I build SOC infrastructure. Not policy, not playbooks — the actual pipes that move alerts from detection to resolution. So when every vendor at RSA 2024 slapped "agentic AI" on their booth, I paid close attention. Not because I was excited, but because I knew someone was going to buy it and I was going to have to integrate it.
Here's my honest read after 18 months of watching this space evolve.
What "Agentic" Actually Means (vs. What Vendors Mean)
The term comes from AI research. An agentic AI system can:
- Perceive its environment
- Take actions autonomously
- Pursue goals across multiple steps without human hand-holding between each one
That's a meaningful technical distinction from a chatbot that answers questions or a model that classifies alerts. A truly agentic system doesn't just tell you an alert is suspicious — it investigates, enriches, correlates, decides, and acts. In sequence. Across tools.
Vendors are using "agentic" to mean approximately three different things:
- Automation dressed up — The tool follows a predefined decision tree. Nodes have more steps. Still deterministic, still brittle, just more flowchart boxes.
- AI-augmented workflows — An LLM sits alongside your existing SOAR/SIEM and provides suggestions. The analyst still drives. This is genuinely useful, just not autonomous.
- Actual autonomous agents — Systems that set sub-goals, use tools (APIs, sandboxes, ticketing systems), retry on failure, and operate continuously. This exists in labs and limited production deployments.
Most current "agentic SOC" products live in category 1 or 2. A few are genuinely approaching category 3. The question is: does that matter for your environment right now?
The Context: SOAR Is Dead, Long Live... What?
In August 2024, Gartner officially declared SOAR dead as a standalone category. This wasn't shocking to anyone who'd actually tried to maintain a mature SOAR deployment. The tools worked — until the environment changed, the playbooks rotted, and nobody had time to maintain them.
The gap SOAR left created real demand. n8n has partially filled it — there are now over 164 community-contributed SecOps workflows on n8n's template library, covering everything from threat intel enrichment to phishing triage. The appeal is obvious: engineers can read and modify the logic, it's not locked in a vendor GUI, and the operational overhead is lower.
But n8n isn't agentic. It's orchestration. The difference matters when you're deciding what problem you're actually trying to solve.
Automation vs. Autonomy vs. Intelligence
This is where most vendor conversations go sideways. Let me define the spectrum clearly:
Automation — If X happens, do Y. Deterministic. Predictable. Scales well. Limited to scenarios you anticipated. Your existing SOAR playbooks are automation. So is your detection rule that pages on failed logins.
Intelligence — Adding context, pattern recognition, and probabilistic reasoning. A model that looks at an alert and says "this looks like LAPSUS$ TTPs based on the lateral movement pattern" is intelligent. It's not autonomous — it still needs a human to decide what to do.
Autonomy — Taking actions in the world without explicit instruction for each step. This is where agentic AI lives. It's also where risk concentrates.
The distinction matters because vendors conflate these terms constantly. A product can be highly intelligent without being autonomous. And an autonomous product that's not intelligent enough is more dangerous than useful.
Real Tools in the Space
CrowdStrike Charlotte AI is the most widely deployed example of AI-augmented security operations. It operates in the intelligence tier — it can explain alerts, summarize incidents, suggest remediation steps, and surface context from Charlotte's training on CrowdStrike's detection data. It's not making decisions. It's making analysts faster. In production use, customers report 40-60% reduction in time-to-triage for Level 1 alerts.
Google's Agentic SOC (via Chronicle and Security AI Workbench) is pushing further. Their SIEM-native agents can autonomously investigate alerts, query threat intelligence, and generate incident summaries. In pilots, they've demonstrated agents that complete full investigation workflows — the kind that previously took an analyst 45 minutes — in under 3 minutes. That's not hype. That's real.
Torq and Tines represent the new generation of security automation platforms. Neither calls itself "agentic" — they're honest about being automation-first with AI assist. Torq's HyperSOC feature does bring LLM decision-making into playbooks, giving it some genuine intelligence. Tines keeps the logic fully transparent and human-controlled. Both are worth evaluating if you're replacing legacy SOAR.
