Every fleet task flows through a tight orchestration cycle — parallel execution, conflict resolution, and unified delivery at every step.
from ai_agents import Orchestrator, Fleet
# Define your specialised agent fleet
fleet = Fleet([
ResearchAgent(model="claude-opus"),
AnalysisAgent(tools=[database, charts]),
ReportAgent(output="markdown"),
])
# Orchestrate complex task across fleet
result = await Orchestrator(fleet).run(
goal="Quarterly supply chain risk report"
)The orchestrator automatically splits complex goals into parallel subtasks, routing each to the most capable specialist agent.
Agents run simultaneously across independent subtasks — dramatically reducing total wall-clock time versus sequential processing.
When agents produce contradictory outputs, the supervisor layer mediates, re-assigns, or escalates with full audit visibility.
Real-time dashboards show every agent's state, task queue depth, error rate, and latency — across your entire fleet at once.
Pre-built orchestration blueprints for the enterprise sectors where parallel agent fleets deliver the highest ROI.
A fleet of procurement, logistics and compliance agents coordinates in parallel — cutting order-to-delivery cycles and predicting disruptions before they escalate.
Run parallel KYC, AML, and regulatory checks across thousands of records simultaneously — with full audit trails and escalation routing baked in.
Deploy fleets of data-gathering, modelling, and reporting agents that assess exposure across portfolios in real-time — reducing manual analyst hours by over 80%.
Fleet API, Orchestrator SDK, MCP bus, and event webhooks — designed for enterprise-grade integration from day one.
Submit orchestration tasks via REST. The API handles fleet spawning, parallel dispatch, and result aggregation automatically.
Tell us your fleet requirements and we'll design the right orchestration blueprint for your enterprise.
Everything you need to know about multi-agent orchestration and how AIAgents powers enterprise-grade AI deployments.