agentrangeJoin waitlistEvaluate your enterprise security agents on specific tasks or wholistic end-to-end workflows. Prove their safety, effectiveness, and reliability bleeding edge benchmarks.
A pentest agent that surfaces a critical vulnerability before a real adversary can is a huge win. The same agent, slightly off the rails, can drop a production database, corrupt customer data, or pivot beyond the scope it was authorized to touch.
On the defensive side, a SOC agent that autonomously contains a live intrusion is invaluable. The same agent, acting on a false positive, can quarantine production servers and take customer-facing applications offline.
AgentRange helps you mitigate these risks by measuring the three things that matter when a security agent acts autonomously — safety, effectiveness, and reliability — so you can prove your agents perform in real environments before you trust them with your production systems.
the specific tasks and workflows to evaluate your agent on
Author your own evals, or pick from our library of pre-built tasks and the metrics that grade them.
your agent inside a real enterprise environment
Each task runs across many trials, each inside its own real environment. Every action your agent takes is recorded for later replay and analysis.
how well your agent performed
See how your agent performed across every trial for every task you defined — graded on the three pillars of enterprise security agents: safety, effectiveness, and reliability.
Designed in partnership with leading offensive security experts, our benchmarks measure an agent’s safety and efficacy on production enterprise environments with complex network topologies and attack paths. Continuously updated and refreshed, our benchmarks avoid the problem of new agents and models being trained to the test.
Every scenario ships with explicit rules of engagement — what is in scope, what counts as destructive, what data may not be touched. Each action the agent takes is recorded against those rules. Agents that reach the objective but violate the RoE fail the trial.
Each scenario has a defined rubric — objectives reached, privilege boundaries crossed, vulnerabilities correctly identified and reported. Scoring is mechanical against actual environment state, so an agent’s score reflects what really happened, not how convincing the writeup sounds.
Every scenario runs many times against re-rolled, version-pinned environments. We report variance alongside the headline number, so agents have to hit the bar consistently — not just on a lucky single shot — and results stay comparable across model upgrades and runs months apart.
Built in collaboration with detection and response experts from top security firms, our benchmarks measure an agent’s accuracy and safety on production SOC environments. Our SOC agent benchmarks include densly populated SIEMs with realistic alert noise and false positive rates, plus ground-truth labels for the attacks behind every alert.
Each scenario defines what the agent is permitted to do on its own — what it can isolate, block, or escalate, and what requires a human in the loop. Response actions outside those bounds fail the trial, even when the call would have been correct.
Each SIEM is populated with months of ground-truth attack and benign activity. Agents are scored on detection precision, recall, and false-positive rate against that label set — what they caught, what they missed, and what they cried wolf about.
Each agent runs through many variants of the same attack — different timings, slightly different IoCs, varying levels of background noise. We surface where detection holds and where it cracks, so brittle pattern-matching gets caught here, not in production.
Agents that handle recon, exploitation, lateral movement, and exfiltration. Built by offensive security teams and red-team-as-a-service vendors.
Real Active Directory domains, AWS estates, and applications with real bugs, with each trial fully isolated. Every run is reproducible, so two prompts or two model versions can be compared on the exact same starting state.
Agents that triage alerts, hunt threats, and author detections. Built by SOC engineering teams and security platform startups.
Populated SIEMs with the alert noise and false positive rates of a real production SOC, plus ground-truth labels for the attacks behind every alert. Eval results that predict how the agent performs at 3am.
We are working with a small group of design partners this fall. Tell us what you are building and we will be in touch.