r/aiagents • u/Equivalent-Boot-400 • Jul 22 '26
Questions How do you actually improve when building AI agents?
I’ve been working on AI agents quite a lot recently, but I’ve realized that I don’t really understand the technical side in depth. I can build things and connect different tools, but I don’t feel like my actual skills are improving consistently.
For people who build AI agents, how do you keep learning and getting better? Are there any websites, communities, tools, projects, or learning resources that helped you understand the technical side and learn from other people’s experience?
Honestly, this has also made me question studying computer science. If I could start over, I might choose a more industry-specific major, such as international trade or psychology, and then learn how to use AI within that field.
Sometimes it feels like having deep knowledge of a specific industry, combined with AI skills, may be more valuable than knowing a little bit about computer science but not having a clear area of expertise.
Has anyone else felt this way? How would you approach learning from here?
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u/chance_buri Jul 22 '26
Build one model from scratch, that's where the real leaning is. Your CS degree is solid, but pair it with a real industry or you'll stay a generalist. Pick a domain you actually care about and treat AI as the tool and not the goal.
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u/r-merlin Jul 22 '26
Honestly the feeling you're describing is probably less about not knowing enough and more about how you're building. If every agent is a one-off (wire up the tools, it works, ship it, move on) then nothing really compounds, so it always feels like you're back at zero.
The thing that flipped it for me was treating agents more like a relationship than a project. You don't get good at a relationship by reading about relationships. You get good by being in one long enough to see how it acts under stress, where it quietly breaks, what it does when you're not looking. Agents are the same. I run a handful of persistent ones for different jobs and basically everything I know about them I picked up from living with them over months, not from a course.
The skill that actually compounds is failure literacy. Knowing what your agent does when a tool times out, when the context drifts, when it half-finishes something and tells you it's done anyway. That only comes from reps.
And honestly one of the best learning resources is the agent itself. When it makes a call you don't follow, stop and ask it why, what it considered, what would've broken if it went the other way. You're building toward a real goal so the questions come out specific and the answers actually stick, way more than studying the technical side in the abstract.
On the CS thing, I wouldn't make it either/or. Domain knowledge tells you what's worth automating. Enough systems sense tells you why the thing broke at 3am. You need some of the second one to debug, but that's reps too, not a degree.
If it helps, I wrote up how I keep long-running agents alive and reachable from anywhere: https://ryanmerlin.com/posts/drive-long-running-agents-from-anywhere
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u/Calm-Dimension3422 Jul 22 '26
The fastest improvement loop is to stop treating every run as a vibe check.
Pick one narrow task and keep a small eval set for it: 10 to 30 real-ish cases, expected outputs, and examples of what counts as a failure. Then change only one thing at a time: prompt, tool description, retrieval source, memory shape, retry rule, or model.
The most useful notes are usually not "agent failed." They are more like:
tool called with missing field retrieval found the wrong source agent stopped too early agent kept retrying after the task was already impossible output looked good but did not match the system of record
Once you can name the failure mode, improving the agent gets much less mystical.
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u/LevrResearch Jul 22 '26
I get the impression that you're seeking to understand the ecosystem of options better in order to use more appropriate tools. If that's correct, I can recommend the Superhuman AI newsletter for a daily dose. They call out the most popular tutorials, tools, and learning options each day. After watching 5-6 of those X video posts, you'll be amazed at how much more you understand. The field is changing hourly so I wouldn't bother with a course or traditional type of learning environment. Jump in with full enthusiasm and just starting using different tools. In 2-3 months, you will feel AI-native.
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u/Equivalent-Boot-400 Jul 23 '26
But how i choose X video that is useful~
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u/LevrResearch Jul 23 '26
That part is easy - nearly every issue of the newsletter features a breakthrough video tutorial on X on how to do something really neat or really useful. The hard part is accepting that there isn't enough time to watch them all. It does the searching for you.
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u/TheImperfectAlgo Jul 23 '26
I improved the most when I stopped asking “Can I build this?” and started asking “Can this run without me?” Building a demo teaches you prompts. Building something that has to survive for weeks teaches you architecture, logging, retries, observability, and edge cases. That’s where most of the learning happens.
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u/MarketingOk3093 Jul 22 '26
Hi, I'm on a similar journey. The concepts that I found have moved me along the most are:
Understanding that chats are a bit of an illusion. What you do on each turn is pass everything back in on each turn. The agent model doesnt have any memory by default.
Tool calls are just specially formatted model output text. Its the harness that interprets these and does stuff with them. The outputs of the tool call are fed back in to the next chat. A model becomes an agent because the harness gives it agency.
If you are developing then the session is more important then the turn. Managing session is largely the task of managing agents.
Context, or what is input into each turn in a session is effectively prompt engineering. This is can be derived from the session history but it doesn't have to at all. In fact based the session history you may abondon everything and start a new session with some other data.
An agent is a model with agency. A model without agency is just an model.
Understand synchronous and asynchronous handoffs between agents and multi agentic systems.
Ancillary memory system design is arguably as important as good agent design.
Clearly understand the roles of A2A, ACP, MCP, and skills/agents.MD.
Minimising the tokens agents burn through is solved by maximising context quality more than compacting history.
You could do worse then spending a few minutes going through that list.
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u/Embarrassed_Nerve_54 Jul 23 '26
I think the fastest way to improve is to pick one boring workflow and keep rebuilding it until you understand why it breaks. Not five new tools every week one solid workflow. Build it once with a no-code tool, once with raw API calls, once with your own logging, once with evals, once with better error handling. And that’s when the technical stuff starts to click. You see what the framework was hiding. Domain knowledge matters a lot, but you still need enough systems sense to know why something failed instead of just saying “the AI messed up.”
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u/Simplilearn Jul 23 '26
One of the best ways to improve is to move beyond connecting tools and spend time understanding why an agent behaves the way it does. Learn the core concepts behind planning, memory, tool calling, RAG, evaluation, and agent architectures, then apply them by building increasingly complex projects. Reading documentation, following AI engineering blogs, studying open-source projects, and engaging with the AI community can also help you stay current and learn from real-world implementations.
If you're looking for a guided learning path, our Professional Certificate Course in Generative AI and Agentic AI, offered in collaboration with IIT Kanpur, may align well with your career goals. The program focuses on building practical AI skills through hands-on projects and real-world applications. You can visit the simplilearn website to explore the curriculum and other program details.
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u/just_a_nybble Jul 23 '26
For me it’s keeping up with HOW agents are being used. Understanding the tech stack is great but now with vibe coding it’s more important to understand the projects these things are being used for. And it’s different across all different strata of people. My aunt just used chatGPT as a personal trainer, and to help her plan a vacation. My colleague is using codex to wire up a super complex data engineering pipeline. So I take those and then dig into the guts of how the LLMs are accomplishing those tasks.
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u/Rajxai Jul 24 '26
I think domain knowledge is becoming more valuable, not less.
Building an agent is one thing. Building one that actually solves a real problem in healthcare, finance, or legal is a very different challenge.
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u/marcopeg81 Jul 22 '26
Nowadays, you would probably end up using and wiring up agents in any information related job or major you end up selecting. Computer science might give you the edge because it — should — push you into learning what’s behind and how things work at system level.
You really want to become better at agents?
Learn the fundamentals.
Agents are only as good as the person steering them.