Traditional software engineering tools — IDEs, linters, CI systems — are passive. They respond to human input and report results. Agentic engineering inverts this: the AI system initiates actions, makes decisions, and produces artifacts (code, PRs, comments) that humans review rather than produce.
Practitioners of agentic engineering design their workflows around agent capabilities and limitations. This means writing clear ticket descriptions that agents can parse unambiguously, maintaining test coverage high enough that agent-produced code can be verified automatically, and establishing review processes that catch the specific failure modes agents exhibit (plausible but subtly wrong implementations, over-broad refactors).
The engineering discipline also includes the infrastructure concerns: how agents authenticate to version control, how their costs are tracked, how their outputs are audited, and how the system recovers when an agent gets stuck or goes off-course.