By deterministic policy enforcement we mean rule-based checks that evaluate to an explicit allow or block decision at execution time. Today that includes a mix of regex-based checks (for example PII patterns), structured detectors, and hard limits or business rules like cost caps, rate limits, and permission constraints. These policies are evaluated inline before model or tool calls, so the outcome is predictable and auditable rather than probabilistic.

On cost tracking: yes, AxonFlow captures per-call metadata including model, tokens, provider, and cost, and attributes it to user, workflow, and tenant. In gateway mode this is per-call audit logging, and in proxy mode it extends across multi-step workflows so you can see cost accumulation per user or execution. We also recently shipped Workflow Control Plane which tracks policy evaluation and cost accumulation across multi-step agent executions, so you get a single audit trail and cost rollup for an entire workflow, not just individual calls. That's been a common pain point we've seen with teams running agents in production.

Each LLM or tool call is evaluated at execution time against the active policy context, which includes the user, workflow, step, and tenant. That allows different steps in the same workflow to run under different credentials, providers, or cost and permission constraints if needed.

In gateway mode, the orchestrator still issues the call, but AxonFlow pre-authorizes it and records the decision so the policy is enforced consistently. In proxy mode, AxonFlow holds and applies the credentials itself and routes the call to the appropriate provider.

The key point is that credentials and access rules are defined once and enforced centrally, while orchestration logic remains separate.

I have already forked the community codebase and am actively working on the implementation to ensure sensitive data in images is protected before reaching public APIs. I will share updates as soon as it's ready for use.