Agentic AI Detection & Response (ADR)
- Agentic AI Detection and Response (ADR) is a systems architecture that unifies distributed sensing, contextual reasoning, and activity coordination under human governance.
- It employs layered agent roles, persistent memory, and orchestration to transform diverse signals into actionable decisions in dynamic environments.
- ADR enhances operational security by reducing false positives, lowering response latencies, and supporting graduated autonomy for both routine and high-stakes actions.
Agentic AI Detection and Response (ADR) denotes a class of closed-loop systems in which autonomous or semi-autonomous agents perform detection, contextual reasoning, decision-making, and response over persistent state, tool interfaces, and human governance. Across the literature, ADR is treated less as a single model than as a systems architecture: distributed sensors and micro-agents, LLM- or ML-enabled reasoning modules, memory, orchestration layers, and policy or governance functions that convert observations into coordinated action (Zambare et al., 12 Aug 2025, Barenji et al., 21 Jul 2025, Kojukhov et al., 12 Feb 2026). The term is used most explicitly in enterprise agent security as an EDR/XDR analogue “for agents,” observing prompts, reasoning, tool calls, and causal chains rather than only files, sockets, or processes (Li et al., 17 May 2026). In one financial-compliance source, “ADR” instead denotes Action Design Research, but the implemented artifact still decomposes monitoring, investigation, reporting, and feedback into bounded autonomous roles under compliance-by-design guardrails (Axelsen et al., 16 Sep 2025).
1. Conceptual scope and historical framing
ADR emerged from dissatisfaction with pipeline-centric architectures in which telemetry ingestion, classification, alerting, and response are treated as separate stages optimized for local metrics such as accuracy or latency. A recurring argument in the recent literature is that these pipelines struggle when evidence is incomplete, conflicting, distributed across tools and modalities, or entangled with organizational and regulatory constraints (Kojukhov et al., 12 Feb 2026, Lazer et al., 8 Jan 2026). The agentic alternative re-architects detection and response as a distributed cognitive system in which specialized agents interpret signals, form hypotheses, inject context, justify decisions, and regulate autonomy.
This shift is visible across several domains. In network security, "NetMoniAI" implements node-resident micro-agents plus a central controller for anomaly detection and advisory mitigation (Zambare et al., 12 Aug 2025). In enterprise agent security, ADR secures MCP-based agent workflows by reconstructing the chain from prompt to reasoning to tool invocation to side effect (Li et al., 17 May 2026). In observability, Adobe’s agentic framework turns alert triage into a ReAct-style loop over logs, code, runbooks, and deployment metadata (Bharadwaj et al., 31 Jan 2026). Related patterns also appear outside cybersecurity, such as epidemic response planning (Mao et al., 11 Dec 2025) and cloudburst prediction with coordinated evacuation (Syed et al., 27 Nov 2025). This suggests that ADR is best understood as a general architectural pattern for high-stakes, stateful detection-and-action problems rather than as a cyber-only product category.
A common misconception is that ADR is merely SIEM, SOAR, or IDS with an LLM interface. The cited work generally rejects that reduction. The meta-cognitive cybersecurity architecture explicitly argues that detection, hypothesis formation, contextualization, explanation, governance, and autonomy control should be decomposed into interacting agents rather than appended to a linear workflow (Kojukhov et al., 12 Feb 2026). Likewise, NetMoniAI positions agentic AI as continuous sense–think–act loops at each node plus a higher-level sense–think–coordinate loop at the controller (Zambare et al., 12 Aug 2025).
2. Architectural patterns and agent roles
The dominant architectural motif is layered, with a perception or sensing layer, one or more reasoning layers, an orchestration or planning layer, a response layer, and a governance or learning layer. NetMoniAI makes this explicit with Service, Agent, Model, and Application layers on each node, coupled to a FastAPI-based central controller that aggregates reports, infers distributed attack patterns, and produces recommendations (Zambare et al., 12 Aug 2025). AgenticCyber uses a four-layer structure—perception, analysis, orchestration, and response—with modality-specific Log, Vision, and Audio agents feeding an Orchestrator and a Responder (Roy, 6 Dec 2025). Adobe’s observability system separates a Splunk Agent, a Tools Agent, and a Reflection Agent inside a ReAct loop (Bharadwaj et al., 31 Jan 2026).
