Military academies are not upper class at all, mostly middle class folks. Officers are generally of the same stock as any other white collar job in engineering, law, business, etc.
Why are you letting the LLM drive? Don't turn on auto-approve, approve every command the agent runs. Don't let it make design or architecture decisions, you choose how it is built and you TELL that clanker what's what! No joke, if you treat the AI like a tool then you'll get more mileage out of it. You won't get 10x gains, but you will still understand the code.
Personally I've found "carefully review every move it makes" to be an extremely unpleasant and difficult workflow. The effort needed to parse every action is immense, but there's a complete absence of creative engagement - no chance of flow state. Just the worst kind of work which I've been unable to sustain, unfortunately. At this point I mostly still do work by hand.
It's unpleasant for me at normal speed settings, but on fast mode it works really well: the AI does changes quickly enough for me to stay focused.
Of course this requires being fortunate enough that you have one of those AI positive employers where you can spend lots of money on clankers.
I don't review every move it makes, I rather have a workflow where I first ask it questions about the code, and it looks around and explores various design choices. then i nudge it towards the design choice I think is best, etc. That asking around about the code also loads up the context in the appropriate manner so that the AI knows how to do the change well.
It's a me in the loop workflow but that prevents a lot of bugs, makes me aware of the design choices, and thanks to fast mode, it is more pleasant and much faster than me manually doing it.
This is my biggest problem with the promises of agentic coding (well, there are an awful lot of problems, but this is the biggest one from an immediate practical perspective).
One the one hand, reviewing and micromaning everything it does is tedious and unrewarding. Unlike reviewing a colleague's code, you're never going to teach it anything; maybe you'll get some skills out of it if you finds something that comes up often enough it's worth writing a skill for. And this only gets you, at best, a slight speedup over writing it yourself, as you have to stay engaged and think about everything that's going on.
Or you can just let it grind away agentically and only test the final output. This allows you to get those huge gains at first, but it can easily just start accumulating more and more cruft and bad design decisions and hacks on top of hacks. And you increasingly don't know what it's doing or why, you're losing the skill of even being able to because you're not exercising it.
You're just building yourself a huge pile of technical debt. You might delete your prod database without realizing it. You might end up with an auth system that doesn't actually check the auth and so someone can just set a username of an admin in a cookie to log in. Or whatever; you have no idea, and even if the model gets it right 95% of the time, do you want to be periodically rolling a d20 and if you get a 1 you lose everything?
I don't think there's anything difficult or unpleasant about the process of letting the LLM run free, that's the whole point, it's nearly frictionless. Which includes not reviewing the code carefully. You say "need" but you mean "ought".
Reviewing isn't hard when the diff is what you asked for. It's when you asked for a one-line fix and get back 40 changed lines across four files. At that point you're not even reviewing your change anymore, you're auditing theirs.
I agree with this too. I decided on constraints for myself around these tools and I give my complete focus & attention to every prompt, often stopping for minutes to figure things through and make decisions myself. Reviewing every line they produce. I'm a senior dev with a lot of experience with pair programming and code review, and I treat its output just as I would those tasks.
It has about doubled my development pace. An absolutely incredible gain in a vacuum, though tiny compared to what people seem to manage without these self-constraints. But in exchange, my understanding of the code is as comprehensive as if I had paired on it, or merged a direct report's branch into a project I was responsible for. A reasonable enough tradeoff, for me.
That's the trap though. The moment you approve every step, you're no longer getting the product that was sold to you. You're doing code review on a stochastic intern. The whole 10x story depends on you eventually looking away.
Just don’t buy the tools for 10x improvements, buy them for the 1.1x improvement and the help it gives with the annoying stuff like refactoring arguments to a function that’s used all over, writing tests, etc. They can also help reduce cognitive load in certain ways when you just use them to ask about your large code base.
I’m basically saying don’t do parallel work, use it as a tool. Just sit there and watch it do stuff, make sure it’s doing what you want, and stop it if it’s doing too much or not what you want to do.
