What question did this study set out to answer?

This research aims to develop a framework for performance management that utilizes autonomous AI as an active organizational agent.

May 20, 2026

Governing agentic performance management: a sociotechnical framework for organisational effectiveness

Key Points

This research aims to develop a framework for performance management that utilizes autonomous AI as an active organizational agent.
Integrated agency theory, intelligent sociotechnical systems, and technology acceptance perspectives.
Derived nine testable propositions for empirical research based on the framework.
Explored governance challenges associated with agentic AI deployment and implications for organizational design.
Identified 'specification risk' as a governance issue when agentic AI optimizes objectives that may conflict with organizational values.
Proposed two governance pillars - procedural transparency and sociotechnical envelopment - to mitigate risks.
Found that successful governance increases alignment, agility, and wellbeing while ungoverned AI deployment may lead to negative outcomes.

Abstract

Purpose Organisations are increasingly delegating consequential decisions to autonomous software. This paper aims to develop agentic performance management (A-PM), a framework that reconceives performance management when agentic AI (A-AI) acts as a delegated organisational actor rather than a passive analytic tool. Design/methodology/approach Agency theory, intelligent sociotechnical systems (iSTS) and technology acceptance perspectives are integrated to explain how agentic capabilities (goal-driven autonomy, adaptive planning and hyper-contextual reasoning) reshape performance management processes and governance demands. From this synthesis, nine testable propositions are derived to guide empirical research. Findings Agentic deployment produces a distinct governance problem, termed specification risk, whereby agents faithfully optimise formalised objectives that may diverge from tacit organisational values. To contain this risk, the framework prescribes two constitutive governance pillars – procedural transparency (explainability, auditability, employee voice) and sociotechnical envelopment (culture, managerial capability, oversight structures). When matched with social readiness, A-PM improves alignment, agility and wellbeing; when ungoverned, it amplifies harm. Practical implications Managers and HR teams must treat A-AI rollout as organisational redesign: pilot within tight governance envelopes, build managerial AI literacy and embed contestability and audit trails before scaling. Originality/value This paper extends agency theory to non-human agents, introduces specification risk for performance management and offers an integrative, testable research agenda for scholars and practitioners concerned with algorithmic governance and organisational design.

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