AI Agents vs Agentic AI: How Cybersecurity Is Moving from Alerts to Autonomous Defence

Security operations have always relied on automation. Intrusion detection systems raise alerts on signatures, SIEM rules correlate events, and machine learning models classify anomalies. Yet these systems are not the same as intelligence. Today, as attackers increasingly automate their campaigns using adversarial AI, the security industry is confronted with an important distinction: AI Agents versus Agentic AI.

The difference is not merely semantic. AI Agents are task-bound assistants: they enrich logs, flag anomalies, or recommend actions within narrow boundaries. They are embedded everywhere in modern SOCs in Splunk’s Machine Learning Toolkit, in Microsoft Sentinel’s analytics rules, in Cortex XDR’s classifiers. They provide efficiency but no autonomy.

Agentic AI, by contrast, represents a new architectural model. It is not limited to classification. It reasons about the environment, maintains state, simulates attacker behavior, and takes bounded defensive actions according to policy. Where AI Agents stops at pointing out risk, Agentic AI can contain risk autonomously.

This evolution determines whether a SOC remains an alert factory dependent on human triage or becomes an autonomous cyber defense ecosystem capable of countering AI-driven adversaries in real time.

AI Agents vs Agentic AI – Quick Comparison

AI Agents in Cybersecurity

AI Agents is already part of daily SOC operations. They are the “quiet workers” hidden inside EDRs, SIEMs, and SOARs, performing very specific jobs.

For example:

• In Splunk, anomaly detection jobs can model login frequencies and flag statistical outliers.
• In Microsoft Sentinel, User and Entity Behavior Analytics (UEBA) models can identify improbable travel logins.
• In Palo Alto Cortex XDR, local ML agents score processes as benign or malicious.

These agents have well-defined properties:

• Scope is narrow. An agent that detects anomalous authentication cannot evaluate whether the account belongs to an administrator with lateral movement privileges.
• Operation is reactive. They respond to triggers in the data stream; they do not proactively test hypotheses.
• Action is limited. Output is usually an alert or enrichment tag, leaving remediation to humans or playbooks.

Example AI Agent in Action

AI Agents excel at atomic tasks. Below are examples that show the range of their utility from simple log parsing to SIEM-ready anomaly detection.

import re
logs = [
    "2025-09-13T10:01:00 LOGIN user=alice ip=10.0.0.5",
    "2025-09-13T10:02:00 LOGIN user=alice ip=8.8.8.8"
]
suspicious = []
for line in logs:
    if re.search(r"ip=(?!10\.0\.)", line):
        suspicious.append(line)
print("Suspicious logins detected:", suspicious)

This is useful but incomplete. It flags suspicious logins but does not correlate with privileges or simulate downstream impact.

from sklearn.ensemble import IsolationForest
import pandas as pd
import uuid
# Sample authentication telemetry
data = {
    "logins": [3, 4, 5, 200, 6, 4, 250],
    "geo_distance": [10, 12, 8, 7200, 15, 11, 6900]
}
df = pd.DataFrame(data)
# Fit anomaly detector
model = IsolationForest(contamination=0.2, random_state=42)
df["anomaly"] = model.fit_predict(df)
events = []
for i, row in df[df["anomaly"] == -1].iterrows():
    events.append({
        "event_id": str(uuid.uuid4()),
        "finding": "Authentication anomaly",
        "features": {
            "logins": int(row["logins"]),
            "geo_distance": float(row["geo_distance"])
        },
        "recommended_action": "investigate"
    })
print(events) 

This code works as a supporting agent. Integrated into Splunk or Sentinel, it might label logins that occur thousands of kilometers apart within minutes. However, at this point the agent stops. It does not ask if the account is a domain admin, nor does it disable access or simulate potential lateral movement.

