AWS describes Tami as ingesting findings from CNAPP and cloud-native sources, enriching them with asset and exposure context, and prioritizing them before specialized agents handle remediation stages.
First implication: Agentic security remediation should be evaluated by safely closed findings and production impact, not by the number of alerts an agent can process.
Thesis
Safe remediation depends on separating investigation, confidence assessment, and execution behind governance controls.
Observed facts
Mechanism
The system gathers context and plans remediation before live changes, then applies guardrails and governance where customer cloud changes are involved.
Why now
Cloud scanners can generate more findings than teams can manually investigate, while production-risk concerns keep fixes from being automated.
Implications
Agentic security remediation should be evaluated by safely closed findings and production impact, not by the number of alerts an agent can process.
Evidence boundary
This is an AWS-published vendor case study; the supplied source excerpt does not provide independent validation or detailed definitions and measurement methods for the reported outcome.
Watch conditions
Whether Tamnoon or a customer publishes independently inspectable methodology, baseline data, and repeatable results across environments.