Recent HFS research highlights the critical importance of orchestration platforms in the evolving AI landscape, particularly for enterprises seeking to implement agentic AI solutions in a controlled, scalable manner.
The Rise of Orchestration Models
In 2025, HFS Research identified that while many enterprises have been “tinkering” with AI technologies for nearly two years, a more compelling “orchestrator” model is quickly evolving. This model provides centralized control, faster ROI, and strategic agility through robust platforms like ServiceNow that act as the command-and-control plane for agentic workflows Orchestrators win: Why tinkering with AI no longer cuts it for enterprises (2025).
The orchestrator model is particularly valuable because it:
- Provides centralized control of autonomous agents
- Embeds governance, observability, and trust into agentic infrastructure
- Manages the “master control program” layer of agentic interfaces with external systems
- Uses role-based controls already part of enterprise systems
System-Agnostic Orchestration Platforms
Cognizant’s approach to multi-agent orchestration demonstrates how system-agnostic platforms are evolving. Their Neuro AI Multi-Agent Accelerator directly addresses enterprise orchestration challenges through:
- Pre-built multi-agent networks for various enterprise functions
- Natural language customization for quick adaptation
- An orchestration framework that facilitates seamless communication between agents
- Multi-vendor integration supporting various LLM and cloud providers Cognizant breaks through enterprise agentic orchestration barrier (2025)
This system-agnostic approach allows organizations to optimize costs and performance without being locked into specific vendors or technologies.
Simulation-First Adoption Path
HFS research emphasizes the importance of a structured, simulation-first approach when implementing agentic AI:
- Launch an agentic use-case discovery workshop: Create a clear, prioritized pipeline
- Run a 4-6 weeks pilot for a high-friction process: Develop proof of value and trust model
- Implement your agentic governance layer: Establish compliance and accountability
- Integrate agentic into actual workflows: Drive adoption and workflow continuity
- Operationalize with scalable IP and service models: Build sustainable, industrialized impact Make the case for agentic AI in your enterprise (2025)
This simulation-first approach allows organizations to prove value in synthetic environments before expanding to live multi-agent operations.
Multi-Agent Operations
Orchestration platforms are increasingly focused on enabling collaborative multi-agent systems that can work together across enterprise functions:
- Lyzr enables multi-agent systems that collaborate, share intelligence, and automate complex workflows across departments, setting the foundation for what they call “Organizational General Intelligence” HFS OneOffice Hot Tech: Lyzr (2025)
- AI-driven red teaming can now simulate threat actors across the entire attack chain, exposing unseen vulnerabilities and mirroring how attackers evolve, making testing environments more dynamic and realistic Human-only cyber defense is dead—AI is now the command layer (2025)
Enterprise Readiness Challenges
Despite the promise, enterprises face significant challenges in implementing orchestration platforms:
A recent HFS OneCouncil session revealed that while enterprise leaders are keenly interested in agentic AI, they struggle with governance, infrastructure readiness, and talent strategy. They specifically called for:
- Standardized terminology and frameworks
- Open ecosystems to avoid proprietary lock-ins
- Support for HR and workforce planning
- Evidence of real-world deployments rather than aspirational roadmaps Enterprises are wide awake on agentic AI—but need help navigating the chaos (2025)
The Path Forward
For enterprises looking to implement orchestration platforms with a simulation-first approach, HFS recommends:
- Build for orchestration, not coordination: Re-architect workflows and roles to support real-time agent interaction
- Stabilize the core: Clean up fragmented data and modernize systems to create the foundation AI needs to scale
- Define value and align delivery: Move beyond traditional pricing models to outcome-based metrics
- Govern with intent: Build oversight models that supervise AI behavior dynamically Agentic AI is redefining services—now, enterprises must redesign themselves (2025)
The organizations that succeed will treat agentic AI not as a bolt-on technology but as a re-architecture of business and technology foundations that collapses workflows, rewrites accountability, and demands a shift from executing tasks to orchestrating outcomes.