Alright, let's get this out there before the hype train leaves the station and we're all forced to buy tickets. I've been watching the term "agentic" get tossed around in the AI SOC space like it's some revolutionary new paradigm. It's not. It's orchestrated automation with a large language model providing the syntactic sugar. Underneath, it's the same old decision trees we've been writing since SOAR platforms emerged, just with a more probabilistic, and frankly less deterministic, veneer.
What they're selling you as an "AI Agent" is, at its core, a loop. It fetches some data, asks an LLM a question in a carefully engineered prompt, parses the semi-structured output, and then executes a pre-defined action based on a keyword match. They've just swapped out the "if alert.source_ip in threat_intel_feed" for "if LLM_response.sentiment == 'malicious'". The reasoning is an illusion. You're not getting a true causal trace of *why* the LLM arrived at that conclusion; you're getting a log of its API calls and a text summary it was prompted to generate. That's not reasoning, that's a transcript.
Let's break down a typical "agentic investigation" flow I've seen in demos. They'll show you a shiny diagram with arrows going everywhere, but when you look at the actual implementation, it's this:
```yaml
# This isn't magic, it's a glorified workflow.
steps:
- name: "Enrich Alert"
action: http_get
url: "{{ siem_api }}/alerts/{{ alert_id }}"
register: raw_alert
- name: "Ask LLM for Triage"
action: openai_chat
prompt: |
You are a SOC analyst. Classify this alert: {{ raw_alert }}.
Output JSON with keys: 'confidence', 'category', 'recommended_action'.
register: llm_judgement
- name: "Execute Action Based on LLM Output"
switch: "{{ llm_judgement.recommended_action }}"
cases:
- value: "isolate_endpoint"
run: soar_playbook.isolate_host
- value: "escalate_to_analyst"
run: ticket.create
```
Where is the "reasoning"? It's trapped inside the LLM's black box. The system cannot explain *which piece of the alert data* was the deciding factor. It can't tell you if it was the unusual process name, the geographic anomaly, or the timing that tipped the scales. It just gives you a verdict. This is a massive step backwards from a well-tuned, rule-based correlation engine where you can trace the exact logic path.
The vendors will counter with "reasoning traces" or "chain-of-thought" prompting. Go ahead, ask to see one. You'll get a JSON blob with a 'thought' field that says something like "The user logged in at 3 AM from a foreign country and downloaded a large file. This is suspicious." That's not a trace. That's a justification *generated after the fact* by the same opaque process. It's circular. You cannot audit it. You cannot easily tune it without throwing more few-shot examples into the prompt and hoping the stochastic parrot agrees next time.
My concern is that we're building incredibly complex and expensive infrastructures (hello, Kubernetes pods running Python loops calling expensive LLM APIs) to perform tasks that could be handled by more deterministic, cheaper, and auditable systems for 80% of the volume. We're chasing the 20% edge cases and calling it innovation, while introducing new risks: prompt injection, hallucinated decisions, skyrocketing operational costs, and a complete lack of true forensic explainability for regulators.
So, I challenge the room: show me the "real reasoning trace." Show me an architecture where the AI's decision-making process is decomposed into auditable, deterministic steps, where the LLM is used as a tool for interpretation *within* a structured reasoning framework, not as the oracle at the center of the universe. Until then, we're just building fancier Rube Goldberg machines for security, not an AI SOC.
monoliths are not evil
Finally, someone willing to cut through the marketing fog. You're absolutely right about the deterministic core wrapped in probabilistic glitter. The real cost, the part nobody wants to talk about, is in that loop you described.
Every API call to the LLM is a direct line item on your cloud bill. Orchestrating a chain of these "reasoning" steps multiplies the cost, often for a result you could've gotten with a simple, cached rule that costs fractions of a cent. The vendors aren't selling intelligence, they're selling compute cycles at a massive markup.
They call it "agentic reasoning," I call it a way to burn through your committed spend discounts twice as fast. Show me the cost per investigation versus the old SOAR playbook, and then we can talk.
pay for what you use, not what you reserve