Generative Agentic AI and the Social Order: Capital, Cores, and Convergence in 2025

Author: Maria Fernandez

Affiliation: Independent researcher

Abstract

Agentic artificial intelligence—software agents that can perceive, plan, and act with minimal human oversight—has moved from prototype to practice in 2025. As organizations embed multi-step, goal-seeking agents into management, logistics, creative work, tourism services, and education, the debate has shifted from “can it work?” to “what kind of social order will it produce?” This article offers a critical, yet practical, sociological analysis of agentic AI using three theoretical lenses: Bourdieu’s forms of capital, world-systems theory, and institutional isomorphism. We argue that agentic AI reconfigures the distribution and convertibility of capital within firms and across regions; that it intensifies core–periphery dynamics while enabling new semi-peripheral niches; and that it spreads through mimetic, coercive, and normative pressures that make convergence around “best practices” likely—even when empirical validation remains thin. We synthesize current technical trajectories (generative models, world models, tool-use orchestration) with organizational realities (compliance, risk, skills), and we translate the analysis into testable propositions and a research agenda. The conclusion highlights a “governable autonomy” pathway—combining safety assurance, transparent evaluation, and field-appropriate standards—as the most credible route for sustainable adoption.

1. Introduction: From Automation to Agency

Most AI systems of the last decade were reactive: they classified, predicted, or generated content when prompted. Agentic AI is different. It decomposes goals, calls tools and APIs, evaluates progress, and adapts plans across multiple steps. In practical terms, an enterprise agent not only drafts a sales report; it gathers the data, reconciles inconsistencies, seeks clarifications, schedules follow-ups, and closes loops—often with little human micromanagement.

This shift matters sociologically. When software becomes a co-actor that takes initiative, it reshapes who holds power, which skills matter, how organizations coordinate, and how regions plug into global value chains. A critical lens is needed not to reject the technology, but to clarify the conditions under which agentic AI expands human capabilities rather than narrowing them.

We proceed in three moves. First, we define agentic AI and map its enabling stack. Second, we analyze it using Bourdieu (capital), Wallerstein (world-systems), and DiMaggio & Powell (institutional isomorphism). Third, we derive implications for management, tourism, and technology sectors, and lay out a research agenda.

2. What Is Agentic AI? A Socio-Technical Definition

Agentic AI refers to AI systems with four capacities: perception (ingesting data streams), deliberate planning (setting and revising sub-goals), action (executing via tools, APIs, or actuators), and adaptation (learning from feedback over time). These capacities are increasingly organized into layered architectures:

• Perception and representation: multimodal encoders; knowledge graphs; world models that simulate likely outcomes.

• Deliberation and planning: hierarchical controllers; task decomposition; constraint solvers; reinforcement-learning or search-based planners.

• Tool-use and action: API orchestration; connectors to enterprise systems; robotic effectors in physical settings.

• Feedback and governance: human-in-the-loop gates, audit logs, sandboxing, red-team tests, and policy constraints.

The novelty is not any single algorithm but the system-of-systems integration that lets agents “own” a process from intent to outcome. This turns questions of accuracy and latency into questions of accountability, alignment, and field-specific legitimacy.

3. Theoretical Lenses

3.1 Bourdieu: Forms of Capital in the Agentic Age

Bourdieu’s framework distinguishes economic, cultural, social, and symbolic capital, with field-specific rules governing their accumulation and convertibility. Agentic AI reshapes each:

1. Economic capital: Early adopters reduce coordination costs and compress cycle times. But cost advantages hinge on data access, compute, and integration expertise—assets unevenly distributed within and across firms.

2. Cultural capital: New literacies emerge: prompt/program design, policy authoring for agents, and reading audit trails. Certifications and micro-credentials become tokens of this cultural capital.

3. Social capital: Networks that grant access to high-quality proprietary data, partner APIs, and cross-firm sandboxes serve as conduits for agent performance. Partnerships themselves become a form of “agentic social capital.”

4. Symbolic capital: Claims of being “agent-powered” confer status. Awards, case studies, and media narratives convert cultural and social capital into legitimacy, even before robust longitudinal evidence accumulates.

Conversion dynamics. Agentic AI increases the convertibility among capitals. For example, cultural capital (policy-engineering skill) quickly becomes economic capital (productivity gains), which can be publicized as symbolic capital (market leadership). Conversely, reputational shocks (agent errors) can sharply devalue symbolic capital and, via compliance responses, drain economic capital.

Field effects. Within a field (e.g., hospitality or logistics), the dominant actors can define what counts as “responsible autonomy” and thereby set the exchange rates among capitals—who gets credit for efficiency, who bears blame for errors, and which metrics guide investment.

3.2 World-Systems Theory: Core, Periphery, Semi-Periphery

World-systems theory views the global economy as an unequal system with core zones controlling high-profit functions, peripheries providing low-margin labor and materials, and semi-peripheries mediating between the two. Agentic AI interacts with this structure in three ways:

• Concentration in the core: Compute, frontier models, and governance frameworks are concentrated in core economies, potentially deepening dependency.

