8 of 27 are a receptionist.

a dental one that books appointments. a restaurant one that takes reservations. a vet clinic one that sorts urgent from routine. a website chat that captures the lead. different builds, identical shape: message comes in, figure out what they want, look it up, book it, log it.

the rest cluster just as tight. 5 turn content into other content. 4 are some form of finding businesses and pulling their public contact info.

almost none of the top posts are the multi-agent stuff people argue about.

what i can't tell from the outside: how many are still running six months later. anyone kept one alive that long?

most automation alerts are too late and too vague.

failed node, missing field, timeout, bad response. that tells you something broke, but not why this run was different from the last one that worked.

the alerts i find useful compare against the last known good run.

same trigger? same input shape? same account permissions? same file count? same downstream record? same external status after the run?

that comparison usually gets you closer to the real cause than the stack trace does.

example: attachment step failed is less useful than last good run had 3 files, this run had 0 files after the parser, but the email step still executed.

for anything recurring, i think every alert should include:

• expected state
• actual state
• last good state
• first step where they diverged
• whether the user-facing result changed

what do you include in alerts so they are actually actionable?

I ran across several YouTube videos about Hermes agent, and so far it seems interesting. The thing is, I use Codex for daily tasks (coding, design, marketing, simple automations, etc.)

Is there anyone who really uses Hermes daily? If so, how? What kind of tasks does it do for you?

I compared them with Codex, and it seems like the latter isn't far away in terms of functionality. So does it even make sense to switch to Hermes?

Manually pulling data, cleaning it, dropping it into a report every week for months. Kept meaning to script it "someday" but the manual version always felt faster than stopping to build it. Finally sat down, wrote a simple script — 45 minutes total. Got my Friday afternoon back for the first time in ages. Anyone else put off a small automation way longer than it actually took to build?

building an automated enrichment layer for inbound leads from Meta and Linkedin forms. Goal is to append company context fast enough to influence the first automated touchpoint, ideally within the same session. The problem is input quality varies a lot. Linkedin leads are cleaner because profile data populates automatically. meta leads are messier, inconsistent company name formatting, sometimes personal emails, abbreviations. Need an api that handles fuzzy matching and ideally returns a confidence signal so I can route low confidence matches differently downstream. Also specifically interested in real time latency. Some APIs are clearly optimized for batch and the p95 response time breaks down under real-time conditions. what are people running in production for this kind of setup??

Dripify costs money n my n8n workflow automation chain for automated LinkedIn messages was free. LinkedIn updated a frontend selector last Tuesday n my headless browser flow started throwing errors. I spent three hours fixing it before my first call.

The DIY automation community makes linkedin message automation look easy. Scrape leads. Enrich them. Fire off connection requests. Then u deploy it n LinkedIn changes their DOM structure. Or ur proxy IP gets flagged. Has anyone here maintained a DIY automation stack for more than six months?

I've been trying to understand how AI outbound calling actually works because I'm seeing it mentioned more and more in automation project. From what I understand, there are a few pieces involved- the system making the calls, the AI voice handling the conversation, and the CRM tracking what happens afterward.

The part I'm still confused about is how these pieces usually connect. Is the dialer normally built into the voice platform, or do people usually connect separate tool together? Also curious how these setups handle scaling before things start getting messy.

If anyone ha worked with a real setup, I'd love to hear how it was structured.

I am trying to build a meeting pipeline that does most of the boring work for me. The dream is record, upload, get transcript, push action items to Todoist, publish a summary to Notion, email the attendee list. I am about halfway there.

Currently I use an n8n webhook to listen for new files in a folder, send them to a transcription service, and then format the result. The fragile part is the transcription step. Some APIs take forever, some have strict file limits, and some return summaries that are too generic to be useful.

I have been testing vomo ai as the transcription step for now. I upload through the web app, download the transcript and action items, and then format them into the next steps. There is no native API yet, so the upload and export steps are still manual glue.

What I can get running in one weekend is something like this. If you have glued transcription into an agent workflow I would love to hear whether you went local or cloud and how painful the handoff was.

the failures that scare me are not the ones where the workflow crashes. those are obvious.

it's the partial success ones: it wrote the draft, skipped the approval note, used an old source, scheduled the wrong version, or marked the job done because one step returned ok.

i've started treating every automated content or ops flow as needing three outputs:

• what changed
• what was skipped
• what needs a human decision

that sounds boring, but it catches a lot of fake progress. if an agent cannot explain the skipped part, i don't really trust the completed part either.

curious how others handle this. do you log partial success as a failure, a warning, or just a normal run with notes?

