The traditional software development workflow — requirements gathering, design, implementation, code review, testing, and deployment — has remained largely unchanged for decades. AI agents are now capable of handling significant portions of each stage, fundamentally altering how software teams operate.
What's Already Being Automated
Code generation is the most visible change, but it's just the beginning. AI agents now handle: automated test generation with high coverage, code review with security and performance analysis, documentation generation from code, bug detection and automatic fixes, and deployment pipeline management. Each of these capabilities existed in primitive form before LLMs, but agents bring reasoning and context-awareness that makes them genuinely useful in production.
The Human Role Evolves
This doesn't mean developers become obsolete. Instead, their role elevates. Senior engineers become system designers who define the constraints and goals that agents work within. They review agent output at a higher level of abstraction, focusing on architecture and business logic rather than syntax and implementation details. The best teams we've seen use agents as force multipliers — one engineer with good agent tooling can deliver what previously required three or four.
The transition is happening now, and companies that resist it will find themselves unable to compete on speed and cost with those that embrace it.