Agentic Coding Mag

Synchronous agents in editors versus asynchronous agents on platforms.

Agents need traditional testing layers applied to a new failure profile.

AI agents excel at backend work but struggle with frontend tasks that require visual judgment.

AI agents need careful setup to avoid predictable failures in complex legacy codebases.

Regulated firms must audit agent autonomy, not just model capability.

Auditability, data control, and vendor independence matter more than price in self-hosted agents.

Splitting deployment across your SDLC beats concentrating automation on code generation alone.

Max Mode lets agents complete multi-file tasks without losing track of earlier decisions.

BDD bridges the gap between AI-generated code and business requirements.

Pytest and sandboxes form the feedback loop that guides coding agents toward correct solutions.

Contamination and flawed tests undermine benchmark scores that now saturate near perfect.