I run an agency out of India which was limited till SaaS, Mobile apps, e-commerce, demand generation, etc. I’ve got clients mostly around USA, Australia and little bit in Europe as well. I’m not much of technical guy but I know how to market and sell very well.

I’m planning to expand into AI services, and looking for a trustable partner in India (No agencies please). We can initially work on some internal POCs (paid) and some project based contracts.

I’m open to partnering or co-running the AI vertical together in future if things workout well. I’m also open to discuss product/service ideas if you got.

I've been experimenting with AI automation over the last few months, and one thing surprised me.

Building the automation is often the easiest part. Understanding the client's workflow, handling edge cases, fixing unexpected issues, and maintaining everything takes much more time than I expected.

I'm curious how others here are handling this.

• Do you charge a one-time setup fee, a monthly retainer, or both?
• Do you ask clients to pay for APIs and software directly, or do you include those costs in your pricing?
• What's one pricing mistake you made early on that you'd never repeat?

I'd love to learn from people who are already working with AI automation clients.

I got interested in workflow automation in my last job, and I’ve now set up n8n on a virtual private server and am using it to host workflows for the full-stack project I’m building, as well as my personal website.

The project is an AI and automation enhanced business simulation, with the idea being to experiment with where I can use AI and automation to, well, enhance the system. The project is still in its infancy, and so far all I’ve done is automate my notion dev log to my website dev log, and automate the contact form on my site.

Automations I have in mind:

·       notifications for customers and merchant (it’s both sides of an ecom store)

·       things around orders being placed such as a calendar note, inventory updated

·       coordinating marketing development for ‘new’ products added to the system

·       sales analysis > email summary

·       eventual CRM integration

These are just ideas, and I know that automation should come as problem solving/time reducing than backbone project structure, but I haven’t got that far yet so I’m still just letting my mind run with it.

Anyway, I thought I’d post this as an introduction as I’ll be posting more automations as I build them.

If anyone has built anything similar, or has any suggestions, I’d be interested to hear.

i'm looking for a browser mainly for automation tasks like account management, testing, and running workflows. i've seen both 1browser and gologin mentioned, but i'm curious how they compare in terms of stability, profile isolation, and automation support.

if you've used either one, which would you recommend and why? are there any other browsers that work better for automation?

Disclosure up front: I work at Serval now, which is one of the tools below, so weight this accordingly. The reason I ended up there is basically the reason for this post. About 18 months ago I was running internal IT for a ~600-person org, inherited Freshservice, and "70% of our tickets are repeats" had become the whole problem, so I ran a real eval. I've tried to write this so it's still useful even if you land somewhere else, and I've left out a couple of tools I can't speak to honestly.

The thing I'd tell past-me: the real fork isn't "which vendor," it's whether you want chat to be an intake channel or the actual place work gets done. Most tools that say "Slack support" just open a ticket from Slack and then move you back into a web console. A smaller group tries to resolve the request inside chat before a ticket ever exists. Those are different products with different day-200 realities, and the demos make them look identical.

How I assessed them: what share of requests actually resolve inside Slack/Teams, how much automation happens before a ticket is created, admin/maintenance overhead, and whether it holds up as volume grows. Here's where each landed for us.

Freshservice, best for structured ITSM with asset and change control. If you want a clean service catalog, asset lifecycle, and change management with defined process, it's solid, and it's what we ran at the time. Slack/Teams are submission channels; most real work stays in the web console. Where it strained for us was exactly the repeat-ticket problem, it organizes the queue well but doesn't do much to stop the queue from filling.

Jira Service Management, best for engineering-aligned orgs already in Atlassian. Strong SLA and approval workflows, deep ecosystem fit, real governance. Chat is intake, configuration lives in Jira. Heavier to set up and maintain than the AI-native options, but if your IT and dev worlds are tightly coupled it's a defensible pick.