Specialized role decomposition is especially prominent in conceptual frameworks. One cybersecurity architecture defines Detection, Hypothesis Formation, Contextualization, Explanation, Governance, and Meta-cognitive Judgement as distinct agent classes coordinated through generative AI (Kojukhov et al., 12 Feb 2026). Another survey organizes risk around Perception, Reasoning, Action, and Memory layers, which in practice become distinct monitoring and control surfaces for ADR (Lazer et al., 8 Jan 2026). In financial crime compliance, the artifact-centric design distributes responsibilities across orchestration, transaction monitoring, investigation, reporting, and reinforcement roles, all mediated through shared artifacts such as alerts, cases, and reports (Axelsen et al., 16 Sep 2025).
Memory is a first-class component rather than a cache. NetMoniAI describes a lightweight memory buffer conceptually as a rolling window of events, with current planning conditioned on that state (Zambare et al., 12 Aug 2025). The general anomaly-management survey distinguishes short-term memory for current anomaly context and tool results from long-term memory for historical anomalies, prior interventions, and domain knowledge (Barenji et al., 21 Jul 2025). Long-horizon security analyses treat memory as a separate attack surface because poisoned or low-integrity state can persist across many planning cycles (Almalki et al., 12 Jun 2026).
Agent coordination ranges from optional advisory layers to stronger orchestration. NetMoniAI’s controller is explicitly advisory rather than commanding; node agents retain autonomy and continue operating if the controller is unreachable (Zambare et al., 12 Aug 2025). By contrast, multimodal systems such as AgenticCyber centralize fusion and response selection in an orchestration layer that dispatches remediation through APIs (Roy, 6 Dec 2025). A plausible implication is that ADR architectures distribute authority according to latency, safety, and fault-tolerance requirements: edge or local agents handle low-latency sensing, while central agents contribute correlation, policy, and global context.
3. Detection and reasoning mechanisms
ADR systems combine lightweight triggers with heavier contextual reasoning. In NetMoniAI, continuous monitoring watches latency, jitter, throughput, and packet loss; threshold breaches activate packet capture and deeper inspection. The triggering rule is expressed as
for a metric such as latency (Zambare et al., 12 Aug 2025). Feature extraction then feeds BERT or external LLMs, while the controller clusters and semantically correlates node reports to identify distributed attacks such as DDoS or reconnaissance (Zambare et al., 12 Aug 2025).
Other systems replace or extend thresholding with behavioral baselining, statistical anomaly detection, and multimodal reasoning. The adaptive cybersecurity architecture for cloud, API, mobile, and edge environments centers on behavioral fingerprints, decentralized risk scoring, and federated threat intelligence sharing; it reports Precision $0.91$, Recall $0.87$, and F1 $0.89$ for its agentic framework (Olayinka et al., 25 Sep 2025). AgenticCyber assigns modality-specific threat scores to logs, video, and audio, then applies scaled dot-product attention to produce a fused score before invoking Gemini for cross-modal threat hypotheses (Roy, 6 Dec 2025). Adobe’s observability agent performs log retrieval, causal correlation, runbook consultation, and code/deployment inspection inside a bounded ReAct loop, aiming not merely to say that an alert fired but to identify the likely fault component and recommended remedial actions (Bharadwaj et al., 31 Jan 2026).
Several papers formalize decision readiness rather than only anomaly detection. The meta-cognitive cybersecurity architecture conceptualizes a judgement function
where evidence , hypotheses , context , governance constraints , explanation adequacy 0, uncertainty 1, and operational risk 2 determine whether the system should act, defer, or escalate, and at what autonomy level 3 (Kojukhov et al., 12 Feb 2026). This reframes ADR from “best prediction” to “is it legitimate and safe to act now?” In more operational settings, RL-style formulations appear in anomaly management and adaptive cybersecurity, where states are environment or telemetry conditions, actions are diagnostic or mitigation choices, and rewards encode reliability, safety, cost, and false-alarm penalties (Barenji et al., 21 Jul 2025, Olayinka et al., 25 Sep 2025).
