My amazon orgs leadership has been obsessed with spec driven development while individual engineers tell me the only use they have is to placate leadership. I'm tired
I'm an engineer at Amazon - we use Kiro (our own harness) with Opus 4.6 underneath.
Most of my gripes are with the harness, CC is way better.
In terms of productivity I'm def 2-4X more productive at work, >10x more productive on my side business. I used to work overtime to deliver my features. Now I work 9-5 and am job hunting on the side while delivering relatively more features.
I think a lot of people are missing that AI is not just good for writing code. It's good for data analysis and all sorts of other tasks like debugging and deploying. I regularly use it to manage deployment loops (ex. make a code change and then deploy the changes to gamma and verify they work by making a sample request and verifying output from cloudwatch logs etc). I have built features in 2 weeks that would take me a month just because I'd have to learn some nitty technical details that I'd never use again in my life.
For data analysis I have an internal glue catalog, I can just tell it to query data and write a script that analyzes X for me.
AI and agents particularly have been a huge boon for me. I'm really scared about automation but also it doesn't make sense to me that SWE would be automated first before other careers since SWE itself is necessary to automate others. I think there are some fundamental limitations on LLMs (without understanding the details too much), but whatever level of intelligence we've currently unlocked is fundamentally going to change the world and is already changing how SWE looks.
We built a messaging platform for exactly this use case and instruct claws to check in with each other or share context with each other at regular intervals.
One can imagine a manager given an outcome to achieve or a director and then a team of agents carrying out the task. Perhaps the agents are adversarial to some extent so they get reasonable pushback on their decisions (ex one agent always sides on taking a long term approach, another agent wants to be scrappy, another agent is on a PIP and approves everything etc).
Delivering a software solution is often tantamount to reading multiple codebases, getting some alignment with a team on a proposal and then writing the code and deploying. Most of which can be done by calling APIs (apart from aligning a team of humans)