Atomicwork, best if you want AI-native but from an ex-Freshworks lineage. Genuinely in the modern-ITSM lane and shows up on most of these shortlists for a reason. Worth piloting alongside the others; where it lands depends on how much of your volume is knowledge-answering versus action-taking.

Moveworks, worth a note mainly for context. It was on my shortlist back then as the AI-assistant/deflection layer, but ServiceNow acquired it (closed end of 2025), so it's now part of ServiceNow rather than a standalone tool you'd buy on its own. If you're already deep in ServiceNow it's becoming the native front door. I'm leaving it here because it shaped how I thought about deflection at the time, not as a current standalone pick.

Serval, best when the pain is action-heavy repeat requests, not just questions. It resolves a request end to end inside Slack, Teams, email, or the portal instead of routing it faster, so an access or onboarding request actually runs (identity and group changes, provisioning, approvals, logged per step) rather than becoming a ticket worked later. The automations are deterministic, so nothing improvises against production. Honest tradeoff: if most of your volume is KB questions rather than cross-system actions, a good deflection tool may serve you just as well.

Where I'd start if I were doing it again: take your actual last 90 days of tickets, cluster them, and look at whether the top clusters are questions or actions. If they're mostly actions (access, onboarding, group membership, resets), weight the automation-first tools. If they're mostly questions, a strong deflection/KB tool gets you most of the way. And whatever you pilot, test it on your real ticket data for two weeks, not the demo. The demos all look the same. The two-week pilot on your own volume is the only thing that told me the truth.

sharing an automation that's been quietly running my week.

What it does

• A WhatsApp message creates a card on a shared board and assigns it to an agent.
• The agent runs the work and saves a real deliverable.
• The moment it's done the task re-assigns to a person to review
• Per-agent model choice; the trigger surface (WhatsApp) and the app are the same agent on the same tables.

So: phone → board → agent does the work → human approves. No terminal, no dashboard-babysitting, whole team sees the pipeline live.

👷 How I built it Stack: Lemma (open source) + Claude — Time: a couple hours

1. Builder skill in Claude, described the flow.
2. One command scaffolded the system across six folders.
3. RBAC + approval workflow included; the WhatsApp surface is native, not a glued-on integration.

For people running real business automations, which problems cause the most ongoing work?

I’m particularly interested in workflows involving AI agents or multiple external tools.

Do you struggle more with:

• Broken integrations
• Expired credentials
• Unexpected data formats
• Duplicate actions
• Approval steps
• Failed retries
• Poor visibility into what happened
• Fixing workflows that only partially completed

Are tools like Zapier, Make, and n8n sufficient once workflows become complex, or do you eventually need to build a custom reliability layer?

I’m researching this problem and would appreciate honest examples of what breaks in practice.

I dread this task and would appreciate any specific direction about how to do this.
If possible explain to me like a 5 year old please. I use Claude and CoWork but currently only at what I would describe as a D+ level.
Thanks very much.

I have been thinking about this while tightening a few AI and workflow automations.

The part that makes me trust a workflow is not just that it runs once. It is whether it handles the boring edge cases clearly: missing inputs, duplicate records, stale context, failed checks, and actions that should stop for human review.

The pattern I keep coming back to is simple: define the input shape, define what safe to proceed means, return a useful stop reason when it should not run, log what changed and what did not, and make retries safe enough that they do not duplicate work.

That feels less exciting than adding another model or integration, but it is usually what makes the automation usable in real work.

For people building automations, what makes you comfortable letting one run unattended? Good logs, human approval steps, rollback paths, test runs, alerts, or something else?

Hey,

I kept seeing the same thing with n8n. People build a workflow, test the happy path once, flip it on, and then something weird happens in production. Duplicate webhook. Missing field. Stripe retry. Suddenly you emailed half your list twice or charged someone wrong.

So I built Crash Labs.

You paste your n8n workflow JSON. It runs it against a fake company (mock email, Stripe, Slack, etc). Then it shows the blast radius. What would have been sent, charged, created. Pass / warn / fail.

It is early. Not every node is covered yet. Coverage and blind spots are in the report so it does not pretend it proved more than it did.

If you build client automations or anything that touches money / customers, try dumping a workflow in and tell me what it gets wrong.

the first one is already free. If you tell me your email you signed up with i can grand you more so you can test the product full!

Happy to take brutal feedback.