import re, json
logs = [
    "2025-09-13T10:00:00Z LOGIN user=alice ip=8.8.8.8",
    "2025-09-13T10:01:00Z LOGIN user=bob ip=10.0.0.12",
]
privileged_accounts = {"alice": ["Domain Admin"], "carol": ["DB Admin"]}
def parse(line):
    m = re.search(r"user=(?P\w+)\s+ip=(?P[\d\.]+)", line)
    return m.group("user"), m.group("ip")
alerts = []
for line in logs:
    user, ip = parse(line)
    if not ip.startswith("10.") and user in privileged_accounts:
        alerts.append({"user": user, "ip": ip, "privileges": privileged_accounts[user]})
print(json.dumps(alerts, indent=2))

This correlates suspicious activity with account privileges, raising risk awareness.

import subprocess
def run_nmap(target):
    return subprocess.check_output(["nmap", "-sV", target]).decode()
def prioritize(scan_results):
    # Normally you'd parse XML and call an LLM for summarization
    return "Host 10.0.1.23: Apache 2.4.29 outdated. Priority HIGH. Mitigation: upgrade, restrict uploads."
scan = run_nmap("10.0.1.0/24")
print(prioritize(scan))

This shows how an agent can automate recon and then use LLM-based logic to prioritize findings.

AI Agents accelerate detection, but they remain bounded. They cannot execute containment or reason about multi-step attack paths.

Agentic AI - Autonomy, Reasoning, and Architecture

Agentic AI takes a fundamentally different approach. It is not a classifier or a single-task automation. It is a goal-oriented system with state and reasoning ability.

Architecturally, an Agentic AI system is composed of six interacting layers:

• 1.Perception Layer -Telemetry ingestion from SIEM (Splunk, Sentinel), EDR (CrowdStrike, Cortex), OT/ICS monitoring, and external threat intelligence feeds.
• 2.World Model -A graph database modeling assets, accounts, privileges, trust relationships, and observed behaviors.
• 3.Planner / Reasoner -Hybrid reasoning combining graph traversal, reinforcement learning, and LLM-based analysis to hypothesize attack paths.
• 4.Executor -Policy-bound adapters to enforce actions (disable AD accounts, push firewall rules, isolate endpoints).
• 5.Policy and Safety Layer -Execution control, ensuring actions are bounded by rules (passive, safe, active modes).
• 6.Feedback Loop -Post-action validation feeding outcomes back into the world model.

Where AI Agents stop at anomaly detection, Agentic AI reasons about what the anomaly means in context. It asks: If this account is compromised, what is the likely next step in the ATT&CK chain? What containment minimizes blast radius?

This mirrors the offensive capabilities already visible in autonomous red-teaming tools like MITRE Caldera and Prelude Operator, where AI models dynamically chain exploits. Agentic AI applies the same orchestration model defensively.

Real-World SOC Workflow Comparison

To understand the difference between AI Agents and Agentic AI, it helps to trace a single incident across both approaches.

Incident: A privileged domain account authenticates from an IP address later identified as a Tor exit node.

In a SOC relying on AI Agents, Splunk’s UEBA module might detect the unusual geo-velocity and raise an alert. The finding is added to the analyst’s queue. At this point, everything depends on human intervention. An analyst must check the IP against a threat intelligence feed, verify whether the account has privileged access, and eventually disable it in Active Directory if the risk is confirmed. The entire cycle could take hours.

By contrast, in a SOC powered by Agentic AI, the login event is ingested into a world model. The system cross-references the IP with OSINT feeds, tags it as malicious, and recognizes the user account as a member of the Domain Admin group. Using MITRE ATT&CK mappings, it simulates how this compromise could unfold: T1078 (Valid Accounts) enabling access, followed by T1021 (Remote Services) for lateral movement, and potentially T1548 (Abuse Elevation Control Mechanisms) to expand privileges. Because the simulated blast radius is critical, the system triggers defensive actions disables the AD account through Microsoft Sentinel APIs, isolates the endpoint using a CrowdStrike quarantine call, and opens a ServiceNow ticket mapped to ISO/IEC 27001 A.9.2.3.

