Agentic AI in 2025: A Sociological Political-Economy of Autonomous Systems, Organizational Imitation, and the Uneven Geography of Computation

Author: Sholpan Rakhimova

Affiliation: Independent researcher

Abstract

Agentic artificial intelligence—autonomous or semi-autonomous systems capable of planning, tool-use, and self-directed action—has emerged as a defining technological trend of 2025. This article develops a critical yet practical, interdisciplinary analysis that connects the technical properties of agentic AI to wider social structures. Drawing on Bourdieu’s theory of capital, world-systems theory, and institutional isomorphism, I argue that agentic AI is not simply a set of algorithms but an evolving socio-technical field shaped by unequal access to compute and data, competitive pressures on organizations to imitate early adopters, and struggles over legitimacy, safety, and control. The paper synthesizes current technical architectures and governance proposals with implications for management, tourism and hospitality, education, and public services. It proposes measurable indicators for responsible deployment and outlines a staged roadmap for organizations seeking value without eroding trust. The conclusion highlights open research questions on long-horizon safety, cross-border standards, labor transitions, and equitable participation in the emerging agentic AI economy.

Keywords: agentic AI; autonomous systems; institutional isomorphism; Bourdieu; world-systems; AI governance; management; tourism; education; digital political economy; organizational strategy

1. Introduction: Why Agentic AI Becomes a “Trend of the Week”

What distinguishes 2025 from the previous phases of AI adoption is the mainstreaming of agentic AI—systems that move beyond reactive assistance toward proactive goal-seeking behavior with planning, memory, and tool integration. In practical terms, this means software that can break tasks into steps, call outside tools or services, monitor results, and adapt plans with minimal human guidance. The immediate attraction is clear: organizations are confronted with rising complexity, chronic skill shortages, and a premium on speed. Agentic systems promise end-to-end automation and continuous optimization.

Yet a technology becomes a social trend only when institutions reorganize around it. This article therefore treats agentic AI as a socio-technical shift and offers a theoretical lens that links algorithms to power, legitimacy, and global economic structure. It asks:

1. How do technical features of agentic AI interact with organizational fields and the global division of labor in data and compute?

2. Why are firms and universities converging on similar agentic strategies, and with what risks?

3. How can managers in sectors like tourism and hospitality, education, and public services deploy agents responsibly while preserving human expertise and public trust?

The argument proceeds by weaving together three theoretical strands with a concrete, implementation-oriented analysis.

2. Conceptual Framework

2.1 Bourdieu’s Capitals in the Agentic AI Field

Bourdieu’s theory of capital—economic, cultural, social, and symbolic—helps explain differential capacity to build and govern agents.

• Economic capital: Access to high-end compute, data centers, and engineering teams. Organizations with stronger balance sheets accumulate the infrastructure to run long-horizon planners and memory-intensive agents.

• Cultural capital: Technical literacy, model ops maturity, experimental discipline, and safety engineering. Elite cultural capital includes knowledge of formal verification, alignment, and audit methods.

• Social capital: Partnerships across cloud providers, startups, and research labs that accelerate access to models, tooling, and best practices.

• Symbolic capital: Reputation for trustworthiness and safety. Institutions with symbolic capital gain regulatory goodwill and user adoption, allowing faster scaling of agentic services.

The field of AI—composed of labs, vendors, standards bodies, universities, and regulators—becomes a competitive arena where these capitals are converted and contested. For example, a hospitality chain that cultivates cultural capital in data governance can convert that into symbolic capital (trust), which in turn attracts partnerships (social capital) and investment (economic capital) for agentic deployments.

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

World-systems theory links agentic AI to a global division of computation and data. Core economies house hyperscale compute, proprietary foundation models, and lucrative downstream markets. Semi-peripheral regions offer talent, specialized datasets, and niche applications; peripheral regions risk becoming raw-data exporters or late adopters paying rents for access.

This pattern matters because agentic AI magnifies returns to scale: large compute budgets and tightly-coupled tool ecosystems enable longer planning horizons, better tool-use, and more reliable autonomy. Without deliberate policy transfer and open standards, agentic capabilities can entrench a new computational core-periphery structure.

