In the quest for smarter automation, businesses have long leaned on rigid workflow engines and task-centric diagrams. But in an increasingly dynamic, AI-powered world, these static pipelines are starting to show their cracks. A new paper, “An Agentic AI for a New Paradigm in Business Process Development,” proposes a compelling shift: reframe business processes not as sequences of tasks, but as networks of autonomous, goal-driven agents.
From Flowcharts to Ecosystems
Traditional business process management (BPM) operates like a production line: each step is predefined, and systems pass the baton from one task to the next. This works well for predictable operations but falters in environments requiring adaptability, exception handling, or dynamic goal reconfiguration.
Instead, the agentic model centers around three primitives:
- Goals — the business outcomes to be achieved.
- Objects — the information elements (documents, records, messages) that mediate agent activity.
- Agents — autonomous executors capable of achieving goals when provided with the right inputs.
Rather than coding a sequence, businesses define what they want accomplished, what data is involved, and what agent capabilities are available. The rest—the order of operations, execution paths, fallback options—emerges dynamically.
A Slice of Pizza, Reimagined
To illustrate the model, the authors walk through a pizza delivery scenario. Here’s a simplified table that shows how the process is defined—not by a Gantt chart, but as agent-goal-object links:
| Agent | Capabilities | Trigger Object(s) | Final Object | Goal |
|---|---|---|---|---|
| a1 | Get & Check Order | order | checkedOrder | AcquireOrder |
| a2 | Inform Customer | checkedOrder/KO | customerNotice | CustomerAlerted |
| a3 | Inform Kitchen | checkedOrder/OK | pizzaSchedule | KitchenAlerted |
| a4 | Cook Pizza | pizzaSchedule | pizzaDone | CookedPizza |
| a5 | Deliver Pizza | pizzaDone | fulfilledOrder | PizzaDelivered |
The workflow isn’t hardcoded. Instead, it’s inferred: when a checked order is valid (checkedOrder/OK), it triggers a kitchen notification agent; if invalid, it triggers a customer alert. Each goal is a node, and each agent is an edge in the graph of process logic.
Why Agentic AI Unlocks Business Agility
This model excels in contexts where:
- Alternative paths exist (e.g., multiple agents capable of achieving the same goal based on constraints).
- Real-time decisions are needed (e.g., choosing a delivery agent based on availability).
- Emergent processes dominate (e.g., collaborations, exception handling, or volatile environments).
Moreover, the model supports key operational concepts like split goals (one object triggers multiple agents) and merge goals (one goal is achieved through the union of outputs from multiple agents). With the right metadata, even complex precedents and dependencies can be computed automatically.
A Formal Yet Flexible Foundation
Critically, the authors provide a formal schema to validate processes:
Agent = (aID, Capabilities, Trigger Objects, Resource Objects, Final Objects, Goal)
Goal = (gID, Object Set, Triggered Agents)
ABP = (Start Objects, End Objects, Resource Objects, Goals, Capabilities, Agents)
This allows systematic checks:
- Are there agents whose trigger objects are never created? (They’ll never activate.)
- Are there objects that are never consumed? (Redundant.)
- Is the precedence graph logically coherent?
This hybrid of declarative modeling and agentic logic builds both flexibility and auditability—a rare but necessary pairing in enterprise systems.
Beyond Automation: Toward AI-Native Governance
However, such agentic freedom raises governance concerns. What happens when autonomous agents make conflicting choices? Or worse, reinforce each other’s mistakes? The authors wisely raise questions of transparency, accountability, and oversight, noting that:
“Agentic AI amplifies both the capabilities and the risks of existing AI technologies.”
To mitigate these risks, the future likely lies in:
- Traceable execution paths and logs.
- Constraint-aware agent design (e.g., safe fallback behaviors).
- Modular override mechanisms for human-in-the-loop correction.
The Bigger Picture: Designing for Emergence
At its core, this paper reminds us that business automation should not mimic machines—it should harness them. As LLMs, planning engines, and generative agents grow more capable, the bottleneck shifts from technology to orchestration. Instead of chaining tasks, we must engineer environments where AI can collaborate toward outcomes.
This agentic model is not just a blueprint for process automation. It’s a design language for business agility in the age of autonomous software.
Cognaptus: Automate the Present, Incubate the Future