One system. Three layers. Five specialist agents.
Cloud telemetry flows in, gets reasoned over by specialist agents sharing one knowledge base, and comes out the other side as either a proposal or — once policy and a human allow it — a safely executed change.
How data becomes a decision.
Three layers, one continuous loop: observe, reason, act.
Every recommendation is traceable to a source. Every high-impact action passes through the same approval path.
That's the design principle behind the whole platform — trusted by the engineers who have to answer for what it does, not just what it flags.
Two agents operational. Proven architecture. Measurable quality.
Not slides, not wireframes — working code. 334 tests pass with zero failures. 31 MCP tools exposed. Architecture scored 80/100 against PROOF, Microsoft multi-agent, and Gartner production-readiness standards.
Five specialists. One brain.
Each agent owns a domain an SME would otherwise need a dedicated hire for. All of them read from the same knowledge base, so a cost decision knows about an open vulnerability, and an incident knows what changed in the last deploy.
Cost & rightsizing
255 tests · 23 MCP tools · 16 detectors- Detects idle compute, orphaned resources, GPU waste, data-transfer egress
- 23% avg GPU utilization — flags AI workloads bleeding cash
- Terraform sync pipeline: discover 28 resource types (AWS 14 + Azure 7 + GCP 7) → import → drift detect → human-gated approval. Provider threaded end-to-end — Azure/GCP run the same full loop as AWS.
- Existing-IaC mode adds plan-based attribute drift: terraform plan in your own workspace catches changes made outside TF
- Multi-sub-agent architecture: Scanner → Terraform → Remediation → Reporter
- Terraform apply NEVER auto-executed — all mutations through human approval queue
Health & incidents
79 tests · 8 MCP tools · 16 EKS/AKS tools- 16 ReadTool/ActionTool implementations for AWS EKS and Azure AKS
- Z-score anomaly detection with 6 pattern-matched root causes (OOM, CrashLoop, etc.)
- Deployment validation, RCA with confidence scores, runbook-driven remediation
- Policy engine: environment allowlisting + per-resource rate limiting
Posture & vulnerabilities
Checks the fence line, all the time.- Checks posture against CIS, SOC 2, ISO 27001, NIST
- Detects vulnerabilities and misconfigurations
- Monitors IAM risk across accounts and roles
- Prioritizes remediation by actual exposure
Audit & evidence
Architecture ready — shares SecOps findings.- Continuously maps evidence to compliance controls
- Cuts the manual effort auditors used to demand
- Shares findings directly with the SecOps agent
- Turns audit season into a non-event
Natural language over your whole estate
The interface to everything the other four know.It learns your architecture and operational history, so anyone on the team can ask a plain-language question and get a grounded answer — not a dashboard to go interpret themselves.
96% of enterprises deploy agents. Only 12% can govern them.
OutSystems surveyed 1,900 IT leaders in 2026. PwC reports 78% plan to increase agent autonomy — but only 21% have governance models ready. CloudSentri's governance is the competitive moat.
Built on principles the best teams converge on.
The 2026 State of AI Agents industry report confirms: the dominant production pattern is single tool-use with human review — exactly CloudSentri's propose→policy-check→approve→generate pipeline.