Introduction to AI Orchestrators
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Introduction
Step-by-step guide
1. Understanding AI orchestration
2. Capabilities
3. Explore orchestrator environments
4. Compare orchestrator environments
2. Capabilities
The pages in this chapter are scheduled for an update.
Understanding orchestrator capabilities
Subtopics (10)
Workflows and automation
Subtopics (7)
Branching and routing
Dependencies between steps
Event triggers
Parallel execution
Task planning
Retries and failure handling
Scheduled runs
Agents and delegation
Subtopics (4)
Agent-to-agent communication
Agent configuration
Task delegation
Model selection
Context, state, and recovery
Subtopics (5)
Context management
Durable execution
Memory
Sessions and resuming work
Shared state
Tools and external systems
Subtopics (5)
API and CLI control
Extensions
Knowledge retrieval
MCP connections
Tool calling
Execution and hosting
Subtopics (5)
Always-on operation
Deployment and hosting
Execution isolation
Remote execution
Scaling
Human oversight and collaboration
Subtopics (5)
User approvals
Work handoffs
Intervening during a run
Reviewing results
Team collaboration
Permissions and data protection
Subtopics (5)
Audit logs
Data handling policies
Guardrails
Execution permissions
Credential management
Tracing and quality evaluation
Subtopics (3)
Debugging and reproducing issues
Quality evaluation
Execution tracing
Cost and resource management
Subtopics (4)
Budget limits
Balancing cost and quality
Cost allocation
Usage measurement
Domain workflows
Subtopics (4)
Business processes
Coding workflows
Git-based review and delivery
Research workflows
From capabilities to environment groups