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CloudThinker ships five specialized agents that analyze, report on, and act across your cloud environment. Each agent owns a domain, keeps persistent memory of your infrastructure, and coordinates with the others on cross-domain work.

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The agent roster

AgentRoleBest for
AlexCloud EngineerCost analysis, infrastructure optimization, multi-cloud architecture, reserved capacity
OliverSecurity EngineerCompliance audits, vulnerability assessment, IAM reviews, threat detection
TonyDatabase EngineerQuery optimization, performance tuning, index recommendations, capacity planning
KaiKubernetes EngineerCluster health, pod right-sizing, autoscaling, RBAC audits, troubleshooting
AnnaGeneral ManagerMulti-agent coordination, cross-domain projects, executive reporting
Want a different tone, restricted access, or a fully custom agent? See Customize agents.

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Choose the right agent

You needMention
Cloud costs are too high@alex
A security audit or compliance report@oliver
A slow database diagnosed@tony
Kubernetes cluster issues resolved@kai
A problem that spans multiple domains@anna
A starting point when you’re not sure@anna
Anna dispatches work to the other agents, so she is the safe default for incident investigations and cross-domain projects.

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Talk to agents

Talk to agents in plain language. The full pattern is: 
@agent #tool your request
  • @agent — who does the work: @alex (cloud), @oliver (security), @tony (database), @kai (Kubernetes), @anna (coordination).
  • #tool (optional) — the output you want: #dashboard, #report, #recommend, #alert, #chart, #kb.
  • Your request — what you need, in your own words. Add any context inline.
See the CloudThinker Language reference for the full syntax. 
@alex #dashboard build an EC2 cost dashboard for the last 30 days
@oliver #report audit security groups open to the internet on ports 22, 3306, and 5432
@anna #report run a quarterly infrastructure review across cost, security, and Kubernetes

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How agents work

  1. Read your prompt — the agent parses your mention, the optional tool tag, and your instruction.
  2. Query your connections — the agent reads the connections you’ve configured: AWS, Azure, GCP, Kubernetes clusters, databases, and more.
  3. Correlate data across sources — Alex cross-references CloudWatch utilization with Cost Explorer data; Tony reads execution plans alongside query frequency.
  4. Deliver the output you asked for — a dashboard, report, prioritized recommendations, or an alert.
  5. Act within its autonomy mode — in Manual mode the agent waits for your approval before executing changes. Every action is logged with an audit trail.

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Autonomy

Each agent runs in one of two modes, configurable per agent:
ModeWhat happens
ManualThe agent proposes the action and waits for a person to approve it before running.
AutoThe agent runs the action on its own and reports the result.
See Auto Mode for how Auto executions are scoped, guarded, and audited.

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Memory and context

Agents get more accurate over time because they retain what they learn about your environment:
Memory typeWhat it retains
EpisodicPast analyses and decisions, such as resources you’ve exempted from recommendations
WorkingContext within the current conversation thread
SemanticPatterns learned about your infrastructure over time
FileDocuments, runbooks, and knowledge base entries

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When agents activate

Agents become available as you add the relevant connections:
AgentAvailable when
AnnaAlways — no connections required
AlexAn AWS, Azure, or GCP connection is added
OliverAn AWS, Azure, or GCP connection is added
TonyA PostgreSQL, MySQL, or other database connection is added
KaiA Kubernetes cluster connection is added

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Next steps

Customize agents

Tune personality, attach connections, and build fully custom agents

CloudThinker Language

The complete @agent #tool instruction syntax reference

Set up connections

Connect cloud providers, databases, and Kubernetes clusters

Auto Mode

Understand how autonomous execution is scoped and audited