Over the past two years, enterprise artificial intelligence has been stuck in the support phase, a world of smarter emails and faster document summaries that boosted individual productivity while leaving core business logic intact.
Now, the novelty of the chatbot gives way to the usefulness of the agent.
The industry is moving from generative AI to agentic AI, reorganizing itself around execution rather than simple information retrieval. Instead of a single assistant responding to prompts, organizations are deploying multi-agent systems or coordinated digital networks in which one agent collects the data, another validates it, a third executes the transaction, and a fourth ensures compliance.
For the digital economy, the value has shifted from the quality of the prompt to the coordination of the workflow.
Prompts to the process
Unique assistants respond to prompts. Multi-agent systems manage workflows.
These systems are cooperative networks in which agents share the context and transmit tasks to each other. below defined rules, according to Google. Such systems work best when work can be divided in modular stages and when communication between agents follows structured paths.
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The architecture reflects the operations of the business. Processes such as underwriting, claims management, purchase approval or financial reporting already go through sequential steps. Multi-agent systems reproduce this structure.
Unlike older screen capture or robotic process automation (RPA) tools that break when a website changes, these agent systems operate within the API layer of the business. They have permissions, track audit logs, and enforce policy In real time. They don’t just imitate human clicks; they navigate the corporate environment as digital employees.
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Growth of multi-agent deployments
Data suggests adoption is accelerating among businesses.
Multi-agent workflows grew up more than 300% over several months as organizations moved projects from pilot phases to production, according to a Databricks report. The agents are be trustworthy with infrastructure-level responsibilities including branching development databases and provisioning data environments.
Companies are also starting to formalize how these systems are built. AWS described several architectural models for multi-agent systems in financial services, including models in which a central monitoring agent assigns tasks and reviews the results, And more distributed designs where agents collaborate under defined constraints. The choice depends on risk tolerance, regulatory requirements and the level of human oversight required.
Anthropic building described multi-agent search systems in which one agent retrieves information, another critiques it, and a third synthesizes the results into a final result. The layered structure is designed to improve reliability by having agents check each other’s work.
Companies are also moving from experimentation to production. Capital one builds multi-agent workflows to support enterprise use cases, integrating agents directly into operational systems rather than isolating them in labs, VentureBeat reported. The emphasis is less on novelty and more on repeatable and governed execution.
Financial directors are also banking on greater autonomy for agents. THE PYMNTS Information report “CFOs advance AI but keep their hands on the wheel” found that 43% of CFOs said agentic AI could have a big impact on dynamic budget planning.. Almost half use AI to continuously monitor working capital and cash flow.
The difference is execution. Instead of using AI to generate information for humans to interpret, agent systems can update projections, flag deviations, initiate adjustments, and document changes within defined guardrails.
The researchers formed groups of AI Agents manage complex research tasks by assigning distinct roles such as planner, researcher and evaluator, then measure how effectively they shared information and corrected each other’s mistakes. In controlled experiments, the multi-agent setup completed tasks more accurately than a single agent working alone because each system focused on a defined function and verified the results.
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