XPF: Agentic AI System for Business Workflow Automation
Achieving Reliable & Accurate AI-Driven Business Workflows with Human-in-the-Loop Planning
XPF is a novel agentic AI system enabling users to create, test, and deploy agents for complex, real-world business workflows using natural language. Comprising a planner, compiler, executor, and verifier, and leveraging LLMs, XPF's experiments demonstrate that human-generated plans achieve significantly higher accuracy and reliability compared to auto-generated plans.
Immediate Value
Executive Summary: Revolutionizing Business Workflows with XPF
The XPF system addresses critical challenges in leveraging Large Language Models (LLMs) for business automation, focusing on accuracy, reliability, and efficient execution. By integrating a planner, compiler, executor, and verifier, XPF empowers users to build, test, and deploy robust AI agents.
Key contributions include a new natural-language-based programmable system, novel techniques within each component (agent-oriented planning, LLM-based compilation, efficient execution, and feedback-based self-correction), and the development of five real-world agents.
Experimental results consistently show that agents built with human-generated plans deliver superior accuracy and reliability compared to those relying solely on auto-generated plans. This highlights the indispensable role of human expertise in defining complex business workflows for optimal AI agent performance.
100%
Accuracy (Human Plans)
3x
Reliability Boost (Human Plans)
5+
Real-World Agents Developed
Deep Analysis & Enterprise Applications
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System Design
Agentic Programming
Execution & Verification
Experimental Results
XPF’s architecture (Fig. 1) integrates a planner for natural language-based plan generation (human-editable), a compiler for translating plans into flow graphs, an executor for distributed execution leveraging LLMs, tools, and RAG, and a verifier for output accuracy checks with self-correction mechanisms. This holistic design ensures robust agent development.
XPF introduces a declarative, agent-oriented programming language where plans are structured in numbered steps, supporting substeps, parallelism ('At the same time', 'Do together'), and looping ('Repeat these steps'). Crucially, 'Verification' and 'Constraints' keywords allow for explicit test definitions and adherence rules, all parsed by the LLM-powered compiler.
The executor (built with LangChain) dynamically loads tools and context, traversing the flow graph to execute steps via LLMs, tools, or RAG. It invokes the verifier for output checks; if tests fail, the executor repeats steps with error feedback for self-correction. The verifier uses LLMs to generate 'yes/no' questions for natural language tests.
Evaluated across five real-world agents (Bike Safety, Marine Security, Poster, Graph, Whitepaper), XPF demonstrates superior performance with human-generated plans. These plans consistently achieve nearly 100% accuracy, significantly outperforming auto-generated plans. Reliability, measured by Rouge-L, Bleu, and Meteor scores, is also markedly higher with human-generated inputs, emphasizing human-in-the-loop benefits.
100%
Accuracy with Human-Generated Plans
Enterprise Process Flow
Task Description (NL)
→
XPF Planner (Human-edited Plan)
→
XPF Compiler (Flow Graph/AST)
→
XPF Executor (LLM, Tools, RAG)
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XPF Verifier (Output Check)
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Solution
| Feature | Human-Generated Plan (XPF) | Auto-Generated Plan (Vanilla LLM) |
|---|---|---|
| Accuracy | Consistently ~100% | Rarely ~100% |
| Reliability | Significantly higher (Rouge-L, Bleu, Meteor) | Lower, inconsistent output |
| Planning Origin | Human-edited natural language | LLM-generated natural language |
| Error Correction | Integrated feedback loop | Less robust / absent |
| Workflow Complexity | Handles complex business logic | Struggles with intricate logic |
Case Study: Enhancing Marine Security with XPF Agents
The Marine Security agent, one of five developed using XPF, aims to detect unauthorized motorized vehicles in marine environments and send audio alerts upon detection.
Leveraging human-generated plans within XPF, this agent demonstrated superior performance and reliability. The meticulous human-in-the-loop planning and verification process proved crucial for achieving optimal, real-world outcomes in a critical application.
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Your Path to Agentic AI
Implementation Roadmap
Our phased approach ensures a smooth and effective integration of agentic AI into your enterprise workflows.
Phase 1: Discovery & Strategy
In-depth analysis of current workflows, identification of automation opportunities, and strategic planning for AI agent deployment.
Phase 2: Agent Design & Development
Leveraging XPF, we design, develop, and rigorously test custom AI agents tailored to your specific business needs, focusing on human-guided planning.
Phase 3: Integration & Deployment
Seamless integration of developed agents into your existing systems and infrastructure, followed by initial deployment and performance monitoring.
Phase 4: Optimization & Scaling
Continuous monitoring, performance optimization, and iterative refinement of agents based on real-world feedback, enabling scalable automation across your enterprise.
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