AgentFlow: A Better Way to Work with Coding Agents

Engineering teams today write very little code by hand, with team members capping manual code at 15% and sometimes as low as 2%. When AI agents generate almost all the codebase, the quality bar rises significantly since every single mistake triggers extra review cycles, model calls, and wasted time.

After spending ten months building a complex project from scratch and maintaining multiple mature codebases, a developer discovered a reliable silver bullet. Rather than relying on a magical prompt, the solution is a disciplined process that prevents agents from wandering off track. This methodology has been packaged into AgentFlow, a micro-framework that gives developers and agents a shared collaborative workflow.

Stock photo for illustration only, not from the actual event

The core philosophy borrows concepts from various sources, refined through real-world software development. A primary reason generated code fails mirrors human error: context transfer in large projects is exceptionally difficult. New hires rarely know existing helpers or solved problems, and even eight-year veterans can miss deep architectural decisions buried in the repository.

Task comprehension represents another major hurdle. Managers, leads, and engineers frequently interpret requirements differently, and complete clarity often emerges only during implementation. Additional friction points include context transfer across separate chat sessions and massive file diffs that become nearly impossible for a human reviewer to audit thoroughly.

"Every new chat with an agent is like a developer's first day at the company."

Kachurun

Built-in planning modes and spec-driven frameworks like OpenSpec offer alternative solutions but introduce adoption friction. Teams often struggle to remember strict command sequences, causing specification directories to bloat until nobody takes ownership and the repository eventually gets abandoned.

AgentFlow incorporates concepts like Matt Pocock's /grilling technique, requiring agents to prove task comprehension before writing a single line of code by restating requirements, surfacing assumptions, and interviewing users on critical decisions.

Source: Dev.to