I think we're going to see different AI tools optimized for different stages of the development workflow, with developers assembling and using a collection of them, just like they currently assemble a collection of tools for different tasks within the stack (backend language, frontend language, database, cache, infrastructure, etc.). It's unlikely that there's ever going to be one AI coding tool to rule them all.
As someone building (and frequently using) an AI coding tool that is very much focused on development time[1], I still also use GH Copilot and ChatGPT plus heavily as well. In a team setting, I could definitely see using my tool in conjunction with Ellipsis too. A feature built partly (or entirely) with AI still needs PR review. And an agent focused specifically on that job is likely going to be better at it than an agent designed for building new features from scratch.
One analogy I see today is the typical testing pipeline. Unit tests get run locally, then maybe a CI job runs unit tests + integration tests, then maybe there's a deployment job which does a blue/green release. At every stage the system is being "tested", but because the tests validate different capabilities, it's like concentric circles that grow confidence for the change.
A software dev lifecycle that uses AI dev tools is similar. Agents will review/contribute at the various stages of the SDLC, sometimes with overlap, but mostly additive and building on the output of one another.
Comments
I think we're going to see different AI tools optimized for different stages of the development workflow, with developers assembling and using a collection of them, just like they currently assemble a collection of tools for different tasks within the stack (backend language, frontend language, database, cache, infrastructure, etc.). It's unlikely that there's ever going to be one AI coding tool to rule them all.
As someone building (and frequently using) an AI coding tool that is very much focused on development time[1], I still also use GH Copilot and ChatGPT plus heavily as well. In a team setting, I could definitely see using my tool in conjunction with Ellipsis too. A feature built partly (or entirely) with AI still needs PR review. And an agent focused specifically on that job is likely going to be better at it than an agent designed for building new features from scratch.
1 - https://github.com/plandex-ai/plandex
Definitely agree with this.
One analogy I see today is the typical testing pipeline. Unit tests get run locally, then maybe a CI job runs unit tests + integration tests, then maybe there's a deployment job which does a blue/green release. At every stage the system is being "tested", but because the tests validate different capabilities, it's like concentric circles that grow confidence for the change.
A software dev lifecycle that uses AI dev tools is similar. Agents will review/contribute at the various stages of the SDLC, sometimes with overlap, but mostly additive and building on the output of one another.