Seven AI Agents. One Coordinated Workforce System.
AI that works across the entire workforce system — not just within it.
Most workforce management platforms have added AI as a feature layer on top of existing tools. Cisne was designed differently. AI is not a module or an add-on — it is built into the architecture through a coordinated layer of intelligent agents that operate continuously across the entire workforce planning lifecycle.
WHY LEGACY WFM AI FALLS SHORT
AI added on top is not the same as AI built in
Legacy workforce management platforms were built on rules-based engines. When AI capabilities were added, they were layered on top — attached to individual features rather than embedded in the underlying architecture.
The result is AI that operates in silos. A forecasting AI that doesn't inform the scheduling layer. An intraday monitor that doesn't feed back into the forecast. Each tool sees part of the picture, but no single layer coordinates across all of them.
WHEN AI OPERATES IN SILOS
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Insights from one layer don't inform decisions in another
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Teams manually connect the dots between systems
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Operational context is lost between planning stages
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AI recommendations arrive too late to influence planning decisions
THE CISNE AGENT ARCHITECTURE
AI agents that share context across the entire planning lifecycle
Shared operational context
All agents operate on the same live data layer — no disconnected models or isolated feature modules
What makes Cisne different is not that it has AI agents — it is that those agents share the same operational data layer, reason about the same planning context, and coordinate across lifecycle stages continuously. This is AI coordination, not just AI assistance.
Cross-lifecycle coordination
Insights from forecasting flow into scheduling. Intraday deviations inform future forecasts. The system learns continuously.
Continuous operation
Agents run continuously, not on demand — monitoring, learning, and surfacing insights across every planning stage.
SEVEN AGENTS. ONE SYSTEM.
Each agent specialized. All coordinated.
Cisne includes seven AI agents operating across the workforce planning lifecycle. Each specializes in a specific domain — and all share the same operational context, so insights in one area automatically inform decisions in others.
Forecasting Agent
Forecast monitoring and improvement
Monitors forecast accuracy and identifies emerging demand patterns, recommending adjustments when conditions shift.
Feeds learning directly into the scheduling and intraday layers.
Scheduling Agent
Schedule optimization and adjustment
Evaluates staffing plans against forecast updates and recommends schedule adjustments when demand or availability changes.
Operates on forecasting agent output and surfaces gaps to the intraday layer.
Intraday Operations Agent
Real-time operational monitoring
Monitors service levels and operational conditions continuously, surfacing risks before service levels degrade.
Feeds real-time deviations back into forecasting and scheduling cycles.
Real-Time Adherence Agent
Agent activity and schedule adherence
Tracks agent adherence to scheduled activities and surfaces deviations to supervisors in real time.
Shares adherence signals with the intraday and scheduling agents.
Reporting and Insights Agent
Operational reporting and analysis
Generates recurring reports automatically and surfaces trends across forecasting, staffing, and performance.
Draws on all other agent outputs to provide system-wide operational visibility.
Data Import Agent
Automated data ingestion and validation
Monitors incoming operational data feeds, validates inputs, and ensures forecasting models operate on accurate data.
Provides the clean data foundation all other agents rely on.
Integration and API Agent
System integration and connectivity
Monitors integration health across connected systems and validates data exchanges between Cisne and external platforms.
Keeps the data layer that all agents share accurate and continuously updated.
HOW AGENT COORDINATION WORKS
AI that operates continuously, not on demand
Cisne's agents don't wait to be triggered. They monitor, reason, surface, and coordinate continuously — across every stage of the workforce planning lifecycle.
AI coordination, not just AI assistance. The system learns from every cycle.
AGENTIC INTELLIGENCE IN THE COMMAND CENTER
Identify events that will affect demand before the volume spike occurs
Most intraday systems rely entirely on internal operational data — queue volumes, service levels, and agent status. These indicators only appear once demand has already begun to shift.
Cisne expands intraday visibility by incorporating both internal operational signals and external data sources, detecting events that are likely to influence demand before those interactions reach the contact center.
CISNE MONITORS
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Internal operational signals from contact center and workforce systems
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External indicators that may influence customer behavior
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Early detection of events that may trigger demand spikes
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Operational alerts before the impact reaches queue volume
OPERATIONAL OUTCOMES
What coordinated AI delivers across the workforce system
When AI operates across the full lifecycle rather than within isolated features, the operational benefits compound across every planning stage.
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Reduced manual administrative work for WFM teams
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Faster identification of operational issues across all planning stages
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Continuous monitoring without constant dashboard review
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More time available for strategic workforce planning
AGENTIC AI
AI that works across the entire workforce system — not just within it.
Cisne's coordinated AI layer helps workforce teams manage operational complexity without increasing administrative overhead. Seven specialized agents, one shared intelligence layer, operating continuously across every stage of workforce planning.