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Why This Taxonomy Exists

“Cloud agents” is a term that gets used to describe everything from cron jobs to IDE copilots. That ambiguity causes teams to:
  • choose the wrong abstraction
  • automate too early
  • or avoid cloud agents entirely because they feel risky
This guide exists to:
Define what cloud agents actually are, how they differ from other agents, and when they make sense for software teams.

A Clear Definition of Cloud Agents

A cloud agent is an AI-driven process that runs on remote infrastructure, is triggered by tasks, schedules, or external events, and uses reasoning over changing inputs to produce reviewable outcomes across shared engineering systems.
Key characteristics:
  • runs outside a developer’s local environment
  • operates on shared team context (repos, alerts, analytics, infra)
  • produces reviewable outcomes (PRs, reports, actions)
  • can run asynchronously and repeatedly
Cloud agents exist to handle work that benefits from reasoning, shared context, and reviewability.

What Cloud Agents Are Often Confused With

Cloud agents are often confused with other tools.

Not IDE Agents

IDE agents focus on individual productivity and local context. Cloud agents operate on shared systems and team-owned work.

Not Just Cron Jobs

Cron is a trigger. Cloud agents reason over changing inputs and produce reviewable outcomes.

Not One-Off Scripts

Scripts execute fixed logic. Cloud agents apply reasoning and shared context even for a single run.

Not Magic Automation

Cloud agents still require review, ownership, and guardrails.

The Two Axes That Matter

Cloud agents are best understood along two dimensions:
  1. What triggers them
  2. How they’re governed
Everything else is implementation detail. Most teams start bottom-left and move diagonally as trust increases to create their Continuous AI Workflow.

Axis 1: Cloud Agent Trigger Type

1. One-Off Cloud Agents

Manually triggered when a human explicitly dispatches work to an agent. One-off cloud agents are first-class agents that differ by trigger, not by capability.

Examples

  • Analyze the top recurring Sentry errors
  • Audit a repo for security issues
  • Summarize system behavior

Best for

  • Unknown scope
  • Exploratory work
  • Building confidence before automation

2. Scheduled Cloud Agents

Time-based triggers that run on a fixed cadence (daily, weekly, hourly).

Examples

  • Weekly vulnerability scans
  • Nightly analytics summaries
  • Regular dependency updates

Best for

  • Predictable recurring work
  • Backlog prevention
  • Drift detection

3. Event-Driven Cloud Agents

Signal-based triggers that react to real-time events from external systems.

Examples

  • New high-severity alert
  • Failed deployment
  • Critical security finding

Best for

  • Operational workflows
  • Incident response
  • Time-sensitive fixes

Axis 2: Cloud Agent Governance Level

This is where most teams get into trouble.
  • Human decides when the agent runs
  • Human reviews everything
Safest starting point.
Cloud agents don’t fail because they’re autonomous. They fail because teams skip the control phase.

Cloud Agents vs Local / IDE Agents

Local / IDE AgentsCloud Agents
Individual contextTeam context
Short-livedLong-running
Developer-initiatedSystem-initiated
Productivity focusReliability & ownership focus
Both are useful. They solve different problems.

Why Cloud Agents Exist at All

Most engineering pain is recurrence.
  • alerts repeat
  • security issues resurface
  • operational work interrupts feature work
Cloud agent workflows exist to:
  • notice repetition
  • respond consistently
  • reduce future interruptions
The goal is not more automation. The goal is less work over time.

When Cloud Agents Make Sense

Use cloud agents when:
  • the same class of issue keeps returning
  • the cost is in handling, not solving
  • work spans multiple systems
  • review and traceability matter
Avoid them when:
  • the problem is novel
  • ownership is unclear
  • blast radius isn’t understood yet

The Architectural Reality

As teams adopt cloud agents, they discover:
Agents alone don’t scale without control and visibility.
Production cloud agents eventually require:
  • a shared place to review runs
  • a record of decisions
  • adjustable prompts and rules
  • gradual automation
Without this, cloud agents become brittle fast.

Where to Go Next


One Sentence to Remember

Cloud agents are how teams take ownership of work that happens after code ships.