Infrastructure integrations give Neotask agents eyes and hands on the systems that keep an application running — compute, orchestration, databases, storage, and the alerting that tells you when something breaks. This is the category where an agent stops being a chatbot and starts being an on-call teammate: it can read a Kubernetes cluster's pod status, correlate a spike in errors with a recent Terraform apply, and page the right person through PagerDuty before a customer even notices. Connected to Prometheus, the same agent can pull real metrics — request latency, memory pressure, queue depth — and answer "why is checkout slow right now" with an actual graph-backed diagnosis instead of a guess pulled from a stale runbook. Provisioning and deployment tools are the other half of the story. Terraform integration lets an agent read the current state of infrastructure-as-code, flag drift between what's declared and what's actually running, or draft a plan for a requested change so a human can review the diff before anything is applied — infrastructure changes are exactly the kind of irreversible action where an agent should propose, not execute, unsupervised. Vercel connects the same reasoning to the application layer: an agent can watch deploy status, correlate a failed build with the commit that caused it, or roll a conversation about "the site is down" straight into checking whether the last deploy actually succeeded. Data-layer integrations — Postgres and Redis — let an agent inspect schema, run read-only diagnostic queries, or check whether a cache is actually being hit before assuming a performance problem is a database issue. This turns "the app feels slow" from a guessing game into a five-minute investigation: check Redis hit rate, check Postgres query time, check Prometheus for where the time is actually going. Storage integration via S3 rounds out the operational picture, letting an agent verify that backups landed, that a bucket policy hasn't drifted open, or that a scheduled export actually ran. What makes this category distinct from the others is the blast radius of a mistake: a bad database write or a deleted infrastructure resource can take down production, so these integrations are read-heavy by default — an agent observes, correlates, and recommends, and any state-changing action (a Terraform apply, a database migration, a scale-down) goes through an explicit human approval step. The payoff is an agent that actually understands your running system well enough to be useful during an incident, not just during a planning meeting.
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