Running a 35person startup and every AI tool I’ve looked at wants me to go somewhere new to use it. Another tab, another login, another thing to remember.

We already live in Slack and email. I want something that just works there without my team having to change how they operate.

Has anyone actually figured out how to create an ai agent that fits into existing tools instead of replacing them? Best and worst experiences welcome.

spent way too long getting an agent to log into a dashboard and pull reports on a schedule. locally it was fine. flipped it to the server and the session just evaporated. cookies gone, back at the login wall, clicking a page it couldnt read

i kept blaming my own code. rewrote the auth handling twice, stored the cookies myself. it would work a day then quietly log out and id only notice when the data came back empty

what fixed it wasnt smarter prompting. it was moving the browser off my box so the logged in session survived between runs, instead of pretending my laptop was a good home for a browser

curious what people lean on to keep an auth session alive across deploys.. still the part nobody really solves​

Just started with apify automation

I'm a logistics coordinator in the UAE. Most of my day is taking sales orders and turning them into shipments: customs documents, invoices, packing lists, and booking small international parcels. A lot of it is copy-paste between systems, and I'm sure I'm doing some of it the hard way.

What are you automating in your role, and with what?

Generating invoices and packing lists from a sales order

Customs declarations and HS code lookups

Tracking updates and chasing carriers

Excel and Google Sheets workflows (macros, scripts, formulas)

Email sorting and templates for shared inboxes

Tools like Zapier, Power Automate or Make, or AI tools

I'd also like to hear what didn't work, so I can skip the dead ends. Thanks!

Shared this tool a while back and have been making some improvements to it. Been using it for our own internal operations since about May and found it especially useful for automating things that need a bit of manual web browser activity. It basically just uses your already installed Claude Code or Codex to create automations that you can run and manage in a local interface that keeps track of run history, catches an errors and helps fix them for you.

Each node is written as code by the Builder Agent, but it creates data validation checks and declares inputs and outputs separately, so you won't need to think about the code being there at all. The benefits over just using vanilla coding agents is that it doesn't just freehand the process, it follows a strict structure that ensures the automations can be run and managed through the interface you can see in the video. You can see run history, and if a step fails it stops there and keeps a snapshot of the data until it has been fixed so you can proceed without starting a new run from the beginning.

This is just a basic example of how we can download a list of everyone working for a company via the company's LinkedIn page. We've used it in my team since May and have not had any issues with LinkedIn yet, but have used in a sensible way, not to send 1000 connection requests per day or other stupid stuff like that.

I published it for free mostly because it's a bit different from what I've seen elsewhere, and I just think its interesting to get feedback from others and find ways to improve it for ourselves too. Or if there's a much better way and we've just missed it!

In high volume operations, one of the biggest constraint on business growth is almost always high stakes decisions sitting in a queue waiting on a scarce subject matter expert to review them.

We saw this firsthand working with an enterprise insurance provider processing thousands of commercial policy endorsements where their operational backlog looked like this: policy updates were sitting 4 to 8 hours in queue behind new business and every single endorsement took about 5 minutes of focused review from a licensed underwriter.

The specialist had to open the request, pull external regulatory safety registries and verify operating authority then scores and confirm loss history against internal policy guidelines.

Because qualified underwriters are expensive and difficult to hire, growing customer volume meant turnaround times got stretched thin creating friction with distribution partners and putting retention at risk.

Instead of treating this as a hiring problem, our team (withnative) deployed an operational agent system that resolves decisions the moment they enter the queue and the moment a submission lands, an ingestion agent pulls the policy changes and an automated worker queries live government registries to pull verified safety inspection histories and operating status.

To guarantee zero regulatory risk with the agents don't make probabilistic guesses and the business guidelines are codified into strict deterministic rules so if the registry checks and coverage limits meet the established criteria, the system clears the decision in 23 to 40 seconds and writes the update directly into their policy admin system and logs an immutable audit trail.

If an endorsement fails a rule say there is an out of territory hauling route or a flagged inspection violation, it routes straight to an underwriter with a pre filled decision brief and the underwriter opens the file with the government data already pulled and the discrepancy highlighted and eventually a 5 minute manual investigation turns into a 30 second sign off.

Over 5 months in production, the system processed 4,224 decisions with 3,130 of them (74.1%) were cleared completely autonomously in under 40 seconds and queue latency dropped from 8 hours to under a minute, freeing up 49 hours of specialist capacity per month without adding headcount and accelerating $2.39M in policy revenue velocity.

There are some tasks i am so tired of doing again and again they are not even difficult just annoying and time consuming what 's one thing you wish you could automate so you had never have to do it yourself again ?

