Agentic AI for Enterprise Decision Automation
Keywords:
Multi Agent, Autonomous AI, Decision Intelligence, Workflow Automation, Enterprise Systems, Data Management, Data Governance, Data Provenance, Cloud Integration, On Premise, Task Orchestration, Agent Collaboration, Agent Negotiation, Knowledge Sharing, Process Automation, Predictive Analytics, Decision Making, Causal Models, Semantic Models, Intelligent Agents.Abstract
Autonomous multi-agent AI supports decision intelligence and workflow automation in enterprise settings, enabling active and independent agents that learn from experience, collaborate, negotiate, share knowledge, and jointly solve problems. The architectural design ensures lightweight deployment and integrability with cloud-based or on-premise enterprise IT ecosystems. First, data management, provenance, and governance facilitate reliable enterprise decision systems. Then, agents assist human users in the full decision pipeline, from analysis to execution and control. Autonomy extends workflow automation: parallelizable enterprise tasks are coordinated by techniques from multi-agent systems, and workflows involving multiple actors are represented as task orchestration, where specialized agents negotiate and execute task sequences. An extensive evaluation demonstrates feasibility, potential, and future directions for real-world application. Autonomous multi-agent AI systems provide a new way to structure and automate enterprise organization and interaction.
Enterprise processes are supported by the evidence pipeline of decision intelligence: sensory data is transformed into information via analytical processes (theory-driven or machine-learning-based), and through prediction and recommendation into knowledge useful for decision-making and control. Semantic- or causal-based approaches to decision intelligence utilize autonomous visual agents to navigate, collect data, construct a model of the environment, and provide the evidence required for decision-making.
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