In today's enterprise landscape, a simple business request rarely follows a straight line. A purchase requisition might evolve into a multi-threaded process involving data enrichment, supplier onboarding, contract generation, and layered approvals. Traditionally, these workflows are defined by static sequences and hard-coded logic—rigid, error-prone, and requiring manual oversight. However, a new paradigm is emerging: agentic workflows, where intelligent software agents collaborate to fulfill complex business objectives autonomously.
From Static Steps to Agentic Flows
Agentic workflows break down a process into modular, role-driven responsibilities—each handled by a specialized software agent. These agents aren't just glorified automation scripts; they are autonomous, context-aware actors capable of decision-making, escalation, and adaptive communication with other agents. Together, they form a collaborative mesh that executes end-to-end workflows in real time.
This approach draws from multi-agent systems in artificial intelligence, where autonomous entities cooperate to solve distributed, dynamic problems. It replaces rigid "if-this-then-that" logic with goal-based orchestration.
A Real-World Enterprise Use Case
Kumaresan Mudliar, a product manager working in the business spend management space, has championed this shift toward agentic orchestration. With experience building scalable intake platforms and automated request engines for global enterprises, he's seen firsthand how traditional workflows struggle with real-world complexity.
Take a typical procurement flow:
- A Requester Agent submits the initial request with limited information.
- A Pre-Processor Agent enriches it with financial codes, preferred suppliers, and contextual metadata.
- A Supplier Agent triggers onboarding tasks and validates supplier profiles against compliance rules.
- A Contract Agent generates a customized agreement based on risk scoring and business terms.
- A Compliance Agent performs policy checks and routes exceptions.
- An Approver Agent ensures the right stakeholder reviews and signs off on the request—managing escalations, re-approvals, and conditions dynamically.
"These workflows can span departments, systems, and geographies," explains Mudliar. "By enabling agents to operate autonomously with shared context, we remove the coordination burden from humans while making processes faster, more transparent, and auditable."
Why a Shared Framework for Agents Is Essential
For agents to collaborate effectively, there needs to be a common framework that defines their roles, capabilities, and protocols for interaction. Without this, agentic workflows can devolve into chaos—overlapping responsibilities, missed actions, or circular dependencies.
A robust agent framework includes:
- Clear Role Definitions: Each agent must know its purpose—what inputs it expects, what actions it can take, and what outcomes it is responsible for.
- Standardized Interfaces: Agents must communicate via predictable APIs or event models, ensuring smooth handoffs between agents even when new ones are introduced.
- Shared Context Layer: Agents should operate on a common data plane, where each state change or decision is visible and traceable across the system.
- Action Registry: A catalog of available actions (e.g., enrich data, validate contract, send for approval) that each agent can invoke, extend, or delegate.
- Conflict Resolution Protocols: Logic to resolve conflicting actions or determine agent priority when two agents attempt to act on the same state.
Think of this as the operating system for enterprise collaboration—a necessary abstraction that turns independent logic into coordinated intelligence.
The Agentic Workflow Builder: A New Kind of Platform
Rather than building workflows as linear chains of steps, product teams now need agentic workflow builders—platforms that let you configure:
- Agent roles and behaviors (e.g., enrich, validate, approve, draft)
- Policy triggers for activation and handoffs
- Shared data models and ontologies that unify context
- Runtime orchestration with versioning, observability, and fallback logic
This infrastructure allows teams to add or update agents without rewriting entire flows. Agents become pluggable and composable, forming flexible chains around the business intent.
As someone who has led large-scale platform modernization projects, Kumaresan Mudliar emphasizes the need for platforms to adapt to change without friction. "Static workflows break as soon as business conditions evolve. But with agentic builders, we can inject new logic on the fly—making the system resilient and future-ready," he adds.
Why Agentic Automation Matters
Agentic workflows are inherently more modular, adaptive, and resilient. They can accommodate edge cases without bloating the core logic, and they scale horizontally—adding more agents as complexity grows. Most importantly, they introduce a more intelligent way of automating work—based on intent, context, and collaboration, not just sequences and rules.
This architecture is also a natural foundation for the AI agent ecosystem now emerging—where LLM-powered agents can reason, summarize, and even negotiate on behalf of business users.
Looking Ahead
As Mudliar puts it, "We're moving from process modeling to intent orchestration. In this future, workflows are not hardcoded—they're co-created and fulfilled by agents that understand goals, context, and constraints."
Imagine a world where a business request triggers not a script, but a mission—and a team of AI-powered agents gets to work, handling each dimension of fulfillment intelligently. Approvals aren't just routed—they're contextual. Contracts aren't just generated—they're optimized. And users don't fill out forms—they state their need, and the system gets it done.
In this world, automation becomes orchestration. And the enterprise becomes a living, adaptive system—powered by a network of collaborative agents.
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