2.3 Institutional Isomorphism: Why Everyone Starts to Look the Same

DiMaggio and Powell’s account of coercive, mimetic, and normative isomorphism explains rapid convergence in organizational AI strategies:

• Coercive: Compliance requirements (auditability, privacy, sectoral rules) push similar dashboards, logs, and human-in-the-loop controls.

• Mimetic: Uncertainty encourages imitation of perceived leaders—pilot projects, “AI agent hubs,” and safety checklists spread quickly.

• Normative: Professional communities (data science, risk, compliance) circulate standards and curricula that define “good practice,” creating similar architectures.

Isomorphism accelerates diffusion and reduces coordination costs, but it can also propagate shared blind spots—for example, over-reliance on benchmark proxies that fail under real-world distribution shift.

3. From Models to Agents: Technical Elements in Plain Terms

3.1 Core Capabilities

1. Planning and decomposition: Converting a broad goal into steps; choosing when to branch, retry, or escalate to a human.

2. Tool-use and APIs: Securely calling calendars, databases, search, payment rails, or code interpreters; verifying tool responses.

3. Memory and state: Retaining long-term context and relevant artifacts; forgetting responsibly to honor privacy and legal constraints.

4. Monitoring and self-correction: Detecting failure modes, rolling back side effects, and updating plans.

5. Alignment and constraints: Guardrails that encode policies, risk thresholds, and ethical norms; human oversight for critical actions.

3.2 Reliability Challenges

• Error cascades occur when a small hallucination compounds across steps.

• Distribution shift breaks plans when real-world data diverge from training conditions.

• Spec-ambiguity arises when goals are underspecified; the agent optimizes the wrong proxy.

• Interface fragility shows up when dependent tools change or rate-limit unexpectedly.

A practical implication is that organizations must treat agentic AI as systems engineering—with versioning, observability, rollback plans, and incident playbooks—rather than as “just a model.”

4. Field Dynamics: How Theory Meets Practice

4.1 Capital Conversion in the Enterprise

• Firms with economic capital acquire orchestration platforms and safety tooling; they leverage cultural capital to define evaluation harnesses and pre-deployment tests; they activate social capital through vendor alliances; and they defend symbolic capital by publishing safety reports and inviting audits.

• Smaller actors can compete by specializing in domain cultural capital—deep process knowledge and curated datasets—outperforming generalist agents in niche tasks.

4.2 The Global Division of Compute

World-systems dynamics predict concentration of agentic infrastructure in compute-rich cores. Semi-peripheral regions gain leverage via:

• Sovereign or regional model hosting with shared safety standards.

• Cross-border talent exchanges and training programs to raise cultural capital.

• Open benchmarks that reflect local languages, legal regimes, and sector needs.

4.3 Organizational Imitation and Its Limits

Isomorphic pressures create playbooks—pilot sandboxes, human-in-the-loop review, and tiered risk classifications. The risk is performative compliance: organizations display artifacts of safety (dashboards) without addressing core reliability or labor impacts. A robust approach combines the forms (isomorphic practices) with substantive, measured safeguards.

5. Sector Analyses

5.1 Management and Operations

Use cases: end-to-end workflow orchestration, forecasting, procurement, IT service management, finance close.Benefits: shorter cycle times, error reduction, and continuous monitoring.Risks: automation of brittle processes, spec-ambiguity, and new operational debt when agents silently fail.Mitigation: explicit service-level objectives for agents, layered approvals for high-impact actions, and post-incident reviews that treat agents as first-class “team members” subject to accountability.

5.2 Tourism and Hospitality

Use cases: itinerary planning, dynamic pricing strategies, multilingual guest messaging, back-of-house inventory optimization.Opportunities: personalized experiences at scale; continuous revenue management that reacts to events.Sociological lens: cultural capital matters—contextual understanding of local norms and seasonality increases agent value. Symbolic capital (brand trust) is decisive when agents interact with guests.Guardrails: transparent disclosure when interactions are agentic; easy escalation to humans; inclusive datasets to avoid linguistic or cultural bias.

5.3 Education and Lifelong Learning

Use cases: study coaches, assessment agents with rubric checking, administrative automation.Equity: world-systems dynamics warn of educational divides if agentic tools remain concentrated in the core. Shared curricula, open rubrics, and regional consortiums can bridge gaps.Academic integrity: design for constructive alignment—agents support learning goals without replacing human judgment; maintain audit trails for assessment decisions.

