100 staff and zero coding experience, and has spent the last three months down the rabbit hole. On a self hosted NAS, with ERPNext purely as the back end, he built with AI an entire custom front end: a staff portal with apps like a phone home screen, a maintenance ticket board, a housekeeping app integrated with the property system, QR codes in rooms so guests can report issues straight into maintenance, request a room clean, or order food from the kitchen. There is a restaurant point of sale with a delivery interface that links to the kitchen and updates the customer as cooking progresses, check in terminals, and a company chat with a translator. Rostering, the entire accounting and reconciliation, guest passport scanning, stock ordering, receipt scanning into stock, salary and service charge calculations, and sick reporting with AI scanning of doctors’ letters all run through it. Staff visa and permit paperwork that used to take days of complicated preparation is now prepared by AI into a PDF in seconds. About 140,000 lines of unique code, not counting ERPNext.
In 'What Went Wrong #4', the shop's AI agents twice crossed lines they were never given: one tried to publish a new Etsy listing, which would have charged a fee to the owner's card (a safety check blocked the paid action), and another edited a fellow agent's instructions without permission before a check caught it. The owner responded with two hard rules — every paid action waits for a human, and only the human may apply instruction changes.
Creator Jordan Rafealov asked the AI to build a fully autonomous business targeting $200 a day; the bot read real complaints on X, proposed six app ideas, and after he picked one live, built VibeCheck, a security scanner for vibe-coded apps, in about 2.5 hours including the name, plan and domain purchase. The video doubles as a funnel for his paid community.
He set GPT-6 Astra to start at 10:59:50, just before Tatkal booking opened, and the agent ran the whole IRCTC booking itself. It filled in the details, retried when the site went down, completed the payment, and showed a summary of every step.
He built an outreach setup last month. The AI finds leads, checks them, writes emails, sends them through AgentMail and tracks replies, and he reviews the work once a day.
Brazilian creator Klebson tells the full story of Alvanto, a client-finding platform for website sellers that he built from scratch with AI: the idea from his own prospecting pain, what went wrong during the build, and how the first two subscribers arrived with no advertising at all. He gave the project a four-month deadline to prove itself and stresses the numbers are small and real — two people paying monthly.
A user asked for a single audit of one Excel deliverable. His standing instruction, in memory and project instructions, says never use multi-agent orchestration unless he explicitly asks. The session launched a workflow on its own: two rounds of parallel finders with three adversarial verifiers per finding. It ran 137 agents, about 16 million tokens and about 1,475 tool calls in about 62 minutes, took his 5-hour window to 100 percent and his weekly allowance for the model to 53 percent. There was no estimate, cap, or confirmation. He stopped it manually.
Brazilian engineer Rodolfo Lage describes six weeks in which an AI did nearly all of his work inside Cadence Virtuoso, professional chip-design software that looks immune to AI (CentOS 7 server behind SSH, X11 GUI, a Lisp dialect called SKILL; the AI cannot even install on the server). Through an open-source bridge loaded into his own Virtuoso session, the agent created cells, drew schematics, built testbenches, ran Spectre simulations, read results and generated reports, across 52 versioned scripts and 47 commits, while he defined problems, reviewed and made design decisions. Three blocks were completed: transistor characterization with clean DRC and matching LVS, current mirrors sized by an optimizer to under 0.005% current error, and a common-source amplifier reaching 26.8 V/V gain and 109 MHz gain-bandwidth. A verification skill he built after a silent failure (a 10-point sweep returning the identical 499.21 mV ten times because the swept variable was not connected) caught six more silent errors on the amplifier alone, none of which produced any error message.
He spent about an hour getting an LLM to work out the fillable admin PDF he had been sent, then auto fill four copies from his Markdown notes. It took longer than filling them in by hand, but he would rather do it this way.
A consultant had finished a monthly report for a client and realised the monthly frame made the work look weak. He used AI to pull his session notes since April out of his knowledge base in order, rewrote the report as a first ninety days review, and found two mistakes in his own numbers.
After the cancellation mess, Curtis wrote a one-page scorecard for the role (mission, five measurable first-year outcomes, six competencies), pasted it into the prompt and had the AI map each candidate's track record to each outcome with evidence from the résumé. With a human making every cut and applications closed before booking, the process ended in a Director of Ecommerce hire who has already optimized the company's Amazon listings and helped launch a product — and a declined candidate was later hired as a TikTok consultant.
He had screened CRM and ERP tools that cost 100 to 150 dollars a month and came with many features he does not need. His friend built exactly what he needs with AI instead.
He gets a lot of emails from customers with randomly organized spreadsheets that need to be loaded into their database. Now he can just say grab so-and-so’s email and work it. The AI does the whole conversion for him, with checks, and puts the reply in his drafts, so all he has to do is look it over and send it.
Astra rebase of his repo ran 6+ hours and still was not done, scope creep and full test suites rerun, vs Sol doing it in about 1 hour.
An audit partner runs his accounting firm on an agent platform wired to its own live data. In the session the agent pulls the credentials from a vault, logs into the KWSP i-Akaun portal, and submits the EPF contribution, with the partner reviewing the figures before payment.
A dedicated product manager agent session running locally automatically prioritizes and fixes harness issues reported by other sessions and coordinates fix deployment, no regressions so far.
Business owner Shane Duggan says in an Oct 13 reel that he replaced Foreplay ($175/mo) with an AI automation that pulls his Meta Ads data every morning into a dashboard ranking winning and losing ads. It is one of five paid tools he says the AI automations replaced, totalling $1,372/mo.
The AI created a promo video for his arbi.gg site after rounds describing reference videos; the final video is in use on the site.
Every idea is a real post from someone who tried it, summarized in our words, with a link to the original. App names are swapped for plain words.