Can your AI agent(s) complete tasks autonomously and when prompted?

True agentic AI takes action rather than just giving information. Look for role-based agents that autonomously handle jobs (not just tasks)—like investigating alerts and containing the threat—from start to finish without requiring constant manual intervention.

Are your agents task-based or objective and persona-based?

Avoid solutions with separate, siloed agents dedicated to specific tasks (e.g., executing a playbook or alert triage) that don’t communicate with each other or work together. Instead, look for solutions with agentic systems that are built to handle complete roles, like detection engineering or threat hunting. These persona based agents can collaborate to extend your team’s impact.

Can AI agents orchestrate actions using the security technologies I already have?

Integration is critical. Ensure the AI can execute actions across your existing tech stack—like EDRs, firewalls, or email gateways—rather than just handing you a list of tasks.

Can your AI agent(s) retain knowledge across multiple workflow steps?

Agentic systems should “remember” what they’ve seen and done. Ask if the agents can maintain context across a detection, investigation, and response flow without resetting their state. Also ask if you can add new memories to the agent for tailored context.

What skills and tools do your AI agents have access to?

Push for specifics. The more robust tools and skills available to an agent, the better it is at handling the job. For example, if an agent is designed to triage alerts, it may need access to the latest threat intel or the ability to create a sandbox, or skills like decoding scripts or analyzing command lines.

Does the system support multi-agent collaboration and knowledge-sharing between agents?

The real power of agentic AI comes when agents work together—like detection agents handing off to investigation agents sharing knowledge the same way detection engineers and threat intelligence analysts should. Avoid systems claiming to be "multi-agent" but only have siloed bots that work independently. Ideally, a provider should have an agent dedicated to each SecOps role.

Can the AI learn and adapt from human feedback?

Real agentic AI adapts. Ask if analysts can provide feedback that tunes agent behavior over time without code or retraining.

Is your agentic AI system tailored to my environment? If so, how?

Your business is unique, and so are its challenges. Agentic AI should adapt to your specific processes and priorities, leveraging a deep understanding of your environment and guidance from incident patterns, workflows, and threat intelligence to provide actionable, personalized solutions.

What is the feedback loop between your frontline security teams (e.g., threat research, hunting, incident response) and the ongoing improvement of the AI?

The threat landscape changes daily. An AI's expertise becomes stale unless it is constantly enriched with fresh insights from active security operations. A direct feedback loop from frontline teams ensures the AI’s knowledge remains current, relevant, and ahead of adversaries.

Is your AI trained on individual customer data?

Training on customer data creates compliance and privacy risks. More importantly, AI reasoning should be informed by patterns across thousands of enterprises and hundreds of technologies—not individual datasets. Operational history and diversity are what enable accurate reasoning in real production environments.

Can the AI agents reason through decisions and take different actions based on changing context or errors?

Unlike basic automation, agentic AI isn’t linear. Truly autonomous AI evaluates changing conditions like asset value, threat severity, or alert correlations and adapts its response. If unexpected values appear, agents should replan around it.

Does the AI leverage proprietary, first-party threat intelligence in addition to public feeds?

Effective platforms augment open-source feeds with proprietary intelligence from your environment and original threat research. That combination is what turns data into better decisions—and reduces noise that slows response.

Are you locked into a single large language model, or do you operate in a multi-model/model-agnostic architecture?

No single model excels at everything. Ask whether the platform can evaluate outputs across multiple models and shift to better-performing configurations as needs change.

How quickly can the platform adapt to changes in your environment or business structure?

Change is constant: new tools, new subsidiaries, new regions, new identities. Look for an architecture that absorbs those shifts without rebuilding from scratch while keeping decisions consistent and governed.

Does your validation approach use multiple layers working together, or do you use a single validation approach?

Layered validation—where multiple methods work together—catches errors that single approaches could miss. Single-method approaches can’t reliably detect all types of failures.

How transparent are the AI’s actions and reasoning?

Avoid black-box solutions. You should have full visibility into the AI’s decisions— what actions it takes, why it takes them, and the data driving those choices. Ideally, this should be accessible through a user-friendly interface.

What guardrails and human-review mechanisms are in place for high-impact decisions?

Guardrails determine whether autonomy is usable. Ask how actions are constrained through approval boundaries, policy rules, and safety logic, so you can scale response speed without adding new operational risk.

What formal testing and validation measures are in place to ensure safe and reliable execution?

Agentic platforms must have an AI validation lifecycle—combining golden dataset testing, continuous customer feedback, secondary AI evaluation, and human expert oversight to ensure reliable, safe, and accurate performance at scale.

How do you manage model changes, versioning, and drift—and communicate that to customers?

Ask what happens after an update. Strong vendors monitor outputs continuously and can explain how changes are validated, approved, and governed, so performance stays consistent and predictable.

How do you handle sensitive data and compliance requirements?

Ask how data is encrypted, who has access, and whether the system uses your data solely for your environment. Verify compliance with SOC 2, ISO/IEC 27001:2022, PCI DSS v4, and HIPAA regulations as needed.