Suppose two agents are given the exact same task.
Agent A finishes in 20 seconds with 15 tool calls.
Agent B finishes in 45 seconds with 5 tool calls.
Which one would you consider "better"? I'm curious how people actually balance performance, reliability when evaluating agents.
I'm trying to go beyond the basic tutorials and understand how people actually design AI agent systems in practice. I'm particularly looking for resources that cover:
* Agent orchestration
* Tool calling
* Memory
* Planning
*Multi-agent systems
*Evaluation
*Production reliability
Books, GitHub repos, technical blogs, courses, or genuinely useful tutorials are all welcome.
When choosing between AI agents, most of the focus seems to be on the model they're using or what they can do in a demo. But I'm curious whether a history of actual performance would change how much you trust an agent.
If two agents can both complete the same task but taking different approaches, what makes one better than the other? Do you look at things like:
* Success rate
* Cost
* Speed
* Number of tool calls
* Reliability across repeated runs
* Quality of the final result or Something else?
I have found a lot of exciting ideas while searching on AI agents, but building the real systems often changes our perspective. So, I am curious was there a pattern, framework, or design decision that you expected to work well but turned out to be less useful in production? And what did you get from that experience
I am really curious on what people would like developing AI agents to have. Most frameworks are getting better at orchestration, memory, and tool calling. But what capabilities do you still feel are missing or underdeveloped?
I often hear people discussing about about scaling performance, but I'm curious about scaling costs. Was there a technical decision—architecture, caching, model selection, automation, or something else that ended up saving you more time or money than you expected?
With automation tools, LLMs, and agent frameworks evolving so quickly, the line between workflow and an agent sometimes feels blurry. What do you think separates a workflow with AI from something you'd actually call an AI agent?
It feels like the term "AI agent" is used to describe everything from simple workflows to fully autonomous systems now adays. What do you think is the biggest misconception people have about AI agents today?
Whether it's prompt testing, better evaluation, logging, versioning, or something else I'm really curious to know what practice has had the biggest impact on reliability in your projects.
Has removing a feature ever improved your product or made the user experience better? what was it and why did you decide to remove it?
It's easy to make predictions before real users get involved, but experience often changes those assumptions. What's one thing you were sure about before launch that users proved wrong?
These days I am trying to be more conscious of my AI use in developing so from experience what's one task you've completely handed over to AI, and is there anything you still prefer to do yourself?
I think every developer has experienced this at some point. So here is what happened I spent weeks building a feature which I was very excited about, only to realize users are much more interested in something else.
What similar thing have you experienced?
There are a lot of sayings in software development that seem overused when you're starting out. So what situation proved it for you and made you think "Now I get why everyone says that."
I have noticed and can surely tell that sometimes the biggest productivity gains don't come from new tools, but from changing the way you work.
So, what's one habit, workflow, or practice you wish you'd adopted much earlier and made your work more productive?
What challenges have you faced while building AI agents that people rarely talk about? I have seen a lot of discussion around prompting and frameworks, but I'm curious about the less obvious challenges people run into once they start building real agents.
There are new AI tools coming out almost every week for different activities. So I want to know which AI tool has genuinely become part of your daily workflow, and what specific task do you rely on it for?
One thing I've noticed is how easy it is to spend weeks polishing something that users may not even care about.
If you could go back to the beginning of your startup journey, what's one thing you wish you hadn't spent much time trying to make it perfect?
I've been spending more time working in Figma lately, and every now and then I discover a feature that makes huge difference in the way i work. It can be auto Layout, Variables, Components, Dev Mode, plugins, or something less obvious.
what's one feature or workflow that completely changed the way you design?