Agentic Workflows in the Enterprise
As the level of interest in building agentic workflows in the enterprise increases, there is a corresponding development in the “AI Stack” that enables agentic deployments at scale. Like any new technology shift, there is a need to build out the various parts of the technology stack – from chips, to infrastructure to orchestration. We’ve seen Microsoft organize a new division – Core AI – to respond to this, while Google outlined a comprehensive vision for its AI stack at its recent Cloud Next 2025 event.
A key part of the stack is enabling systems to talk together. This is reminiscent of the enterprise application integration (EAI) market in the early 2000s. I worked at BEA Systems and saw the development of “integration middleware” with BEA’s WebLogic, IBM’s Websphere, Microsoft’s BizTalk Server, and Oracle’s Fusion middleware among others.
For AI though, it’s not enough just to be able to connect and pass data back and forth. Agents need to make that data work intelligently which is part of the agent orchestration layer we see being developed.
Industry momentum: standardizing collaboration
Within the AI stack, multi-agent systems (MAS) bring together specialized agents, each focusing on a particular task and working towards a shared goal. A couple of recent protocol developments are accelerating MAS adoption:
- Anthropic’s Model Context Protocol (MCP): A universal connector for AI systems that standardizes how agents access data sources and external tools—critical for enterprises managing mixed-vendor environments.
- Google’s Agent-to-Agent (A2A) Protocol: Enables cross-platform agent collaboration by allowing specialized agents from different vendors to share insights and coordinate tasks.
These protocols reduce integration friction while maintaining security—key for enterprises scaling MAS beyond pilot phases. Recently the leading model providers including OpenAI and Google have opted in to the Model Context Protocol, signaling this as a new industry standard.
Orchestration frameworks: coordinated intelligence
Orchestration frameworks are the control centers of agentic systems – and they rely on collaboration protocols. They manage workflows, delegate tasks, and ensure agents operate as a cohesive unit.
| Function | Description | Example |
| Task delegation | Assigns tasks based on capability and context. | Escalation agents handle complex support tickets requiring human-like reasoning skills. |
| Process prioritization | Dynamically schedules tasks based on urgency or value. | Urgent IT tickets prioritized over routine maintenance tasks in enterprise systems. |
| Communication management | Ensures agents share data reliably across workflows. | Logistics agents coordinate inventory updates with delivery schedules in real time. |
| Workflow state tracking | Monitors process progress, enabling pause/resume functionality. | QA agent resumes review from saved software development checkpoints after interruptions. |
Why it matters
- Efficiency: Avoids duplication of effort across workflows.
- Adaptability: Adjusts workflows based on real-time conditions.
- Scalability: Supports increasing agent complexity without central bottlenecks.
Agentic AI systems promise intelligence, but intelligence alone isn’t enough. These systems must be trusted, cohesive, and ready for real-world demands.
The build-out and hardening of the AI stack removes many of the technical barriers – the barriers become organizational. System integration is a key enabler, and along with advances in reference architectures, security, monitoring, model farms, developer tools, and governance, will provide a solid technical foundation for making agents properly work together and deliver the promised business benefits.
Of course, to deliver those benefits, each component of the system, as well as the linkages between those components, must function seamlessly. Rigorous testing becomes even more critical as systems become more deeply integrated and complex.
Conclusion: From infrastructure to impact
The evolution of agentic AI in the enterprise is no longer about proving potential — it’s about engineering systems that scale, interoperate, and deliver. With robust orchestration frameworks, emerging collaboration protocols, and increasing vendor alignment, the technical foundation for agentic workflows is rapidly solidifying.
What comes next is not just a matter of stack maturity, but of enterprise readiness. The next frontier is organizational: aligning teams, processes, and governance models to fully harness agentic capabilities. Enterprises that move now — investing in the right infrastructure while preparing for operational integration — will be best positioned to lead in this new era of AI-powered collaboration.
Agentic systems are ready. The question is: is your enterprise?
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