Palo Alto XSIAM is the most aggressive bet on agentic operations from an incumbent. Their 2024 ROI study claimed 257% return over three years — the main drivers being analyst time savings and faster detection-to-containment cycles. Take the number with appropriate salt (it's vendor-published), but the directional story holds up: if you can automate L1 triage at scale, the labor savings are real.
The 3-Question Evaluation Framework
When a vendor walks into your office and says "agentic," here's what to ask:
Question 1: "What happens when the agent is wrong?"
This is the most important question and most vendors don't have a good answer. An autonomous agent that enriches threat intel wrongly is recoverable. An autonomous agent that blocks a user account, quarantines a production host, or opens a firewall rule based on a misclassification is a different problem entirely. You want to know: what's the blast radius of a bad decision? Is there a kill switch? Is there a human-in-the-loop option for high-impact actions?
Question 2: "Show me the decision log."
Any system making decisions that affect your security posture should be fully auditable. This isn't just a compliance requirement — it's how you learn whether the system is actually working. The log should tell you: what data the agent saw, what decision it made, why, and what action it took. If the vendor shows you a summary dashboard but can't produce a full decision trace, that's a red flag.
Question 3: "How does it handle novel situations?"
SOAR failed partly because playbooks couldn't adapt. Ask specifically: what does the agent do when it encounters an alert type that doesn't match any training data or configuration? Does it escalate gracefully? Does it fail open or closed? Does it log the gap so you can improve coverage? An agent that silently drops or misfires on novel inputs is worse than no automation.
The Implementation Checklist (For When You're Ready)
If you've decided to move forward with an agentic SOC component, don't start with the autonomy. Start with the foundations:
- [ ] Alert enrichment working cleanly — Agentic AI is only as good as the data it sees. If your SIEM is sending raw, unenriched alerts, an agent will make bad decisions. Fix your data pipeline first.
- [ ] Decision logging infrastructure in place — Before you let anything act autonomously, you need to be able to audit every decision. Build the logging before you turn on the agent.
- [ ] Scope limited to low-blast-radius actions first — Ticket creation, Slack notifications, threat intel lookups. Not host isolation, not account lockouts. Earn trust in low-stakes actions before expanding scope.
- [ ] Anomaly baseline established — You need to know what "normal" looks like in your environment before an agent can meaningfully identify "abnormal." Run the system in observation mode for 2-4 weeks minimum.
- [ ] Rollback plan documented — What do you do if the agent starts making systematically bad decisions? Who can turn it off, how fast, and what's the human fallback?
- [ ] Analyst feedback loop built — The agent should improve over time. Build a mechanism for analysts to mark agent decisions as correct or incorrect. This is how you get better over time rather than just degrading.
My Honest Assessment
The hype is real. So is the technology. They're just not at the same maturity level yet.
The productivity gains from AI-augmented security workflows are demonstrably real today — faster triage, better analyst experience, reduced alert fatigue. You don't need fully autonomous agents to capture most of this value. Start there.
True autonomous action — agents that investigate and act without human approval — is coming, and in some narrow contexts it's working in production today. But for most SOC environments, the readiness gap isn't in the technology. It's in the data quality, the process maturity, and the audit infrastructure.
The security teams that will benefit most from agentic AI in the next 18 months are the ones who use this time to fix their data pipelines, clean up their detection logic, and build the observability infrastructure that will make autonomous decision-making trustworthy.
Then the agents will actually have something to work with.
Practical Next Steps
If you're evaluating agentic SOC tools right now:
- Run a 30-day pilot with any candidate tool in read-only mode. Don't let it take action yet. Just watch what it would have done and compare to what your analysts actually did.
- Measure analyst acceptance rate for AI suggestions. If analysts are ignoring more than 30% of AI recommendations, the signal quality isn't there yet.
- Start with threat intel enrichment as your first autonomous action. It's high value, low risk, and will demonstrate ROI quickly.
- Pick platforms that separate intelligence from action — tools where the AI recommends and humans approve, with a clear path to automation as confidence grows.
The vendors pushing "fully autonomous SOC" as a day-one deployment are overselling. The vendors building toward it incrementally, with transparency and auditability at the core, are building something real.
Build for the future. But deploy for today.