ADR research also broadens the meaning of “detection.” In EpiPlanAgent, detection includes epidemic-type recognition and the satisfaction of trigger conditions inside narrative reports before response plans are generated (Mao et al., 11 Dec 2025). In cloudburst response, perceptual agents detect convective initiation and downscaled rainfall risk before hydrological and routing agents act (Syed et al., 27 Nov 2025). This suggests that ADR can be understood as a family of systems that transform evolving observations into operationally actionable state estimates, not only as malicious-activity classification.
4. Response, autonomy, and coordination
Response in ADR ranges from advisory outputs to direct actuation. NetMoniAI’s current implementation emphasizes alerting, dashboards, natural-language summaries, and LLM-generated policy recommendations to human operators; adaptive mitigation and SDN-based enforcement are explicitly left for future work (Zambare et al., 12 Aug 2025). Adobe’s observability agent similarly produces actionable diagnostic summaries and may execute low-risk steps such as validation scripts, while higher-risk operational changes remain human-gated (Bharadwaj et al., 31 Jan 2026). By contrast, the adaptive cybersecurity architecture directly supports token revocation, access-policy changes, resource isolation, and API throttling, with a reported autonomous mitigation latency of 220 ms (Olayinka et al., 25 Sep 2025). AgenticCyber goes further by coupling fused threat hypotheses to a Q-learning responder that can call firewall, IAM, and policy-engine APIs (Roy, 6 Dec 2025).
A recurrent design principle is proportional or graduated autonomy. The meta-cognitive architecture states that weak, conflicting, or high-impact situations should be escalated, whereas low-risk or reversible actions can be autonomous (Kojukhov et al., 12 Feb 2026). Design-workflow research in professional settings reaches a parallel conclusion: participants were willing to grant agents operational authority over routine, low-risk chores, but insisted that final authority remain with humans for creative or high-stakes decisions (Wadinambiarachchi et al., 25 Sep 2025). Financial-compliance and observability systems embed the same pattern through policy gates, bounded mandates, and structured handovers (Axelsen et al., 16 Sep 2025, Bharadwaj et al., 31 Jan 2026).
Coordination is often decentralized at the sensing edge and centralized at the reasoning or policy layer. NetMoniAI’s node agents detect locally and can continue without controller connectivity, while the controller contributes global correlation and advisory policy (Zambare et al., 12 Aug 2025). In low-altitude IoT intrusion detection, the Perception and Memory Agent, Reasoning Agent, and Adaptive Classification Agent divide feature learning, feature selection, and resource-aware classification, with the LLM used mainly as a higher-level optimizer (Li et al., 25 Jan 2026). Agentic honeynet configuration offers another variant: IDS alerts remain the low-level detector, and an LLM-based control plane dynamically reconfigures which honeypots are exposed in order to maintain attacker engagement under budget constraints (Mirra et al., 14 Mar 2026). These examples indicate that ADR commonly separates fast local sensing from slower but semantically richer coordination.
5. Implementation patterns and empirical evidence
Reported evaluations are heterogeneous: some papers emphasize qualitative operator utility or latency, others provide benchmarked precision/recall, and a few describe production deployment. The table summarizes representative results.
| System | Domain | Representative reported result |
|---|---|---|
| NetMoniAI (Zambare et al., 12 Aug 2025) | Network security | End-to-end detection + analysis latency < 5 seconds |
| Adaptive cybersecurity architecture (Olayinka et al., 25 Sep 2025) | Cloud, API, mobile, edge | Precision 0.91, Recall 0.87, F1 0.89; 220 ms mitigation |
| Enterprise ADR for MCP agents (Li et al., 17 May 2026) | Agentic AI security | Zero false positives while detecting 67% of attacks on ADR-Bench |
| AgenticCyber (Roy, 6 Dec 2025) | Multimodal cybersecurity | F1-score 96.2%; latency 420 ms; MTTR reduction 65% |
| Adobe agentic observability (Bharadwaj et al., 31 Jan 2026) | Alert triage | 90% reduction in mean time to insight |
| LAE-IoT multi-agent IDS (Li et al., 25 Jan 2026) | Aerial IoT intrusion detection | Superior performance of over 90% classification accuracy |
| AVDA (Bulut et al., 26 Mar 2026) | Detection authoring | Agentic workflows achieve a 19% overall similarity improvement over Baseline |
These results illustrate both the promise and the uneven maturity of ADR. Some systems are strongly benchmarked. Enterprise ADR for MCP-based agents was deployed at Uber for over ten months, reached over 7,200 unique hosts, processed over 10,000 agent sessions daily, achieved zero false positives while detecting 67% of attacks on ADR-Bench, and reached 97.2% precision in a credential-prevention layer that detected 206 true credential exposures (Li et al., 17 May 2026). Adobe’s observability deployment reported Mean Time to Insight of 2.3 minutes, Error Localization Accuracy of 88.4%, and Alert Responsiveness of 90.4% over 250 alert events (Bharadwaj et al., 31 Jan 2026).
