Cognitive Admission Control: Risk-Conditioned Assurance for Consequential Actions in Agentic Distributed Systems

Autonomous agents can produce harmful behavioral patterns from individually valid requests -- a threat class per-request policy evaluation cannot address, because stateless engines evaluate each request in isolation. We present ACP, a temporal admission control protocol enforcing behavioral properties over execution traces via static risk scoring combined with stateful signals (anomaly accumulation, cooldown) through a LedgerQuerier abstraction. ACP blocks execution based on deterministic, history-aware risk scoring -- not anomaly detection. Under a 500-request workload where every request is individually valid (RS=35), a stateless engine approves all 500; ACP limits autonomous execution to 2 out of 500 (0.4%), escalating after 3 actions and denying after 11. We identify a state-mixing vulnerability in ACP-RISK-2.0 (cross-context false denials) and introduce ACP-RISK-3.0, scoping anomaly signals to PatternKey(agentID, capability, resource). Decision evaluation: 739-832 ns (p50); throughput 1,720,000 req/s. Safety and liveness model-checked via TLA+ (11 invariants + 4 temporal properties, 0 violations) across 4,294,930,695 distinct states. We formalize deviation collapse -- enforcement active but never exercised due to upstream constraints -- and introduce Boundary Activation Rate (BAR) as its detection mechanism. An adversary suppressing BAR to 0.00 is detected via DeltaBAR before collapse (BAR_C=1.00). N coordinated agents accumulate risk independently; coordination window CW_appr=2N with zero deviation: activity scales linearly, preventing superlinear amplification. ACP is Paper 1 of a 6-paper Agent Governance Series: P0 -- atomic decision boundaries; P2 -- behavioral drift detection (IML); P3/4 -- governance structure, fair allocation, and irreducibility; P5 -- runtime execution validity (RAM, arXiv:2604.22898); P6 -- operationalization of RAM.

Agent systems execute through runtimes with very different control points: local coding tools, framework SDKs, managed agent platforms, API gateways, and observer-only integrations. A high-risk action such as publishing data externally may therefore appear as a shell command in one runtime, a tool call in another, and a hosted session transition in a third. This makes it difficult to answer a basic governance question consistently: what action was authorized, under whose authority, with what approval semantics, and with what evidence after execution? This paper presents Proof-Carrying Agent Actions (PCAA), a runtime-neutral governance model centered on an action certificate rather than on a vendor-native session record. PCAA organizes control around five checkpoints: pre-action admissibility, action open, assumption capture, approval, and outcome closure. It binds these checkpoints to a portable action envelope, runtime and approval receipts, and replay-ready proof. The model is extended in two practical ways: the certificate is externality-aware, carrying boundary facts such as destination visibility and account provenance, and approval is described by explicit enforceability classes rather than by a single reviewed or unreviewed bit. We study the model through a reference implementation in a heterogeneous agent control plane and a disclosure-bounded evaluation protocol. On a protected benchmark expanded from 24 executable seeds to 96 traces across four runtime families, PCAA preserves route quality while exposing distinct failure modes under ablation. The paper contributes a systems formulation of runtime governance around certificate-bearing actions and an implementation-grounded account of how that formulation can remain portable under runtime churn without collapsing into vendor-specific control surfaces.

Benchmarks, audits, and agent protocols describe performance, permissions, and repair, but not how observed evidence should change an agent's authority during a consequential task. We call this the assurance-transition gap. We propose a Runtime Assurance Contract (RAC), a policy-level formal schema binding autonomy boundaries, component eligibility, evidence state, transition policy, human-review capacity, and non-compensatory gates. Under RAC, soft metrics may inform routing, whereas a failed or unknown mandatory gate forces retry, switch, escalation, deferral, or stop; aggregate performance cannot authorize action. We define the contract, an evidence record, a permission rule, and five invariants, and illustrate them in clinical, industrial, and judicial failure probes. We then report a deterministic failure-injection study in agentic coding: 280 constructed cases evaluated by a gate conjunction, a score-only rule, and a restricted protocol baseline. At the published example weights and threshold, the score rule admits 80 of 100 block-required injections and all 40 review-required injections. Tuned in hindsight, it matches the conjunction on this corpus. For positive weights, a positive threshold, binary risk signals, zero-signal controls, and an injected case firing each signal alone, we show that exact agreement holds if and only if the threshold does not exceed the smallest weight. A separate set of 18 hand-authored traces checks version-pinned evidence and review transitions against simpler policy variants. In a further prospective synthetic holdout of 24 episodes, two blinded LLM judges assign identical labels to all 72 action attempts; RAC and a separately implemented full stateful baseline both match these labels. These studies test mechanisms on synthetic cases; they establish neither deployed safety nor cross-domain effectiveness.