
Autonomous agents can produce harmful behavioral patterns from individually valid requests. This class of threat cannot be addressed by per-request policy evaluation, because stateless engines evaluate each request in isolation and cannot enforce properties that depend on execution history. We present ACP, a temporal admission control protocol that enforces behavioral properties over execution traces by combining static risk scoring with stateful signals (anomaly accumulation, cooldown) through a LedgerQuerier abstraction that separates decision logic from state management. Under a 500-request workload where every request is individually valid (RS=35), a stateless engine approves all 500 requests. ACP limits autonomous execution to 2 out of 500 (0.4%), escalating after 3 actions and enforcing denial after 11. We identify a bounded state-mixing vulnerability where agent-level anomaly aggregation inadvertently elevates risk across unrelated contexts. ACP-RISK-3.0 resolves this by scoping temporal signals to (agentID, capability, resource), preserving enforcement within each context. We further identify deviation collapse: a degenerate regime in which enforcement is active but never exercised because upstream constraints eliminate the inputs required for DENIED decisions. We introduce Boundary Activation Rate (BAR) as a metric and counterfactual evaluation as a detection mechanism (Experiment 9: BAR drops from 0.70 to 0.00 under sanitization, restored to 1.00 via counterfactual injection). Decision latency: 767-921 ns (p50); throughput: 920,000 req/s. Safety and liveness model-checked via TLA+ (9 invariants, 4 temporal properties, 0 violations across 5,684,342 states), validated by 73 signed conformance vectors. Specification and implementation: https://github.com/chelof100/acp-framework-en
v1.23: deviation collapse (Exp 9), BAR metric, counterfactual evaluation, Failure Condition Preservation, ACP-RISK-3.0 in Technical Mechanisms, Related Work extended. v1.22: stateless vs stateful (500/500 vs 2/500), state-mixing (Exp 7) and ACP-RISK-3.0 fix (Exp 8). v1.21: TLA+ extended (9 inv, 4 temp, 5.6M states), token replay, PQ hybrid signing. v1.20: adversarial evaluation and performance
FOS: Computer and information sciences, Artificial Intelligence (cs.AI), admission control autonomous agents agent governance cryptographic protocol ACP institutional control Ed25519, Cryptography and Security (cs.CR)
FOS: Computer and information sciences, Artificial Intelligence (cs.AI), admission control autonomous agents agent governance cryptographic protocol ACP institutional control Ed25519, Cryptography and Security (cs.CR)
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 0 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