I’ve been thinking about where a model like M3.1-Flash actually makes sense in office automation. Probably not the completely deterministic stuff. If a workflow is just: new row → copy field → send email I’d rather keep that as normal automation. The more interesting part is the messy middle where every case looks slightly different. For example: read several documents → figure out what matters → compare it against existing records → update a working sheet → flag anything ambiguous for a person. Or: collect updates from several places → decide which ones affect the weekly report → draft the changes → leave final approval to a human. I’m starting to test M3.1-Flash around that kind of workload because a Flash model makes more sense when the workflow needs a lot of reasoning calls. I still wouldn’t give it unrestricted write access. My first test would be whether it can reliably separate the boring cases from the ones that actually need human attention. Where do you draw the line between normal automation and an agent in office work?

I have been personally using Claude a lot, I’m on their 20USD plan.

I am mostly using their schedule features to create my content, SOPs, client’s blogs, etc., and updating everything in my ClickUp and Google sheet, and all assets in Google Drive.

Curious to hear from people running automation agencies or doing this for clients.

I'm working out a focus for my own AI automation agency. There's no shortage of workflows you can build, but figuring out which problems businesses actually want to pay to fix seems like the harder part.

Have you found an industry or type of workflow where demand feels fairly obvious, or does every project still take a lot of explaining and convincing? I'd love to hear how specialising has worked out for you, including things that looked promising but didn't sell.

And if there are good communities where automation agency owners talk honestly about the business side, I'd be interested in those too.

Hi, I’m Sidhant, a CS student working with n8n, Python, and API integrations. In my own independent project, A company-monitoring engine, I’ve built workflows for data intake, scheduled checks, alerts, bulk uploads, and error handling, with Python services doing some of the heavier logic.

I’m taking on a few small, clearly scoped projects while I build experience working with real users. For $25, I can build few simple n8n workflows with python microservice integration or fix few specific issues in an existing workflow. That includes testing with sample data and a brief handoff explaining what I changed.

if its just a simple clearly scoped project or a simple pre-existing workflow fix messing with your business , just send me the text and I will fix it up for free for an honest review :)

If your workflow needs several integrations, custom Python services, or ongoing support, I’d scope and price that separately. If you have a repetitive task or a broken workflow, describe the input, the result you want, and the tools involved in a comment. I’ll tell you honestly whether it fits this offer.

if its just a 30 mins fix, I would be happy to work for free to connect with you as a fellow engineer

Running ops at a 25 person startup and tryingto figure out how, to actually hand off recurring work to AI, not just answering questions, but stuff like following up on things, pulling info from different places, that kind of work, What have you tried that worked? Curious what approaches real teams are using, and what fell flat.

Now that the final M3.1-Flash is available, the use case I want to see more of is repetitive office work.

Not “write me a clever answer.”

More like

• read 50 documents

• pull deadlines into a sheet

• compare two versions of a file

• generate a recurring report

• extract action items

• update records

• route exceptions for review

That kind of workflow can involve a lot of model calls, so a Flash-class model gets interesting if it stays accurate enough while keeping the loop fast and inexpensive.

Space Bunny was an early preview, but I’d rather see fresh tests on the final M3.1-Flash now.

For people running real automations, what office task would you use to test it?

If you handle customer support yourself, refund and cancellation emails are the ones you want to see first. Here is the setup I use for that, and it costs almost nothing to run.

The trigger is a new email in Gmail. The automation checks whether the email mentions a refund or a cancellation. If it does, I get a message on my phone with the sender and a one-line summary of what they want. It makes no changes in Gmail, so it does not reply or label anything.

Before this, the same automation ran on a 15-minute schedule. That is 96 runs a day, with the email trigger it only runs when an email actually arrives, and up to 3 emails can be handled at the same time. The trigger is not instant. It took about 2 minutes in my tests.

I built this in my own iOS app, PocketClaw. If you want the same setup, search for "PocketClaw: Business AI Agent" on the App Store. It has a ready-made template called "Refund and cancellation emails".

Do you filter emails before the model step, or let the model read everything?

Disclaimer: I am NOT offering services; I am NOT experienced.

I have some ideas, that could help beginners.

First off, NOTHING is ever free, so don't mislead your clients.

You should probably work from the client's infra (meaning the client creates an account), NOT yours, so there are always operational costs.

Now you could cover the costs yourself, ofc.

Or you could just offer to either:
\- Offer free consultations.
\- Offer a free build cost, and keep the IP rights.

For both, you can take advantage of partner/affiliate programs.

Now, if you keep the IP rights, you can offer retainer services or offer to sell IP rights later if they want to hire another developer or work on it themselves.

We've been monitoring token usage and setting budget limits per model, and sending lighter workloads to cheaper models. The problem is that when we update the model pricing or change a model, our existing thresholds and routing rules don't work anymore. We've also been considering cost per request, however the increase in retries, and larger context windows has made it more challenging to pinpoint where the costs are coming from. So this still needs some additional manual investigation.

I was thinking of putting our thresholds more in line with model usage and cost, so it alerts us if a change in model or pricing goes above a set limit. But I'm worried about how to update routing rules without screwing up latency or output quality. How do you automate it in production where models and workloads change so often?