CloudThinker takes your Datadog monitors, alerts, and metrics as input, then detects, analyzes, remediates, and verifies incidents — autonomously, under your team's policy. Brokered credentials, sandboxed execution, and a tamper-evident audit trail on every action. Datadog tells you what is wrong; CloudThinker closes it.
Datadog fires the monitor at 2am. A human still has to investigate, decide, and act.
Datadog is excellent at detection — dashboards, monitors, anomaly detection. But the signal is only the start of the work. Alert fatigue, ballooning MTTR, and tribal knowledge that walks out the door every rotation all live in the gap between the alert and the fix. That gap is still yours to close by hand.
CloudThinker runs a closed loop on every Datadog-triggered incident. Each pass is recorded, so the next incident of the same shape starts smarter than the last.
Clusters raw Datadog monitors and alerts into a single real incident — no more paging on noise.
Runs parallel root-cause investigation across Datadog logs, metrics, traces, and the dependency graph.
Executes the matching runbook inside a sandbox with brokered credentials — under your approval gate.
Confirms the Datadog signal cleared, closes the incident, and writes a tamper-evident receipt of what it did.
The agent starts read-only and earns scope per runbook — from L1 (observe and propose) to L4 (act autonomously within a guardrail). Engineers set the gate; the platform enforces it on every task.
Promote each runbook from notify, to act-with-approval, to autonomous — one at a time, as it earns trust.
Scoped credentials are issued per task and live in the sandbox — never in the prompt, never in the model.
Every action runs in an isolated environment, so a bad step can be contained and rolled back.
Sensitive data in Datadog signal is tokenized deterministically at egress — production PII never leaves in the clear.
Every detection, decision, and action is recorded in an append-only, tamper-evident log.
Humans review outcomes and tune guardrails instead of driving every keystroke of the response.
Because every resolved incident lands in agent memory, recurring Datadog alerts resolve faster each time — the loop learns.
De-duplication and correlation mean the agent absorbs the Datadog alert storm so your team only sees real incidents.
Detect-analyze-remediate-verify runs around the clock, so the 2am Datadog page becomes a morning summary to review.
Want the full mechanics? Read about the Deep Response Engine, the DARV loop, and graduated autonomy.
A Datadog AI agent is an autonomous software agent that acts on the signals Datadog produces — monitors, alerts, metrics, logs, and traces — instead of just surfacing them to a human. CloudThinker is that agent: it treats your Datadog signal as the input to the DARV loop (Detect, Analyze, Remediate, Verify), investigates root cause, executes the matching runbook, and verifies the fix, all under your team policy. Engineers stay on the loop rather than in the middle of every action.
CloudThinker ingests Datadog monitor and alert events, de-duplicates and correlates them into a single real incident, and triggers a parallel root-cause investigation across your logs, metrics, traces, and dependency graph. It then executes the matching runbook inside a sandbox with brokered, scoped credentials — under your approval gate — and writes a tamper-evident receipt of everything it did. Datadog tells you what is wrong; CloudThinker carries it through to a verified, reversible production change.
No. CloudThinker composes on top of Datadog rather than replacing it. Datadog stays your observability and alerting layer; its signal becomes the input the CloudThinker agent reasons over. You keep your dashboards, monitors, and SLOs, and add an autonomous action layer that closes the loop from alert to verified fix.
CloudThinker is built so autonomous action stays safe. Every task runs under graduated autonomy (L1–L4) — the agent starts read-only and earns broader scope per runbook. Credentials are brokered per task and never handed to the model; execution is sandboxed; sensitive data is tokenized deterministically at egress; and every action lands in a tamper-evident audit log. Engineers set the approval gate for each environment and stay on the loop.
DARV is the four-stage loop CloudThinker runs on every Datadog-triggered incident: Detect (cluster raw Datadog signal into a real incident), Analyze (parallel root-cause investigation), Remediate (execute the matching runbook inside a sandbox under policy), and Verify (confirm the fix held and the incident is closed). Because every loop is recorded, the next incident of the same shape starts smarter than the last.
CloudThinker ingests signal from common observability and alerting stacks — Datadog, Prometheus, Grafana, Splunk, ELK, PagerDuty, Opsgenie — and connects to your cloud and Kubernetes environments through brokered, scoped connections. The Datadog signal layer becomes one input among many; the agent reasons over all of them to close incidents end to end.
Connect CloudThinker to Datadog and let it detect, analyze, remediate, and verify — under your policy, with a full audit trail. Start a trial or book a demo.