AI Agents vs. Automation Playbooks

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You finally got your SOAR playbooks working, alerts are flying through the system, and you’re not getting paged every 15 minutes. But then, like clockwork, some user drops a special character into a form and boom, your precious automation pipeline faceplants. Or worse, one of your vendors decides to change the format of their API response without notice, and suddenly half of your JSON-based parsing logic starts throwing errors like it’s auditioning for a bug bounty program. That moment when your EDR integration updates and all playbooks referencing it crash? Yeah, you think about changing careers.

Playbooks have been our automation crutches for years, and just as we get comfy, along comes the new hotness: AI Agents.

So what’s really the deal here? Is this just another buzzword shift like from SIEM to XDR, or are we talking about a genuine leap? Let’s dig into the actual differences, and maybe throw in a few opinions while we’re at it.

Table of Contents

• What is a Playbook ?

• What about AI Agents?

• Agentic RAG: Where Things Get Wild

• When Should You Use a Playbook?

• When Should You Use AI Agents?

• Risk Tolerance and Choosing the Right Approach

• Customisable vs Plug-and-Play Solutions

• Closing Thoughts

This edition is sponsored by BlinkOps

What is a Playbook ?

If you’ve ever done a SANS course or worked as part of a SOC, you’ve heard of them. In security, a playbook is a predefined, structured set of actions to guide teams through responding to specific threats or incidents. Think: “Phishing alert detected? Great, here’s how we investigate, block, and close the case.”

This concept became foundational with the rise of SOAR platforms. SOAR needs structure to function, you can’t automate orchestration without telling the system how. Hence, playbooks became the way we defined those workflows. Every vendor calls them that, and they became almost synonymous with "automated response."

What about AI Agents?

AI Agents are the next evolution in automation. Powered by large language models (LLMs), they don’t just follow steps, they make decisions. Instead of saying, “Do A > B > C,” you tell them the goal, and they figure out the path.

They’re adaptive, can handle fuzzier input, and often integrate easily via plug-and-play setups. In security, they’re increasingly being used to handle:

• Alert triage

• Threat enrichment

• Initial investigation flows
  

What makes AI Agents different isn’t just autonomy, it’s context awareness and multi-step reasoning. They can update their own steps based on what they discover mid-way. Total game changer for noisy environments or unstructured alert types.

Agentic RAG: Where Things Get Wild

Agentic RAG (Retrieval-Augmented Generation with Autonomy) takes the classic idea of "ask the LLM, and it grabs some docs first", and supercharges it. RAG is already nice because it lets models work with live data instead of just what they memorized during training. But let’s be real, classic RAG is one-shot: it runs one retrieval and moves on.

Agentic RAG brings actual brains into the loop. You get an AI agent that doesn’t just ask once, it thinks about what’s missing, rephrases queries, hits different data sources, and loops until it feels confident enough to give an answer. It’s basically an LLM that acts more like an actual analyst.

So instead of you hardcoding "fetch IOC data" from one TI feed, the agent figures out which feeds are relevant, what’s still missing, and whether it should pivot to internal logs, case notes, or even historical alerts. It dynamically orchestrates the data gathering based on real-time context.

Use cases? Triage workflows that depend on context. Enrichment that evolves based on what’s found mid-way. Even things like breach investigations, where every step might depend on what you learn in the last one.

When Should You Use a Playbook?

Playbooks are still super relevant. Don’t throw them out. Here’s when they shine:

• Legal or HR-related incidents (where the steps must be exact).

• Integrations where you need precise sequencing (like MFA disablement + access review).

• Environments with static, well-understood processes (e.g., containment flows). Like a good friend of mine Andrei Cotaie used to say, "stuff where you're not paid to think", this is where you want simple, step-by-step automation, no reasoning, no next-gen shenanigans. Just do the thing, and do it right, every single time.
  

Also, in regulated environments, you may be forced to prove exactly what your automation does. Playbooks give you that granularity.

