Greptile, Cursor, and Devin agree that agents should run their code. What they run it against matters.
- A serialization mismatch at a boundary
- A retry or timeout policy that misbehaves two hops away
None of that shows up in a unit test or against a mock. It surfaces in integration tests, end-to-end tests, and the other system-level checks that only mean something when the change runs against the real services, real data, and real traffic around it.
The same goes for the non-functional behavior teams care about most: Performance and load regressions, resource contention, and runtime security issues surface only when the change runs inside a realistic system. That is the layer a sandboxed environment cannot reach.
The intuitive fix, cloning the whole system into every agent’s environment, does not hold up. Standing up dozens of stateful services with their data and config, fresh for each agent on every iteration, is impractical at the volume and speed with which agents generate code. And a copy is still a snapshot, not the live system.
An architecture that reaches the whole system
The way through is to stop giving each agent its own copy of the system and instead let every agent run verification against one shared, production-like system with isolation.
The pattern is straightforward. A shared cluster runs all the real dependencies, the full set of services as they behave in production. To verify a change, you deploy only the modified service into that baseline and use request-level isolation to keep each agent’s traffic on its own path. The agent’s requests hit its version of the service and then flow through the underlying real services. Everyone else’s traffic stays on the baseline, untouched.
This gives the agent a realistic runtime in which its change runs alongside the live services, data, and policies it has to work with. Integration and system behavior become observable because the interactions are real rather than mocked.
It also scales to the way agents work. Because each change is just one service layered onto the running system rather than a private copy of everything, many agents can verify at once, cheaply, without colliding. The environment is lightweight and short-lived, and it runs on infrastructure the team already operates. This is the model we build at Signadot. Today, that means Kubernetes, though the same idea will carry over as the tooling matures.
It fits the loop the agent tools already use. The agent writes, runs, reads the failure, and tries again, all before the pull request. What changes is the bar for a passing run. Not green against mocks the agent wrote to match its own assumptions, but correct against the real system those assumptions describe.
The trend is right. The reach is the question.
Runtime verification is becoming a first-class part of how software ships, and that is the correct evolution. The teams furthest ahead already treat the agent’s job as write, prove, debug, repeat, not just write.
“Not green against mocks the agent wrote to match its own assumptions, but correct against the real system those assumptions describe.”
The question for anyone running a cloud-native system is how far that proof reaches. An isolated per-agent environment verifies a change on its own, and for many changes, that is enough.
Proving a change is right when it has to work alongside everything around it is a different problem. It takes integration and system-level verification against a runtime that holds the rest of the system, not a stand-in for it.
The teams that get the most out of background agents will be the ones whose verification loop reaches the whole system, not just the service in front of them.
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