AI Agents Accelerate Development, Shifting Bottlenecks to Orchestration

Key Quotes

"Generative ai models have grown more powerful and are now being applied to a broader range of use cases the lines between data and ai engineering are becoming increasingly blurry the responsibilities of data teams are being extended into the realm of context engineering as well as designing and supporting new infrastructure elements that serve the needs of agentic applications"

Max Beauchemin explains that the increasing capabilities of generative AI are blurring the lines between traditional data engineering and AI engineering. This shift requires data teams to expand their responsibilities into "context engineering" and support new infrastructure for agentic applications.

"Orchestration is the foundation that determines whether your data team ships or struggles ETL, ML model training, AI Engineering, Streaming - Prefect runs it all from ingestion to activation on one platform."

This quote highlights the critical role of orchestration in data teams' success. Prefect is presented as a platform capable of handling diverse workloads, from ETL to AI engineering, ensuring that data operations can be managed effectively from start to finish.

"Teams that use Bruin need less engineering effort to process data and benefit from a fully integrated data platform."

Bruin is described as an open-source framework that simplifies data integration. The author argues that by handling data movement, lineage, quality, and governance, Bruin reduces the engineering effort required for data processing and provides a cohesive data platform.

"The bottlenecks have shifted very very quickly right so if you you know people report being multiplied by ai by different degrees some people will say like i'm 20 faster i'm slower personally like since the get go i've been like two five 10x like somewhere i would say between two x and 10x on coding specifically or you know the act of you know designing and shipping software some of the areas that are still you know that are kind of becoming a traffic jam now are you know code review for sure qa right so you still need to go in your app though you can get a lot of help from the agents to write good unit tests and integration tests"

Max Beauchemin observes that as AI accelerates software development, traditional bottlenecks like code review and QA are becoming more prominent. While AI can assist in writing tests, the human elements of review and quality assurance still present challenges in the accelerated workflow.

"Treating context as code is like you know the first thing to do probably and so that's definitely an important thing to do um to do early on and systematically now"

This statement emphasizes the importance of managing contextual information for AI agents as if it were code. Beauchemin suggests that organizing and treating this context systematically is a foundational step for effective AI-driven development.

"Agor is a tool where you can essentially manage the tool will manage your git work trees for you maybe we'll start from the ground up so first thing is you add your repos to agor and it's a super visual interface right so you you get into this agor interface and the first thing you would do is bring in the work the the the git repos you want to work with and then it will manage your work trees for you"

Max Beauchemin introduces Agor as an agent orchestration platform that visually manages Git worktrees. The core functionality involves users adding their Git repositories to Agor, which then handles the creation and management of worktrees for feature development.

"The beauty in agor is that you can really just define your own framework it's an open canvas you know you can define whatever zone you want and whatever prompts you want and whatever whatever workflow you want"

Beauchemin highlights Agor's flexibility, describing it as an open canvas where users can define their own frameworks, zones, prompts, and workflows. This allows for highly customizable agentic engineering processes tailored to individual or team needs.

"The meta stuff was kind of a discovery along the way right so early on i was like hey it'd be cool if agor exposed this mpc serve like this mpc agor service to itself so it can be used internally you know but externally too so since i've had that i added a lot of mpc tools right so get the list of session get the list of work trees and inspect the session you know fire up an environment look at the logs for an environment you know create a user"

Beauchemin discusses the development of Agor's internal MCP (Multi-Command Protocol) service as a key feature. This allows agents within Agor to interact with the platform itself, enabling functionalities like listing sessions, inspecting environments, and creating users, which was an emergent discovery during development.