Most discussions about AI agents focus on planning, memory, or tool use.
But many failures actually happen one step later: when the agent executes real actions.
Typical problems we've seen:
runaway API usage
repeated side effects from retries
recursive tool loops
unbounded concurrency
overspending on usage-based services
actions that are technically valid but operationally unacceptable
So we started building something we call OxDeAI.
The idea is simple: put a deterministic authorization boundary between the agent runtime and the external world.
Flow looks like this:
the agent proposes an action as a structured intent
a policy engine evaluates it against a deterministic state snapshot
if allowed, it emits a signed authorization
only then can the tool/API/payment/infra action execute
The goal is not to make the model smarter.
The goal is to make external side effects bounded before execution.
Design principles so far:
deterministic evaluation
fail-closed behavior
replay resistance
bounded budgets
bounded concurrency
auditable authorization decisions
Curious how others here approach this.
Do you rely more on:
sandboxing
monitoring
policy engines
something else?
If you're curious about the implementation, the repo is here:
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