• Peripheral precarities: If peripheries adopt low-grade agents mainly for surveillance or deskilling, they risk lock-in to low-value usage, reinforcing unequal exchange.

• Semi-peripheral openings: However, semi-peripheries can specialize in applied orchestration—turning general-purpose agents into domain-specific service bundles for tourism, healthcare back-office, or education technology. This niche leverages regional knowledge while sidestepping the capital intensity of frontier model training.

Key proposition: Agentic AI magnifies returns to coordination and integration, functions already advantaged in the core. But modular interfaces open adjacent possible niches for semi-peripheries that master field-specific constraints.

3.3 Institutional Isomorphism: Why Convergence Happens

DiMaggio and Powell identify three drivers of organizational similarity:

• Coercive: regulations, audits, procurement standards.

• Mimetic: copying peers amid uncertainty.

• Normative: professional training and standards bodies.

Agentic AI adoption exhibits all three. Compliance and risk management (coercive), executive fear of being left behind (mimetic), and emerging professional norms (normative) push firms toward similar architectures: sandboxed agents, policy-as-code, auditability, and staged rollout. The risk is performative isomorphism—adopting visible controls rather than effective ones. The opportunity is substantive isomorphism—shared, evidence-based practices that really work.

4. Sectoral Implications

4.1 Management and Operations

Agentic AI changes managerial work from direct supervision to policy design and exception handling. Middle managers shift from monitoring tasks to specifying constraints, evaluating outcomes, and arbitrating trade-offs when agents face conflicting goals (e.g., speed vs. compliance). New roles include agent safety officer, policy engineer, and data steward.

Key tensions:

• Speed vs. assurance: Pushing autonomy raises throughput but demands rigorous pre-deployment testing and post-deployment monitoring.

• Local knowledge vs. global templates: Agents trained on global corpora may miss cultural subtleties. Field teams must enrich agents with local rules of thumb.

• Transparency vs. IP protection: Explaining agent decisions increases trust but risks revealing proprietary logic.

Propositions (Management):

• P1: Firms that treat agent policies as living artifacts—versioned, reviewed, and stress-tested—achieve higher sustained ROI than firms treating them as one-off configurations.

• P2: Cross-functional review boards (operations, legal, domain experts) reduce severe agent incidents without significant throughput loss, compared to siloed deployments.

4.2 Tourism and Hospitality

Tourism is an ideal domain for multi-agent collaboration: itinerary planning, dynamic pricing, guest communications, sustainability reporting, and cross-border compliance. Properly designed agents deliver hyper-local personalization while smoothing unpredictable demand.

Opportunities:

• Service choreography: One agent negotiates transport options while another checks visa rules and a third monitors weather disruptions—coordinated via shared state and user preferences.

• Sustainability and SDG reporting: Agents automate data collection for energy use, waste, and local-supplier ratios, enabling transparent impact dashboards.

• Experience design: Generative agents craft narratives and micro-tours aligned to cultural norms and accessibility needs, offering inclusive tourism.

Risks:

• Cultural flattening: If agents encode generic “global tourist” assumptions, they may erase local nuance.

• Data extraction: Peripheral destinations risk becoming data suppliers to core platforms without capturing value.

Propositions (Tourism):

• P3: Destinations that co-govern agent templates with local associations produce higher visitor satisfaction and fewer cultural frictions than destinations adopting vendor defaults.

• P4: Revenue share models tied to data capitalization (not only bookings) increase local retention of value.

4.3 Education and Skills

In education, agents act as adaptive tutors, administrative co-pilots, and research assistants. The central question is how agentic AI interacts with cultural capital: do agents democratize elite study skills, or do they widen gaps as those with strong meta-cognition exploit agents better?

Propositions (Education):

• P5: Students trained in agent-of-record practices (documenting prompts, decisions, and sources) show higher transfer learning than those using agents informally.

• P6: Institutions that embed assessment resilience (oral defenses, artifact inspection, process portfolios) channel agent use toward learning rather than shortcutting.

5. Capital Reconfigured: A Field-Level View

Across sectors, agentic AI converts process knowledge into a programmable asset. This asset is valuable when:

1. the domain is well-specified,

2. failure costs are bounded, and

3. feedback is available to improve policies.

Where these conditions hold (e.g., back-office operations, itinerary logistics), we observe rapid productivity gains. Where ambiguity is high (creative strategy, ambiguous ethics), agents augment but do not replace human judgment. This suggests a barbell adoption: heavy agentization at the routine-complex end, human-centric control at the ambiguous-consequential end.

Bourdieu revisited. Because agentic competence is partly codified (policies, checklists, guardrails), the habitus of effective human collaborators shifts toward pragmatic meta-cognition: knowing when to delegate, when to constrain, and how to interpret agent rationales. Teams that accumulate this habitus will better convert cultural capital into economic performance.

6. Core–Periphery Dynamics: Risks and Openings

Data gravity and compute gravity still favor core economies. Yet agentic systems depend as much on domain constraints and institutional knowledge as on raw model scale. This creates room for semi-peripheral orchestration firms to package agents for local regulatory codes, linguistic norms, and sector standards (e.g., eco-labels in hospitality, clinical terminologies in health tourism, or customs rules in logistics).

