I added two custom fields to my tickets and tasks in Super Easy CRM a little while back. Nothing super fancy, just some integer fields called estimated and actual time to complete. Additionally, I added some more values to Ticket Type and Task Type respectively. My goal was a pretty simple one: figure out how long it took me to get things done. I wanted to see how different my perception was from reality and though I consider myself a pretty realistic guy…I clearly underestimated the challenges I faced. We'll start off this deeply personal insight with my one true programming language love, Python.
Python Related Tasks
I have been writing Python for years, in fact, I even think in Python. Seriously, as I listen to problems and come
up with solutions, I am writing pseudo code in my mind before picking up my Macbook. In fact, I even name the
variables after the person talking and how I feel about them in the moment. So, if you see any
annoying_bob var anywhere in my code you know why.
When a Python task hits my queue, I estimate on the short end. I'm comfortable with the language, I know the patterns, and in my head that means it should be fast. Then I built Fantasy Brawls, a turn-based browser game, and hosted it on PythonAnywhere.
The code was fine...mostly. But the problem was that PythonAnywhere did not support WebSockets at the time. That has nothing to do with how well you write Python. It is a platform constraint, and it does not care about your virtual environment or how clean your project structure is. Real-time mechanics were part of the plan, so I had to rethink the whole approach. I ended up implementing polling instead, which works fine for a turn-based game but was not where I started and definitely not what I estimated.
When I looked at the task data afterward, actual time was close to three times the estimate. And looking back at the notes I had written on the ticket, the reason was right there: I had estimated the Python, not the environment around it. I never accounted for the hosting layer as a variable.
The Power Automate Situation
Power Automate desktop workflows look approachable. But that visual builder gives you this false sense of confidence and my estimates reflected that. Then I started layering in the desktop component and things got awfully humbling fast.
Here is what I kept running into:
- Window positions shift between runs for no obvious reason
- Screen coordinates that worked yesterday break today because something updated in the background (new UI elements, mostly)
- A dialog pops up mid-flow that the workflow has no idea how to handle
- Timeouts behave inconsistently depending on system load
And unlike a Python script, there is no clean stack trace telling you what broke. You are watching a workflow poke around a live desktop environment and the failure modes are environmental, not logical. Debugging it is slower than it looks and the first few times it happened I definitely uttered some phrases my kids cannot repeat.
What actually fixed it was getting more aggressive about error handling upfront. Anticipating the weird edge cases before they show up, building in fallbacks, and just making piece with the fact that the desktop will do something unexpected at the worst possible time. Once I started doing that, turnaround on fixes dropped significantly. My bug and analysis tickets in this category were consistently running way over estimate until I adjusted my mental model of what desktop automation debugging actually involves.
Where Otto Came In
I built Super Easy CRM to be the most useful tool a working professional has in their arsenal. When we added Otto, the platform went from useful to an absolute staple. After enough tickets and tasks had both time fields filled in, I started feeding that data to Otto, Super Easy CRM's AI assistant powered by Gemini. The trick is the notes. I have always written notes on my tickets but I got more deliberate about it once Otto started reading them. A time field tells you something took four hours instead of one. A note tells you it took four hours because PythonAnywhere does not support WebSockets and you had to scrap your original plan halfway through.
Otto looked at the type, the estimated time, the actual time, and the notes together and gave me a clear picture I could not have put together myself. Not because the data was hidden but because when you are inside the work it is too easy to write off individual overruns as bad luck or a one-off learning curve. Otto doesn't really care about such things. It isn't there to sugar coat things like its colleague ChatGPT...its there to provide insights and help you get things done.
For me that meant seeing clearly that my overruns were not random. They were concentrated in work that touched external environments and third-party systems, and my own notes showed I was usually finding out about the constraints mid-task rather than before I started. That is a planning problem, not a skills problem, and knowing the difference matters a lot when your time is genuinely limited.
The Changes Otto and My Custom Fields Inspired
A few concrete things shifted after seeing the data laid out:
- Real buffer on external dependencies. Anything that touches a hosting environment, third-party API, or desktop automation gets an honest estimate, not an optimistic one. The environment will do something unexpected, so I need to plan for it. Clients don't care that your hosting provider is tough to build on. They want their product delivered on-time and will shop elsewhere if you don't deliver.
- Pattern-driven investment in error handling. If a certain type of bug ticket keeps running over, that area gets better error handling and more coverage before the next ticket blows up, not after.
- Notes that actually mean something. This takes time and no one likes documentation but a note like "Fixed" or "All Set" is worthless." Something like "PythonAnywhere does not support WebSockets, pivoted to polling for turn-based game mechanics" is useful three months later when you are estimating something similar or when Otto is looking for context on why a development task ran long.
The notes are what make the whole thing work. Without them the time fields are just two numbers sitting next to each other. With them you have the story behind the gap, and that is the part you can actually learn from.
Want to Set This Up?
If you are already using Super Easy CRM, the custom fields take about five minutes to add. If you want to go further and wire up automations around your ticket and task data, I wrote a full walkthrough on connecting n8n to Super Easy CRM to automate your workflows. And if you are just getting started with what the Automation Builder can do on its own, the post on five CRM automations that save hours every week is a good place to start.
On the topic of building things that hold up over time, I also put together some thoughts on CRM security architecture when vibe coding, which is worth a read if you are spinning up integrations and want to make sure you are not leaving doors open you did not mean to.