Maybe I’m just weird (actually that’s a given) but I don’t mind babysitting the clanker while it works.
I define tools that perform individual tasks, like build the application, run the tests, access project management tools with task context, web search, edit files in the workspace, read only vs write access source control, etc.
The agent only has access to exactly what it needs, be it an implementation agent, analysis agent, or review agent.
Makes it very easy to stay in command without having to sit and approve tons of random things the agent wants to do.
I do not allow bash or any kind of shell. I don't want to have to figure out what some random python script it's made up is supposed to do all the time.
This is a cool idea, can you write more about how your tools work or maybe short descriptions of a few of them? I’m interested in more rails for my bots.
I just made MCP servers that wrap the tools I need the agents to use, and give no-ask permissions to the specific tools the agents need in the agent definition.
Both OpenCode and VsCode support this. I think in ClaudeCode you can do it with skills now.
The other benefit is the MCP tool can mediate e.g. noisy build tool output, and reduce token usage by only showing errors or test failures, nothing else, or simply an ok response with the build run or test count.
So far, I have not needed to give them access to more than build tools, git, and a project/knowledge system (e.g. Obsidian) for the work I have them doing. Well and file read/write and web search.
I use Cursor but it's getting expensive lately, so I'm trying to reduce context size and move to OpenCode or something like that which I can use with some cheaper provider and Kimi 2.5 or whatever.
POC, sure (although 10x-ing a POC doesn't actually get you 10x velocity). MVP, though? No way. Today's frontier models are nowhere near smart enough to write a non-trivial product (i.e. something that others are meant to use), minimal or otherwise, without careful supervision. Anthropic weren't able to get agents to write even a usable C compiler (not a huge deal to begin with), even with a practically infeasible amount of preparatory work (write a full spec and a reference implementation, train the model on them as well as on relevant textbooks, write thousands of tests). The agents just make too many critical architectural mistakes that pretty much guarantee you won't be able to evolve the product for long, with or without their help. The software they write has an evolution horizon between zero days and about a year, after which the codebase is effectively bricked.
There is a million things in between a C compiler and a non-trivial product. They do make a ton of horrible architectural decisions, but I only need to review the output/ask questions to guide that, not review every diff.
A C compiler is a 10-50KLOC job, which the agents bricked in 0 days despite a full spec and thousands of hand-written tests, tests that the software passed until it collapsed beyond saving. Yes, smaller products will survive longer, but how would you know about the time bombs that agents like hiding in their code without looking? When I review the diffs I see things that, if had let in, the codebase would have died in 6-18 months.
BTW, one tip is to look at the size of the codebase. When you see 100KLOC for a first draft of a C compiler, you know something has gone horribly wrong. I would suggest that you at least compare the number of lines the agent produced to what you think the project should take. If it's more than double, the code is in serious, serious trouble. If it's in the <1.5x range, there's a chance it could be saved.
Asking the agent questions is good - as an aid to a review, not as a substitute. The agents lie with a high enough frequency to be a serious problem.
The models don't yet write code anywhere near human quality, so they require much closer supervision than a human programmer.
A C compiler with an existing C compiler as oracle, existing C compilers in the training set, and a formal spec, is already the easiest possible non-trivial product an agent could build without human review.
You could have it build something that takes fewer lines of code, but you aren’t gonna to find much with that level of specification and guardrails.
I don’t find it slower overall, personally, but YMMV depending on how you like to tackle problems. Also the problem space and the project details can dictate that these tools aren’t helpful. Luckily the code I write tends to be perfect for a coding agent to clank away for me.
The agent mostly helps me reduce cognitive load and avoid the fiddly bits. I still review and understand all of the code but I don’t have to think about writing all of it. I also still hand write tons of code when I want to be very specific about behavior.
anonu has explicitly said that they've wiped a database twice as a result of agents doing stuff. What sort of diff would help against an agent running commands, without your approval?