The difference is not speed; it is capability. One generates an alert. The other executes a defense plan.

Integration with Frameworks and Tools

Agentic AI cannot exist in isolation. It must interoperate with the frameworks and tools already present in SOC workflows.

• MITRE ATT&CK: Provides the adversary model. AI Agents tend to detect individual techniques (e.g., T1078 logins). Agentic AI reasons across chains (T1078 → T1021 → T1548).
• Microsoft Sentinel: Exposes APIs for account disablement, conditional access enforcement, and automated rule updates. Agentic AI can call these directly as part of execution.
• Splunk: Acts as a data lake and detection engine. Agentic AI can subscribe to Splunk searches, enrich results, and trigger SOAR playbooks automatically.
• Cortex XSOAR: Functions as an orchestration plane. Where AI Agents simply pass alerts into playbooks, Agentic AI decides when and how to call playbooks.
• MITRE Caldera and Prelude Operator: Demonstrate autonomous red-teaming. Agentic AI mirrors these offense capabilities in a defensive posture, continuously validating attack paths and applying mitigation.

Example Agentic AI Workflow -LangGraph

Agentic AI uses reasoning loops and state to move from detection to containment. LangGraph provides a useful framework for modeling this.

from langgraph.graph import StateGraph, END

class SOCState:
    def __init__(self):
        self.alerts, self.decisions, self.actions = [], [], []

def detect_login(state):
    state.alerts.append({"user": "alice", "ip": "8.8.8.8", "asset": "srv-1"})
    return state

def check_intel(state):
    alert = state.alerts[-1]
    alert["intel"] = {"malicious": True, "confidence": 0.95}
    return state

def decide(state):
    alert = state.alerts[-1]
    if alert["intel"]["confidence"] >= 0.9:
        state.decisions.append({"action": "quarantine", "asset": alert["asset"]})
    else:
        state.decisions.append({"action": "escalate"})
    return state

def execute(state):
    decision = state.decisions[-1]
    if decision["action"] == "quarantine":
        state.actions.append(f"Quarantined {decision['asset']}")
    else:
        state.actions.append("Escalated to analyst")
    return state

workflow = StateGraph(SOCState)
workflow.add_node("detect", detect_login)
workflow.add_node("intel", check_intel)
workflow.add_node("decide", decide)
workflow.add_node("execute", execute)
workflow.add_edge("detect", "intel")
workflow.add_edge("intel", "decide")
workflow.add_edge("decide", "execute")
workflow.add_edge("execute", END)

result = workflow.compile().invoke(SOCState())
print("SOC actions:", result.actions)

This is not alerting; it is orchestration. The system maintains state, applies reasoning, and executes bounded containment, a qualitative leap from point automation.

Production-Grade Workflow

A more advanced LangGraph design includes:

• Adapters for EDR, AD, Firewall APIs.
• ATT&CK-based reasoning.
• Safe-mode enforcement.
• Compliance logging.

# Extended LangGraph pseudo-code (simplified for readability)
# Nodes: detect -> enrich -> simulate -> decide -> execute -> audit

This shows how Agentic AI goes beyond detection to stateful, policy-driven orchestration.

Execution Safety and Policy Guardrails

Autonomy in cybersecurity cannot be unconstrained. An Agentic AI system must operate under explicit safety modes that determine what kinds of actions it can take in different environments. Without such controls, there is a risk that a system could inadvertently disrupt production infrastructure.

The three commonly discussed execution modes are:

• Passive Mode: No interaction with assets. The system analyzes logs, configurations, or metadata but never generates traffic. This is suitable for production environments where zero operational impact is mandated.
• Safe Mode: Default mode for continuous defense. The system simulates exploit paths or containment steps using inert payloads. For example, a reverse shell is “deployed” only as a no-op file, or login attempts are simulated using known credentials. Safe Mode allows validation without risk.
• Active Mode: Full autonomy. Used only in staging or red-team labs, this mode allows execution of payloads, privilege escalation, and even persistence validation. It provides the most realism but is gated by environment.