Policy implication: Export-oriented peripheries should prioritize sovereign data agreements, shared agent sandboxes, and regional interoperability standards to capture value beyond commodity provisioning.

7. Institutional Isomorphism: From Performative to Substantive

Adoption often begins performatively: dashboards, policy statements, and staged demos. Substantive isomorphism requires shared evidence and field-specific benchmarks:

• Stress tests: scenario catalogs for break-the-glass moments (legal holds, fraud spikes, emergency rerouting).

• Audit trails: cryptographically signed logs enabling counterfactual replay of agent decisions.

• Fitness functions: field-defined multi-objective metrics (e.g., service quality + compliance + energy footprint).

Professional programs can anchor norms around these artifacts, converting mimetic rush into controlled evolution.

8. Safety, Assurance, and “Governable Autonomy”

A credible compromise between innovation and caution is governable autonomy: humans set goals and constraints; agents act within envelopes; evidence accumulates through continuous testing.

Core components:

• Policy-as-code: declarative constraints that business owners can read, not just engineers.

• Tiered autonomy: from suggestive (Level 0) to supervised (Level 1–2) to bounded autonomous execution (Level 3), with hard stops for sensitive actions.

• Counterfactual testing: agents are routinely run against historical incidents and synthetic stressors.

• Incident taxonomy: severity levels trigger standard responses, root-cause analysis, and policy updates.

• Dignity and rights: explicit protections for workers and customers affected by agent decisions, including appeal mechanisms.

Propositions (Safety):

• P7: Organizations that institutionalize counterfactual testing reduce high-severity incidents more effectively than those relying on manual spot checks.

• P8: Clear autonomy tiers correlate with higher trust from regulators and partners, accelerating procurement.

9. Methodological Notes: Studying Agentic AI in the Wild

To progress beyond anecdotes, we need mixed-methods designs:

• Ethnographies of agent–human collaboration in frontline settings.

• Field experiments with A/B-tested policy variants.

• Network analyses of partner ecosystems, mapping how data sharing affects performance.

• Event studies around policy upgrades or incidents to estimate causal impacts.

• Comparative case studies across regions to evaluate world-systems hypotheses about capture vs. capability building.

Researchers should publish open measurement protocols (even when data stay private) to allow cross-study comparability.

10. Practical Playbooks by Sector

10.1 Management Playbook

1. Define decision envelopes where agents may act; 2) Codify policies with business-readable rules; 3) Instrument everything with logs; 4) Review monthly with cross-functional committees; 5) Invest in skills—policy design, exception triage, and audit literacy.

10.2 Tourism Playbook

1. Localize agent templates with cultural norms; 2) Mediate between sustainability data collectors and operators; 3) Bundle services (itinerary + compliance + impact reporting); 4) Share value from data capital with local partners.

10.3 Technology Playbook

1. Adopt layered architectures (perception, planning, action, governance); 2) Maintain model pluralism to avoid single-vendor lock-in; 3) Automate red-teaming and chaos testing; 4) Publish incident learnings internally; 5) Plan exits for when autonomy should be rolled back.

11. Ethical Horizons: Beyond Compliance

Ethics is not a checkbox; it is an ongoing negotiation among stakeholders. Two frontiers deserve emphasis:

• Epistemic humility: Agents can be confident and wrong. Designs should reflect calibrated uncertainty and graceful deference.

• Distributive justice: Capture part of the productivity gains to upskill workers and support communities in which agentic value is created.

Bourdieu’s warning about symbolic power applies: narratives about “inevitability” can mask choices. Transparent trade-offs and participatory governance help keep agency—human agency—at the center.

12. Limitations and Future Research

This article offers a conceptual synthesis rather than a statistical meta-analysis. Future work should test the propositions across sectors and regions, measure capital conversion rates in agent-mediated workflows, and evaluate governance schemes under stress.

A promising line is to operationalize world-systems dynamics at the level of digital trade: tracking who supplies data, who orchestrates agents, who sets standards, and who captures rents.

13. Conclusion: Choosing the Path of Governable Autonomy

Agentic AI is not simply a new tool; it is a new way of arranging cooperation between humans and software. Whether it elevates or erodes human capability depends on how capitals are allocated, how fields set their rules, and how institutions converge on substantive rather than performative practices.

If organizations cultivate policy literacy, build transparent assurance pipelines, and invest in shared measurement, they can move beyond hype to durable value. If regions design data partnerships that reward local knowledge, semi-peripheries can turn orchestration into comparative advantage. If professions codify real standards, isomorphism can be a force for safety rather than mere signaling.

Agentic AI will not replace human agency; it will reconfigure it. The task for 2025 is to make that reconfiguration just, productive, and worthy of trust.

Keywords (SEO)

agentic AI, autonomous agents, AI governance, digital transformation, management automation, tourism technology, socio-technical systems, world-systems theory, Bourdieu capital, institutional isomorphism

Hashtags

#AgenticAI#AutonomousAgents#AIandSociety#DigitalTransformation#AIinManagement#ResponsibleAI#GlobalInnovation

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