When Should You Use AI Agents?

AI Agents are better when you want flexibility and speed:First-line triage of EDR alerts.

• Phishing triage and summary extraction.

• Enriching indicators with open-source and premium feeds.

• Auto-investigating malware samples or suspicious behavior.
  

In "Why SOCs are Turning to AI Agents", I wrote about how agents help with alert triage bottlenecks by replacing clunky sub-playbooks with decision-making logic. This is where they shine, handling repetitive, noisy tasks without you needing to micromanage every condition.

In "How I’d Use AI Agents in a Security Automation Platform", I dig into how these agents work as plug-and-play components inside SOAR tools. They're especially useful in enrichment and response phases where rigid workflows usually fall apart.

Then in "SecOps Process Blueprint", I broke down the whole incident response lifecycle, identification, investigation, containment, etc., and showed where AI agents can be slotted in to speed things up without sacrificing accuracy.

"Beyond the Tiered SOC" explores the whole idea of moving past outdated Tier 1/2/3 models, and yeah, AI agents are basically your new Tier 1. Faster, cheaper, and they don’t get bored.

And finally, in the "Blueprint for AI Agents in Cybersecurity", I go all-in on architecture. This one's for the folks who actually want to build multi-agent flows using concepts like ReAct, tool-use, and dynamic task planning (not just prompt + reply).

Risk Tolerance and Choosing the Right Approach

When considering AI agents versus traditional automation in cybersecurity, it's critical to think about your risk tolerance—how much unpredictability you can afford, and where you need consistent, auditable outcomes.

AI Agents (Left-side of IR): These are your go-to for high-speed, high-flexibility tasks. Perfect for:

• Triage of noisy EDR alerts.

• Fast alert summarisation.

• IOC enrichment from multiple feeds.

• Automated investigation of unusual behaviors.

But don’t let them pull the trigger on remediation. You don't want your agent nuking user machines because it flagged a scheduled script as C2. Let humans or well-controlled workflows make those final calls.

AI Workflows (Middle-ground): These offer flexibility with some guardrails. Think of them as guided decision trees—smart enough to adapt, but still restricted from making big mistakes.

Traditional Automation & Playbooks (Right-side of IR): Here it’s all about predictability. Use this for:

• Legal/HR-driven processes.

• Integration flows needing strict sequencing.

• Containment and remediation that must be provable and repeatable.

In regulated environments, that last group becomes critical—playbooks give you transparency, audit logs, and step-by-step accountability.

Ultimately, balancing agents, workflows, and playbooks is about knowing where you can afford risk, and where you absolutely can’t.

Customisable vs Plug-and-Play Solutions

Here’s the question that always sneaks in, do you want full control, or do you want something that just works out of the box?

At the end of the day, it’s a tradeoff. Flexibility versus speed. Custom builds versus simplicity. You can’t have both unless you’re okay living somewhere in the middle, tweaking just enough to make it yours, without going full DevSecOps chaos mode.

Some platforms now offer agent builders, where you get the best of both worlds. One example?

Closing Thoughts

So here’s the part where I’m supposed to wrap things up nicely, right? Let’s keep it real.

Playbooks got us through the early automation grind. They were our duct tape, structured, controllable, and honestly a bit fragile.

But don’t think for a second that dropping an AI agent into your SOC means you’re done. Nope. If your data is garbage, if no one knows where things are logged, or if you still rely on Bob from HR to confirm account access via email, yeah, your AI agent won’t save you.

Also, a lot of real-world stuff doesn’t show up in SIEM. It’s buried in emails, Slack messages, or someone yelling across the room. Make sure your setup can deal with that mess too.

So before you drop a bunch of budget on the latest AI-infused shiny object, check the plumbing. Do your teams talk to each other? Is the data clean? Can your agent even reach it?

Get those basics right and then, then, start building the SOC that doesn't make you want to throw your laptop out the window every Monday morning.