Agent does not have to run in your user context. It is easy mistake to make in yolo mode but after that it's easy to fix. e.g. this is what I use now so I can release agent from my machine and also constrain its access:
$ main-app git:(main) kubectl get pods | grep agent | head -n 1 | sed -E 's/[a-z]+-agent(.*)/app-agent\1/'
app-agent-656c6ff85d-p86t8 1/1 Running 0 13d
Agent is fully capable of making PR etc. if you provide appropriate tooling. It wipes DB but DB is just separate ephemeral pod. One day perhaps it will find 0-day and break out, but so far it has not done it.
You can ask it to make the changes in appropriate PRs. SOTA model + harness can do it. I find it useful to separate refactors and implementations, just like with humans, but I admittedly rely heavily on multi-provider review.
I hear this a lot but I don't think decades of experience atrophies irretrievably so quickly as to make it worth it (alone) to abstain from making full use of these tools. I still read and direct enough of the architecture to not be lost in the code it generates. Maybe you haven't tried using agents to reorganize/refactor as much - I have cleaner code than I did before when it was done by hand, because I can afford to tackle debts.
I also don't find the permissions it prompts for very meaningful. Permission to use a file search tool? Permission to make a web request? It's a clumsy way to slow it down enough for me to catch up.
Solving “cars killing kids” is a lot harder than solving “dropping bombs on schools killing kids”. With the latter, we simply stop doing it. We can’t just stop using cars right away.
This is the way to do it if you're a serious developer, you use the AI coding agent as a tool, guiding it with your experience. Telling a coding agent "build me an app" is great, but you get garbage. Telling an agent "I've stubbed out the data model and flow in the provided files, fill in the TODOs for me" allows you the control over structure that AI lacks. The code in the functions can usually be tweaked yourself to suit your style. They're also helpful for processing 20 different specs, docs, and RFCs together to help you design certain code flows, but you still have to understand how things work to get something decent.
Note that I program in Go, so there is only really 1 way to do anything, and it's super explicit how to do things, so AI is a true help there. If I were using Python, I might have a different opinion, since there are 27 ways to do anything. The AI is good at Go, but I haven't explored outside of that ecosystem yet with coding assistance.
If you use a type checker in strict mode (e.g. pyright with "typeCheckingMode: strict") and a linter with strict rules (e.g. ruff with many rules enabled), the output space is constrained enough that you can get pretty consistent Python code. I'm not saying this is "good Python" overall, but it works pretty well with agents.
I love it. I was getting burnt out due to ADHD or autism burnout but with AI tooling I’m able to work a full week without burnout. I think the kind of burnout I get is helped with these tools, but since I’m not neurotypical it’s different from the burnout people are getting from doing too much.
I do see “task expansion” happening often though. If I can do the full feature rather than doing baby steps I’ll often do that now, because wrangling code is easier.
People in the comments still confused about “agentic development” vs. “agentic development”. One uses the cli best, while the other cannot use a cli very well.
The first is using agents locally to develop.
The second is developing an agent. Not necessarily for coding, mind you. Not even for just text sometimes.
They are different cases, MCP is great for the latter.
This is obviously not what it is. If I give you APIGW would you be able to implement an MCP server with full functionality without a large amount of middleware?
I’ve implemented an MCP tool calling client for my application, alongside OAuth for it. It was hard but no harder than anything else similar. I implemented a client for interference with the OpenAI API spec for general inference providers, and it was similarly as hard. MCP. SDKs help make it easy; MCP servers are dead simple. Clients are the hard part, IMO.
MCP is basically just an RPC API that uses HTTP and JSON, with some other features useful for AI agents today.
The chatbot app initiates an OAuth flow, user SSOs, chatbot app receives tokens to its callback URL, then tool calls can access whatever the user can access.
If you use the official MCP SDK, it has interfaces you implement for auth, so all you need to do is kick off the OAuth flow with a URL it figures out and hands you, storing the resulting tokens and producing them when requested. It also handles using refresh tokens, so there's just a bit of light friendly owl finishing on top.
Source: I just implemented this for our (F100) internal provider and model agnostic chat app. People can't seem to see past the coding agents they're running on their own machines when MCP comes up.
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