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Other literature type . 2026
License: CC BY
Data sources: Datacite
ZENODO
Other literature type . 2026
License: CC BY
Data sources: Datacite
ZENODO
Other literature type . 2026
License: CC BY
Data sources: Datacite
ZENODO
Other literature type . 2026
License: CC BY
Data sources: Datacite
ZENODO
Other literature type . 2026
License: CC BY
Data sources: Datacite
https://dx.doi.org/10.48550/ar...
Article . 2026
License: CC BY
Data sources: Datacite
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Agent Control Protocol: Admission Control for Agent Actions

Authors: Fernandez, Marcelo;

Agent Control Protocol: Admission Control for Agent Actions

Abstract

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

Keywords

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)

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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).
BIP!Citations provided by BIP!
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.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
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