From Alert Fatigue to Agentic Response: How Workflows and Agent Builder Close the Loop

SOC leaders face a daily battle against basic math that doesn’t add up. Data volumes are growing exponentially, attack surfaces are expanding globally, yet your team’s capacity remains linear. You cannot hire your way out of this problem.

Attempting to chase individual alerts is a losing strategy. To succeed, we have to move beyond simple automation scripts and into the era of Agentic AI.

At Elastic, we view the modern security operation as an operational nervous system. It needs Senses (the data foundation to see everything), a Brain 🧠(AI driven analytics to find the signal in the noise), and Hands 🙌(Workflows to execute actions and drive outcomes).

With the introduction of Agent Builder and Elastic Workflows, we are unifying these elements. We aren't just giving you a chatbot; we are giving you the ability to construct an autonomous SOC where agents reason over data and workflows execute sophisticated actions—bidirectionally.

Here is how these two powerful engines work together to transform your security operations.

The Power of "Brain" and "Hands" Working Together

To understand why this combination is significant, we must differentiate their roles.

• Elastic Workflows (The Hands): These are deterministic. They are perfect for rigid, repeatable processes—"If X happens, create a Jira ticket, ping Slack, and isolate the host." They provide structure, auditability, and reliability.
• Agent Builder (The Brain): Agents are probabilistic and reasoning-based. They perceive the environment, plan a sequence of steps, and adapt. An agent can look at a vague threat report and decide which queries to run to find evidence.

The magic happens when they interact: Previously, you had to choose between a rigid playbook or a manual investigation. Now, Workflows can invoke Agents to perform complex analysis during an automation loop, and Agents can invoke Workflows as tools to perform reliable, heavy-lifting actions during a chat.

What This Isn't

Let's be clear: this isn't about replacing your analysts. It's about removing the toil that keeps them from doing the work that actually matters - the creative, adversarial thinking that no model can replicate. The goal is to shift your team from being reactive log-chasers to proactive threat hunters. The agent handles the grunt work; your people handle the judgment calls.

Use Case: Automated Triage at Alert Time

From Alert to Analysis without Human Intervention

Let’s look at a real-world scenario involving a ransomware attack (ex: BlackCat/ALPHV - a ransomware-as-a-service operation). In a traditional setup, an alert fires, and an analyst spends 30 minutes gathering logs, checking virus totals, and writing a summary.

With Elastic, this entire triage phase is automated before the analyst opens their laptop, reducing mean-time-to-triage from 30 minutes to under 2 minutes.

The Workflow:

1. Trigger: Attack Discovery runs on a schedule and correlates 15 disparate alerts into a single, high-fidelity Attack Chain.
2. Workflow Step (Enrichment): The workflow is triggered automatically and loops through every entity involved—hosts, users, file hashes. It runs a lookup against threat intel sources like VirusTotal.
3. Workflow Step (Invoke Agent): The workflow passes this bundle of data to a specific "Triage Agent."
4. Agent Execution: The agent doesn't just copy-paste data. It reasons over the attack chain, compares it against the MITRE ATT&CK framework, correlates related logs, and generates a human-readable investigation summary tailored for a Tier 2 analyst.
5. Outcome: The workflow posts this AI-generated analysis directly into a new Case, complete with severity scoring, deep dive investigation, root cause analysis, and recommended next steps.

User Impact: The analyst starts their day reviewing a fully contextualized case, not chasing raw logs.

Use Case: The "Human-in-the-Loop" Investigation

Turning Natural Language into Deterministic Action

Once an analyst is investigating, they often need to perform administrative tasks that break their flow like finding out who is on-call, setting up war rooms, or notifying leadership.

In Elastic Security, the analyst stays in the chat interface. Because we allow you to define Workflows as Tools for your agents, the analyst can simply ask the agent to handle the logistics.

The Workflow:

1. Analyst Prompt: "We have a confirmed incident. Who is on call? Please create a Slack channel for this incident and invite them."
2. Agent Reasoning: The agent recognizes the intent matches a "Incident Response Setup" workflow tool you have pre-configured.
3. Workflow Execution:
   • Step 1: Queries the PagerDuty integration to find the on-call engineer.
   • Step 2: Calls the Slack API to create a channel named #incident-[id].
   • Step 3: Posts the initial case summary into that channel.
4. Outcome: The agent confirms to the analyst: "I have created channel #incident-982 and added Jane Doe (On-Call) to the channel."

Use Case: Guided Remediation and Containment

Precision Response at Speed

When it is time to contain a threat, speed is critical, but so is safety. You don't want an LLM "hallucinating" an API call to a firewall. This is where the Agent + Workflow combination shines for safety.

The Workflow:

1. Analyst Prompt: "Isolate the host involved in the BlackCat alert."
2. Agent Reasoning: The agent identifies the host123 host from the context of the investigation. It creates a plan to invoke the "Host Isolation" workflow.
3. Decision Point: The Agent presents the plan to the user: "I am about to trigger the 'Isolate Host' workflow for host123 via Elastic Defend."
4. Workflow Execution: The deterministic workflow executes the isolation command via Elastic Defend (XDR), ensuring the action is logged and performed exactly as defined by your engineering team.
5. Outcome: The host is isolated immediately.

User Impact: You get the ease of natural language interaction with the safety and audit trails of hard-coded automation.

We are moving away from a world where you have to choose between flexible AI chat and rigid SOAR playbooks. The future is an Autonomous SOC where the two are inextricably linked.

By using Agent Builder to create custom agents that understand your specific environment (using RAG with your own data) and equipping them with Elastic Workflows as tools, you effectively multiply your team's capacity and scale expertise. You are not just deploying a chatbot; you are deploying a virtual team member that knows your runbooks, respects your permissions, and works 24/7.

For more detailed information on getting started with Agent Builder read this blog.

Agent Builder and Workflows are available now as a tech preview. Get started with an Elastic Cloud Trial, and check out the documentation for Agent Builder here, and Workflows here.