The third layer is MCP servers and CLIs for cloud integration. The agent can scale a dev environment up, deploy a candidate, tail logs, fetch metrics, scale back down, with the same guardrails I have as the human operator. Reversible by default, audited by the cloud provider's own controls.
With those three layers in place, three things compound.
First, the loop that used to cost a day costs an hour. Ten iterations becomes doable. A multi-component sequence becomes solvable.
Second, the loop can run when I am not at the desk. I scheduled long-running analyses to run overnight while I was offline, and I came back in the morning to results rather than to the queue of work I had left behind.
Third, the loop is repeatable. The same automated process that helped me find the answer will check the productised fix when the dev team ships it. I do not have to rebuild the environment, rewrite the load test, or re-derive the measurements. I can confirm the requirements are met in the same complex setup without much extra work.
That third one is the one that matters most for the investment case. Each layer alone is interesting. The three together compound across the whole lifecycle: faster discovery, work that runs off-hours, and cheap re-checking when the fix lands.
Investing in agentic tooling is not investing in a single product. It is investing in the predictable glue, the product MCP, and the cloud integration as a stack. The engineers who get to use that stack will solve problems the team has been carrying for years, and will keep checking those fixes cheaply every time they ship in a complex environment.