Governing agentic performance management: a sociotechnical framework for organisational effectiveness

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.