Greptile, Cursor, Devin agree agents should run code

Three widely used coding agents -- Greptile, Cursor, and Devin -- have independently arrived at the same conclusion: agents should run their generated code, not just return a diff. The New Stack documented this convergence in June 2026, framing runtime verification as an emerging baseline for agentic development. The shift validates a tighter feedback loop -- generate, execute, observe, iterate -- rather than handing off an unverified change for humans to test downstream.

The harder problem is fidelity of the execution environment. For simple applications with a single database and no external dependencies, a mock-based sandbox provides a reasonable signal. For cloud-native systems -- microservices on Kubernetes with shared Kafka topics, Redis caches, Postgres instances, and downstream API calls -- mocks encode the developer's assumptions about integration boundaries, not the actual behavior of those dependencies.

Signadot documented the consistent failure pattern in May 2026: mocks return whatever the agent told them to return, so if validation only sees unit tests and mocks, the agent never discovers failures that matter in a distributed system. A change that triggers unexpected behavior in a downstream consumer, or corrupts shared state, will pass agent verification cleanly when the agent only sees a mocked version of those dependencies.

Shared staging was already under stress before agentic coding. At agentic scale -- with dozens of agents running concurrent tasks -- it becomes a contention bottleneck. Approaches like lightweight ephemeral Kubernetes environments that share the underlying cluster infrastructure (services, databases, queues run once) while giving each agent run an isolated routing and branching layer are gaining traction as a way to support parallel agent loops without collision.

The consensus that agents should run code is now settled. The open question -- what they run it against -- is the defining variable for agent reliability in production. For cloud-native teams, the gap between mock-verified agent output and production behavior is the primary source of agent-introduced bugs. Evaluating coding agent tooling should include whether the agent's validation loop can reach real upstream dependencies, not just whether it executes code at all.