A Reliability-First Framework Combining Adaptive AI with Hallucination Safeguards

Autonomous AI systems are transforming how industries operate — enabling faster decisions and reducing dependence on manual processes. Enterprises across IT, healthcare, and logistics are deploying these agents to enhance complex, multi-step workflows. But as AI moves to the operational core, a fundamental tension emerges: how do you build autonomous agents that adapt without losing reliability?

The problem of hallucination — AI systems generating confidently wrong information at the wrong moment — is a critical risk for enterprise deployment. Existing research on hallucination mitigation in language models exists, but most solutions are not designed for mission-critical enterprise contexts where errors have direct operational, financial, or regulatory consequences.

This paper proposes a framework for autonomous enterprise AI that combines RL with RAG and uncertainty analysis to improve decision reliability. At its core is the H-Score — a new evaluation metric for identifying an AI agent's hallucination risk and predicting its business reliability. The goal is a principled architecture that bridges the gap between adaptability and dependability in enterprise-grade deployments.

The research focuses on three core objectives:

1. 01

   Enterprise Workflow Execution

   Designing AI agents capable of autonomously handling complex, domain-specific workflows in IT operations, healthcare administration, and logistics management.

2. 02

   Hallucination Mitigation

   Implementing a hybrid AI architecture that integrates reinforcement learning, retrieval-augmented generation, and human oversight to reduce hallucination rates in real-time applications.

3. 03

   Quantifiable Reliability Metrics

   Developing and validating the H-Score — a standardized metric for measuring and benchmarking the reliability of autonomous AI agents in enterprise environments.

Organizations continue to hesitate to deploy AI at scale due to uncertainty challenges and hallucination risk. Standard AI models from major vendors require industry-specific safeguards to prevent reliability failures in real-world settings. This research fills that gap by combining enterprise uncertainty detection with workflow optimization.

The framework delivers original findings on making AI more dependable by integrating reinforcement learning and retrieval-augmented generation into a business-grade process. This is not a theoretical exercise — the methodology is designed for validation against real enterprise datasets in logistics, healthcare, and IT support.

Despite widespread enterprise adoption, autonomous AI systems still struggle with reliable performance across different operational contexts. Tools from major vendors excel at decision-making but produce misleading results through hallucinations. Three critical gaps exist in current literature:

The research follows a structured methodology integrating machine learning techniques, uncertainty quantification, and enterprise deployment strategies. The study is divided into four phases: foundational development, vertical integration with industry partners, longitudinal evaluation, and commercialization.

The core methodology combines three established techniques into a novel enterprise-grade stack:

• Reinforcement Learning (RL): Training agents on structured workflows using Stable Baselines3, incorporating Confidence-Weighted Action Selection (CWAS) to prioritize high-certainty actions.
• Retrieval-Augmented Generation (RAG): Embedding domain-specific knowledge via vector databases to reduce hallucinations by retrieving verified enterprise data at inference time.
• Uncertainty Quantification: Comparing Monte Carlo dropout vs. conformal prediction for reliability scoring, feeding into the H-Score composite metric.
• Human-AI Escalation: A real-time verification loop where AI confidence scores determine dynamic escalation thresholds for human review in high-stakes decisions.

The proposed framework uses a multi-layered AI architecture designed to balance adaptability with hallucination mitigation. The architecture is designed for scalable deployment, ensuring compatibility with existing enterprise software ecosystems (SAP, ServiceNow, Epic EHR).

Initial experiments using synthetic IT ticket datasets produced early evidence that the proposed approach is feasible. These results informed the architecture decisions described above.

These findings support the viability of a hybrid RL + RAG architecture for enterprise AI agents. Full experimental validation with production datasets is planned across Phases 1 and 2.

Enterprise AI that self-directs decisions introduces both new capabilities and new risks. This framework's expected contributions span academic, industry, and societal dimensions:

This paper presents a structured framework for autonomous enterprise AI that reliably makes decisions at scale. By combining Reinforcement Learning, Retrieval-Augmented Generation, and uncertainty quantification, the proposed system addresses the central challenge of AI in enterprise: adaptability without reliability failure.

The H-Score metric provides a first-of-its-kind standardised measurement for hallucination risk in autonomous agents — enabling enterprises to benchmark, compare, and certify AI systems before production deployment. The CWAS algorithm and REAC benchmark corpus further ground the framework in reproducible, production-tested research.

This work represents a concrete contribution to the emerging field of trustworthy enterprise AI — one built by a practitioner who has operated data systems at scale, and designed to solve problems that exist in production today.