Comment by aabdi | Hacker News Reader

Comment by aabdi | Hacker News Reader

Parent Full thread

There's lots of ways. You have to upskill through the stages IMO. Write code, write w/ agent, write w/ multi agents, write w/orchestrators.

My way is to just run a giant AI agent factory engine and make the agents full flow do everything. (plan long term, write prd, task, review).

Here's ~4000 commits in last month as an example, i have about ~10k ish including private/work stuff? https://github.com/portpowered/you-agent-factory/commits/mai...

The premise when you get to full automation generally is you go full industral engineering:

1. watch overall flow, improve process via continuous improvement

2. work via checklists and gates.

3. replace process with mechanisms as much as possible (code > agents)

4. optimal throughput is continual testing and iteration (CI, CD), coverage, full e2e tests, mock everything, general best practices really.

decent blog: https://openai.com/index/harness-engineering/

general points:

- build lots of linters

- document literally everything (arch, prd, best practices in repo)

- too many agents at the same time makes lots of code conflicts, so need to consider architecture of code how to maximize concurrency.

Genuinely curious - in your case, where do the requirements for what needs to be built come from?

In every project I've touched, business requirements are always the bottleneck - so I've never been able to wrap my head around what kind of requirements can be fed into a setup like this at high enough volume to justify it.

myself? senior engineer role is basically figuring out the business problem and getting funding.

IDK i've always been able to get senior managers/managers to give me lots of leeway to do wtv i want and figure out problems. At least in most places i've worked at.

i work on platform stuff mostly, so there's always a large need for stuff. the backlog alone b4 all the agentic stuff was roughly 20-30x the capacity of any team (per year). 80% of requests were usually SVP goals and we'd just outright drop them due to lack of capacity or request HC transfer/away teams.

i.e. internal improvements alone were always massive (not gonna talk about prod code/cross team organization).

1. we need better test coverage for x,y,z

2. we need to be able to eval the long term costs (XX growth YOY, how to reduce)

3. the internal system streaming is inefficient, need to eval the alternative systems

4. we need better ops handling/management automation for issues and sev3s/sev2s. i.e. scaling, anomaly analysis, bugs introduced, improved metrics, dashboards.

5. DX stuff needs better handling, people keep confusing themselves on how to onboard. better docs on how to onboard, automation,

6. teams x,y,z are fighting with each other bcz they don't have a good grasp on systems, improve internal docs on arch and interop

7. we need automation to be able to more easily test our systems in an adhoc fashion

8. there's no linters for API platform, leading to bad results and inconsistency.

9. we're seeing bugs in the code, but aren't appropriately manualy testing after deployments. spawn 100 agents to do it, compile the results. do it every X days, feed the bugs back into the system.

i could go on and on. and this is one service, usually you own quite a few, and each one has their unique set of challenges.

Have you been able to build anything substantial with AI factory itself? I have done some of these experiments myself on these sort of things and found they ended up often being less effective than using the latest tools in harnesses like claude code.

But curious if you've found it to be a big unlock. I have been doing some of this industrial engineering myself.

well work wise its usually for adjacent tooling. It unblocks other things, but like actual prod code, i'm always a little skeptical.

For example we had this problem where we had to take in customer inputs for requests and calculate out the projected downstream TPS. This is fairly complex since we run a query parser/orchestrator.

This is expensive to write myself or to have engi's do it, but the scaling algorithms are all there and we have excel sheets for spreading out overall costs.

so then all was needed was basically write out a big spec of the reqs - give it the docs/parser code/excel sheets, then just have it span out the pieces as a sequential checklist. 1. CI/OPS 2. docs 3. test infra 4. incremental build out in phases to chain it all together.

Reply on news.ycombinator.com