Cost recommendations from native tooling are accurate but contextless. They tell you a database is over-provisioned without telling you who owns it, what changed last week, or how risky the resize is. They go to dashboards rather than to the people writing the Terraform.

Engineering teams meanwhile experience FinOps as someone else's problem, surfaced quarterly in a slide that is already out of date.

A discovery agent correlates billing data with workload owners, tags, and recent deployments, answering the 'who and why' that native tooling skips.

A recommendation agent translates findings into the engineer's own language: explicit cost-versus-risk trade-offs, with the proposed Terraform change attached.

An execution agent applies pre-approved low-risk changes (rightsizing, lifecycle policies, idle-resource cleanup); everything else routes to a human approver in the chat tool the team already uses.

An agent that can apply changes is only acceptable if the boundary of what it can do is explicit and auditable. We codify the boundary in policy-as-code: a human-approved manifest of action types, scopes, and blast-radius limits.

Every action is logged with the prompt, the retrieved context, the model, the proposed change, the approver (human or policy), and the outcome. Auditors should be able to reconstruct any decision after the fact.

Annual spend reductions in the high-twenties to low-thirties percent are achievable on mature multi-cloud estates within the first year, and often more in the first quarter where the obvious waste lives.

The more durable benefit is operational: FinOps becomes a continuous practice rather than a quarterly exercise, and engineering teams own their costs because the agent meets them where they work.