Once you know what you want an AI agent to do, building it is the easy part. The part that gets glossed over is the prep work that sets AI agents up for success.

Creating an effective agent is all in the prep work of the team mapping their workflows in enough detail to see where repetitive, well-bounded, high-volume work lives. This mapping can take time and is often overlooked when the team is too busy with their day-to-day activities. Without this effort security teams tend to aim either too large (“AI needs to detect zero-days”), or too small (“AI can check whether this IP is on our blocklist”) when trying to build an agent. An effective AI agent starts with a well-scoped task: narrow enough to execute reliably, but consequential enough to justify the investment.

This is a stakeholder-alignment problem, not a technical one

Mapping the work looks a lot like a data-governance project before it looks like an AI project. Anyone who has run a data-classification or access-review initiative knows the pattern: the hard part isn’t the tooling; it’s getting the people who do the work in a room and agree on what that work really is. An analyst’s mental model of their own job and the actual repeated pattern in their ticket queue are often two different things, and the gap between them is where the automatable work is hiding.

That means the right first step toward AI adoption isn’t a proof of concept, it’s a whiteboard session. Walk through the workflow step by step with the people who own it, and the well-bounded, repeatable pieces surface on their own. Skip that step and go straight to building, and you end up automating the task someone happened to mention in a hallway conversation, not the task that moves the needle.

What well-bounded work looks like

Two examples from SRA’s own SCALR AI deployments, that I’ve watched play out directly, make the “too big, too small, or just right” distinction concrete.

At a non-profit medical center with over $1 billion in revenue and 15,000 employees, the security team was responsible for monitoring for expired Microsoft Entra application registration keys, and finding which applications were at risk and tracking down the owner. This was a fully manual process eating 20 hours of staff time a week. That’s the kind of task that looks too small to bother with until you see the hours attached to it, and too unstructured to hand off until someone maps the actual steps. Once mapped, it turned out to be well-bounded: identify registrations expiring within 30 days, check authentication logs to see if the application was still in use, and either flag it for discontinuation or register new keys with the owner. SCALR AI took on that workflow directly, and in the process discovered that 70% of the flagged applications weren’t even in use anymore.

In another example, analysts were stuck manually triaging every security alert with no existing way to cut down on the false positives before the alert reached a human. That’s a task that sounds like the “detect zero-days” trap, until it’s broken into what it truly is: analyze endpoint activity continuously, flag genuine anomalies, and produce a documented account of how the system reached its verdict. SCALR AI took on that triage workflow and cut MTTA and mean time to triage down to 2 and 5 minutes respectively; while producing a full report on every investigation so the analyst reviewing it could see exactly how SCALR AI got there.

Neither of these started as an obvious AI use case. They started as workflows somebody had to sit down and map before an agent could touch them.

Process beats technology

The teams that get the most out of AI aren’t the ones with the biggest model or the most agents running. They’re the ones who did the important prep work of mapping their operations enough to know where an agent belongs and where it doesn’t.

This mapping is what SRA’s AI workshops are built for. SCALR AI is free on the Azure Marketplace, so there’s no cost barrier to getting the platform running. The conversation worth having is what to focus on before the first agent gets built.