A configuration file enforces these constraints:

production:
  mode: safe
  allow_write: false
  excluded_assets:
    - scada
    - backup_servers

staging:
  mode: active
  allow_write: true

lab:
  mode: active
  allow_write: true
  allow_network_scanning: true

Here, OT assets such as SCADA or backup systems are excluded entirely in production, reflecting IEC 62443’s principle of strict zone segmentation. Staging and lab environments, however, allow Active Mode for full-scope adversarial simulation.

Compliance and Reporting Integration

For CISOs, the value of Agentic AI is not simply in faster containment. It is in the ability to produce audit-ready evidence that connects technical actions to compliance frameworks.

A containment action such as disabling a domain admin account must not only be logged but also mapped to relevant standards. For example:

• **ISO/IEC 27001 -A.9.2.3:**Management of privileged access rights.
• IEC 62443 -SR 5.2: Account management in industrial control systems.
• NIST CSF -DE.CM-7: Monitoring for unauthorized personnel, connections, devices, and software.

An Agentic AI platform can generate structured outputs directly consumable by ServiceNow or Splunk dashboards:

{
  "incident_id": "INC-2025-0913-01",
  "finding": "Suspicious Domain Admin login from Tor IP",
  "attack_chain": ["T1078", "T1021", "T1548"],
  "action_taken": "Disabled AD account, quarantined srv-1",
  "confidence": 0.95,
  "compliance": [
    {"framework": "ISO/IEC 27001", "control": "A.9.2.3"},
    {"framework": "IEC 62443", "control": "SR 5.2"},
    {"framework": "NIST CSF", "category": "DE.CM-7"}
  ],
  "ticket_id": "SNOW-SEC-7734"
}

This is not just logging enrichment. It is automated compliance mapping, saving analysts from manual correlation work during audits.

Limitations and Research Challenges

While the promise of Agentic AI is strong, there are constraints that must be acknowledged.

• 1.Explainability: Autonomy without transparency is unacceptable. Every decision to disable an account or quarantine a host must be justified with a reasoning chain. CISOs and auditors require human-readable explanations.
• 2.Adversarial Robustness: Attackers can attempt to poison inputs, feeding false logs, manipulating threat intelligence feeds, or crafting adversarial examples to mislead models. Agentic AI must be hardened against such manipulation.
• 3.Integration Constraints: Current SOC tools expose APIs for data ingestion (Splunk, Sentinel) but not always for autonomous enforcement. This forces Agentic AI to use SOAR platforms like Cortex XSOAR as intermediaries.
• 4.Legal and Ethical Boundaries: While disabling accounts is acceptable, “hacking back” remains legally prohibited. Autonomous offensive countermeasures are off the table in production.

Conclusion

AI Agents remains indispensable. They power anomaly detection, log enrichment, and malware classification at machine speed. But they stop short of defense. They are assistants, not operators.

Agentic AI changes that paradigm. By reasoning over context, simulating attacker chains, and executing bounded defenses, it enables SOCs to match AI-driven adversaries’ step for step. It transforms operations from reactive alert triage to proactive containment.

The adoption path should be incremental:

• Step 1: Deploy AI Agents for deterministic detection tasks (anomaly detection, log parsing).
• Step 2: Pilot Agentic AI in Safe Mode, under strict policy guardrails and human oversight.
• Step 3: Integrate compliance mapping for audit readiness.
• Step 4: Expand scope to cover hybrid IT/OT environments, ensuring IEC 62443 and ISO 27001 alignment.

Done responsibly, this transition moves enterprises from point automation to autonomous defense, an evolution as fundamental as the shift from antivirus to EDR. Just as antivirus evolved into EDR, SOCs must now evolve into autonomous defence ecosystems. Those who adopt Agentic AI early will be the ones able to withstand AI-driven adversaries.