Spend control and governance for AI agents

Cappr is a single proxy that sits in front of your AI stack. Point your agents' model, tool, and payment calls at Cappr and it observes spend in real time, enforces per-agent and per-workflow budgets, and records a tamper-evident audit trail of every request. Autonomous agents are powerful, but an agent that can call an API can also burn a budget, and an agent that can trigger a payment can do so faster than any human can review it. Cappr gives teams the guardrails to ship agents to production without hoping they behave.

Start in shadow mode, then enforce

Enforcement is graduated. You begin in shadow mode, where Cappr watches every call and reports what it would have done without blocking anything. Once you trust the picture, you tighten the policy step by step: warn on overspend, throttle noisy workflows, require human approval for sensitive actions, and finally hard-stop calls that exceed a budget cap. Because the ladder is incremental, you never flip a risky switch blind — each rung is backed by the data from the rung before it.

One audit trail across the whole stack

Every request through Cappr is logged with the agent, the workflow, the model or tool it hit, the cost, and the policy decision that applied. That gives finance a real spend breakdown, gives security an evidence trail for incident review, and gives engineers a way to trace exactly why a call was allowed or blocked. Cappr works with providers like OpenAI and Anthropic, and aligns with emerging risk guidance such as the NIST AI Risk Management Framework.