Are AI coding tools fundamentally changing Agile/team software development?
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Traditional agile practices always sucked and were beyond problematic.
Maybe the speed of AI is just making it more apparent.
Maybe the speed of AI is just making it more apparent.
The Core Tension:
We're facing pressure to adopt a more "startup-like" approach: bigger PRs, fewer tickets, individual engineers taking on massive chunks of work solo with AI assistance. The argument is that AI tools let one engineer build in 5-6 days what used to require parallelizing across a team.
But this seems to violate core software engineering principles:
- Knowledge silos: One person becomes "the GraphQL guy" with 8,000-line PRs that are impossible to meaningfully review
- No knowledge sharing: Junior engineers don't learn from participating in the work
- Bus factor: What happens when that person leaves?
- Code quality: Can you really review an 8,000-line PR, or does it become "ship it and fix bugs later"?
The Counter-Argument:
- Startups move fast this way and win
- AI tools ARE changing everything.. maybe we're the ones using "punch cards" by sticking to old practices
- The customer doesn't care about our internal code quality, only that features ship
- Does tech debt even matter anymore if AI can navigate messy codebases?
My Current Thinking:
AI tools absolutely make us faster, but they're a multiplier on existing skill. A senior engineer with Claude can maintain good architecture and patterns while moving 10x faster. A junior engineer might just produce 10x more mediocre code faster.
I believe AI should enhance our existing workflow... better ticket planning, faster implementation of small chunks, AI-assisted code review.. not replace the workflow entirely with "hero engineering."
But I'm genuinely uncertain:
- Are traditional Agile practices (small tickets, parallelized work, thorough code review, documented backlogs) becoming obsolete?
- Is this a genuine paradigm shift, or are we just rediscovering why those practices existed in the first place?
- How do you balance "move fast" with "build maintainable software" in the AI era?
- Does code quality matter if you can ship features quickly and customers are happy?
Context:
- Team of ~20 engineers across 3 teams
- Using Claude Code, Cursor, etc.
- Pressure from leadership who built solo/small-team projects quickly to adopt that approach at scale
- Some engineers still not using AI tools effectively (or at all)
Has anyone successfully navigated this transition? What does "good" software engineering look like in 2025 with these tools? Am I clinging to outdated practices, or are there real risks to the "move fast, big PRs, worry about quality later" approach?