Problem & Motivation. The generative AI boom is spawning rapid deployment of diverse LLM software agents. New standards such as the Model Context Protocol (MCP) and Agent-to-Agent (A2A) protocols let agents share data and tasks, yet organizations still lack a rigorous way to keep those agents - and legacy algorithms - aligned with organizational targets and values. Objectives of the Solution. We aim to deliver a software reference architecture that (i) provides every stakeholder natural-language interaction across planning horizons with software agents and AI algorithmic logic, (ii) provides a multi-dimensional way for aligning stakeholder targets and values with algorithms and agents, (iii) provides an example for jointly modelling AI algorithms, software agents, and LLMs, (iv) provides a way for stakeholder interaction and alignment across time scales, (v) scales to thousands of algorithms and agents while remaining auditable, (vi) remains framework-agnostic, allowing the use of any underlying LLM, agent library, or orchestration stack without requiring redesign. Design & Development. Guided by the Design-Science Research Methodology (DSRM), we engineered HADA (Human-Algorithm Decision Alignment)-a protocol-agnostic, multi-agent architecture that layers role-specific interaction agents over both Large-Language Models and legacy decision algorithms. Our reference implementation containerises a production credit-scoring model, getLoanDecision, and exposes it through stakeholder agents (business manager, data scientist, auditor, ethics lead and customer), enabling each role to steer, audit and contest every decision via natural-language dialogue. The resulting constructs, design principles and justificatory knowledge are synthesised into a mid-range design theory that generalises beyond the banking pilot. Demonstration. HADA is instantiated on a cloud-native stack-Docker, Kubernetes and Python-and embedded in a retail-bank sandbox. Five scripted scenarios show how business targets, algorithmic parameters, decision explanations and ethics triggers propagate end-to-end through the HADA architecture. Evaluation. Walkthrough observation and log inspection were used to gauge HADA against six predefined objectives. A stakeholder–objective coverage matrix showed 100 % fulfilment: every role could invoke conversational control, trace KPIs and values, detect and correct bias (ZIP-code case), and reproduce decision lineage-without dependence on a particular agent hierarchy or LLM provider. Contributions. The research delivers (i) an open-source HADA reference architecture, (ii) an evaluated mid-range design theory for human–AI alignment in multi-agent settings, and (iii) empirical evidence that framework-agnostic, protocol-compliant stakeholder agents can simultaneously enhance accuracy, transparency and ethical compliance in real-world decision pipelines.

HADA: Human-AI Agent Decision Alignment Architecture

会议论文
发表日期：

2026年

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Tapio Pitkäranta,
Leena Pitkäranta

摘要

Problem & Motivation. The generative AI boom is spawning rapid deployment of diverse LLM software agents. New standards such as the Model Context Protocol (MCP) and Agent-to-Agent (A2A) protocols let agents share data and tasks, yet organizations still lack a rigorous way to keep those agents - and legacy algorithms - aligned with organizational targets and values. Objectives of the Solution. We aim to deliver a software reference architecture that (i) provides every stakeholder natural-language interaction across planning horizons with software agents and AI algorithmic logic, (ii) provides a multi-dimensional way for aligning stakeholder targets and values with algorithms and agents, (iii) provides an example for jointly modelling AI algorithms, software agents, and LLMs, (iv) provides a way for stakeholder interaction and alignment across time scales, (v) scales to thousands of algorithms and agents while remaining auditable, (vi) remains framework-agnostic, allowing the use of any underlying LLM, agent library, or orchestration stack without requiring redesign. Design & Development. Guided by the Design-Science Research Methodology (DSRM), we engineered HADA (Human-Algorithm Decision Alignment)-a protocol-agnostic, multi-agent architecture that layers role-specific interaction agents over both Large-Language Models and legacy decision algorithms. Our reference implementation containerises a production credit-scoring model, getLoanDecision, and exposes it through stakeholder agents (business manager, data scientist, auditor, ethics lead and customer), enabling each role to steer, audit and contest every decision via natural-language dialogue. The resulting constructs, design principles and justificatory knowledge are synthesised into a mid-range design theory that generalises beyond the banking pilot. Demonstration. HADA is instantiated on a cloud-native stack-Docker, Kubernetes and Python-and embedded in a retail-bank sandbox. Five scripted scenarios show how business targets, algorithmic parameters, decision explanations and ethics triggers propagate end-to-end through the HADA architecture. Evaluation. Walkthrough observation and log inspection were used to gauge HADA against six predefined objectives. A stakeholder–objective coverage matrix showed 100 % fulfilment: every role could invoke conversational control, trace KPIs and values, detect and correct bias (ZIP-code case), and reproduce decision lineage-without dependence on a particular agent hierarchy or LLM provider. Contributions. The research delivers (i) an open-source HADA reference architecture, (ii) an evaluated mid-range design theory for human–AI alignment in multi-agent settings, and (iii) empirical evidence that framework-agnostic, protocol-compliant stakeholder agents can simultaneously enhance accuracy, transparency and ethical compliance in real-world decision pipelines.