Other evaluations remain narrower or more qualitative. NetMoniAI reports low latency and successful role inference in a testbed and NS-3 simulation but does not provide precision, recall, or F1 (Zambare et al., 12 Aug 2025). The LAE-IoT framework reports over 90% classification accuracy across benchmark datasets, but response actions remain largely conceptual (Li et al., 25 Jan 2026). AVDA shows that agentic authoring improves overall detection similarity, with especially strong TTP matching at 99.4% and syntax validity at 95.9%, but weak exclusion parity at 8.9% and logic equivalence at 18.4%, which limits unattended deployment of generated detections (Bulut et al., 26 Mar 2026). A plausible implication is that ADR components often mature unevenly: contextual authoring, triage, and explanation may become production-ready sooner than fully autonomous enforcement.
6. Governance, risks, and future directions
The strongest consensus in the literature concerns governance. Long-horizon security analysis classifies threats into Input-Based Attacks, Memory Attacks, Tool-Related Attacks, Planning Attacks, and Multi-Agent Attacks (Almalki et al., 12 Jun 2026). The agentic AI survey frames these risks across Perception, Reasoning, Action, and Memory layers, emphasizing agent collusion, cascading failures, oversight evasion, and memory poisoning (Lazer et al., 8 Jan 2026). Together, these works imply that prompt filtering alone is insufficient; ADR must instrument memory writes and reads, tool invocations, inter-agent communication, and goal evolution over time.
Agent governance therefore becomes an ADR requirement, not an afterthought. Financial-compliance work emphasizes artifact-centric logging, explicit role boundaries, guardrails, and compliance-by-design (Axelsen et al., 16 Sep 2025). Enterprise observability and MCP security systems use policy gates, bounded reflection loops, uncertainty tagging, and restricted tool scopes (Bharadwaj et al., 31 Jan 2026, Li et al., 17 May 2026). The offensive-risk forecast for enterprises and the Mittelstand argues that identity, phishing-resistant authentication, patch velocity, CI/CD and Linux/container hardening, agent governance, telemetry, and recovery readiness should be strengthened immediately (Koch, 6 May 2026). This aligns with proposals for immutable audit traces, compliance-aware policy modules, and tighter runtime control of non-human identities (Olayinka et al., 25 Sep 2025, Lazer et al., 8 Jan 2026).
Future work across the corpus converges on a few themes. Several systems aim to close the loop from advisory response to direct actuation, for example through adaptive mitigation and SDN-based enforcement in NetMoniAI (Zambare et al., 12 Aug 2025). Others call for safer policy learning through digital twins and simulation, especially for complex systems and high-impact domains (Barenji et al., 21 Jul 2025). Benchmarking remains a major gap: long-horizon agentic systems lack unified standards for measuring propagation, persistence, and recovery under attack (Almalki et al., 12 Jun 2026). Detection authoring research likewise points toward execution-based validation, telemetry replay, and richer MCP tool suites for historical alert analysis and efficacy testing (Bulut et al., 26 Mar 2026).
Taken together, the literature presents ADR as a systems discipline concerned with governable autonomy under uncertainty. Its defining features are persistent memory, tool use, iterative planning, cross-layer observability, and explicit regulation of when action is authorized. Where those elements are well integrated, ADR becomes more than faster alerting: it becomes an architecture for producing operationally actionable, auditable decisions from evolving evidence (Kojukhov et al., 12 Feb 2026, Barenji et al., 21 Jul 2025).