ABP is a research and engineering framework for exploring adaptive, explainable, and policy-driven agentic AI workflows in enterprise settings.
Traditional workflow systems are often slow, rigid, manually escalated, and unable to make intelligent context-aware decisions at scale. ABP solves this problem by introducing a formal architecture for dynamic workflow automation and enterprise decision intelligence.
Where:
- G = Goals
- A = Agents
- O = Objects
- C = Context
- R = Rules
- P = Policies
This architecture enables intelligent decision routing, policy enforcement, automated approvals, compliance governance, workflow optimization, and scalable enterprise automation.
The major original contribution of this framework is the concept of Decision Boundary Rigidity.
This introduces a formal method for measuring inferential rigidity in enterprise decision systems and improving workflow execution using explainable AI architecture.
It represents both a theoretical and practical contribution to enterprise CRM automation, agentic AI systems, and intelligent business process governance.
This framework demonstrates that enterprise decisions should not be treated as static checklist-based execution, but as dynamic policy-aware inferential systems.
The accompanying research manuscript is included in this repository.
The research demonstrates:
- formal framework architecture
- workflow simulation methodology
- policy-driven automation design
- enterprise validation experiments
- implementation strategy
- deployment-oriented implementation design
- enterprise integration scenarios
The repository is intended to demonstrate how the framework can be implemented and evaluated in enterprise-oriented scenarios.
- CRM workflow automation
- approval routing systems
- Agentforce AI orchestration
- customer operations management
- HR workflow automation
- compliance approval systems
- intelligent escalation systems
- enterprise policy enforcement
- financial workflow routing
- service operations optimization
- large-scale workflow governance
- Agent orchestration engine
- Policy engine
- Rules engine
- Workflow engine
- API integrations
- Docker deployment
- Dashboard modules
- Evaluation framework
- Compliance architecture
- Salesforce integration
- ServiceNow integration
- Enterprise deployment structure
This framework enables organizations to reduce manual approvals, improve compliance safety, accelerate enterprise decision-making, and create scalable intelligent workflow systems for large-scale operations.
It is designed as an implementation-oriented research framework for enterprise scenarios.
The objective is measurable operational efficiency, stronger governance, and explainable AI-driven workflow execution.
Nikhil Reddy Donapati
Independent Researcher
Senior Salesforce Architect & Agentforce AI Specialist
Texas, United States
ORCID: 0009-0006-7699-3928