5.4 Public Services and Regulation

Use cases: benefits triage, permit intake, infrastructure maintenance scheduling.Ethics: the state’s symbolic capital depends on due process—explainability, appeal rights, and human review are non-negotiable.Governance: publish model cards, risk tiers, and measurable fairness tests; separate policy setting (human) from execution (agent with oversight).

6. Measurement: What to Track Beyond Hype

To move from aspiration to accountability, organizations can track:

1. Task success rate (TSR) with confidence intervals, segmented by user cohorts and languages.

2. Safe-action rate (SAR): percentage of actions passing constraint checks and post-hoc audits.

3. Escalation latency: time from risk detection to human takeover.

4. Bias and inclusion metrics: error differentials across protected or relevant demographic groups; multilingual parity.

5. Energy and cost intensity per successful episode.

6. Incident recurrence: repeated failure motifs and mean time to mitigation.

7. Human satisfaction and trust: user-reported clarity, consent, and perceived helpfulness.

These indicators must be embedded in continuous evaluation, not just pre-deployment testing.

7. Implementation Roadmap for Responsible Agentic AI

Phase 1: Groundwork

• Map candidate processes; eliminate broken workflows before automation.

• Establish a risk taxonomy (low, medium, high) tied to approval gates.

• Invest in data governance and red-team style testing; define retention and deletion policies for agent memory.

Phase 2: Sandboxes and Human-in-the-Loop

• Pilot agents on low-risk tasks with clear rollback.

• Instrument everything: traces, tool call logs, policy hits, and counterfactual checks.

• Train staff to supervise agents; create “agent owner” roles akin to service owners.

Phase 3: Scale and Federate

• Expand to medium-risk processes with tiered autonomy: agents propose, humans approve; later, agents act within budget and policy limits.

• Adopt cross-functional safety reviews with compliance, security, and domain experts.

Phase 4: Governance and External Assurance

• Publish artifacts of accountability (capability cards, evaluation reports).

• Seek independent audits for high-impact deployments; join sector consortia to harmonize standards and incident reporting.

8. Labor, Skill, and the Sociology of Work

Agentic AI reshapes the division of cognitive labor. Routine coordination tasks shrink; demand rises for exception handling, judgment, and socio-technical supervision. Using Bourdieu, workers can convert cultural capital (know-how about processes and ethics) into symbolic capital as guardians of trustworthy automation. Organizations should:

• Create career ladders for “agent operations,” safety engineering, and policy translation roles.

• Offer training in prompt design, tool orchestration, and bias auditing.

• Share productivity gains through reskilling budgets and internal mobility, preserving social cohesion and trust.

9. Ethics, Risk, and Trust Architecture

Principles: necessity and proportionality (deploy agents when benefits exceed risks), transparency (clear disclosure and logs), contestability (appeal channels), and non-discrimination (evaluated empirically).

Practices:

• Policy-as-code: encode legal and ethical constraints in the agent runtime.

• Dual-control for critical actions: finance transfers, medical or legal decisions require human countersignature.

• Data minimization and memory hygiene: limit what agents remember; implement time-bound retention and deletion.

• Crisis drills: exercise incident response with simulated failures and adversarial prompts.

Trust is not a marketing claim; it is a measurable outcome sustained by these practices over time.

10. The Political Economy of Standards

The next decade will be defined by standard-setting: evaluation benchmarks, incident taxonomies, model and agent “capability cards,” and interfaces for audit logs. Core economies will attempt to universalize their standards; semi-peripheral alliances can push for mutual recognition frameworks that respect local laws while enabling interoperability. This re-balancing is central to avoiding a computational dependency trap predicted by world-systems analysis.

11. Research Agenda

1. Long-horizon safety: formal methods and empirical stress tests for multi-step plans under uncertainty.

2. Socio-linguistic robustness: equitable performance across dialects, scripts, and low-resource languages.

3. Human-agent teaming: design patterns for calibrated trust, shared mental models, and graceful handoffs.

4. Evaluation science: task families and metrics that reflect real work, not only static benchmarks.

5. Comparative governance: cross-jurisdictional studies of liability, procurement, and audit practices.

6. Labor transitions: longitudinal studies on skill formation, well-being, and wage dynamics in agent-augmented workplaces.

7. Energy and sustainability: lifecycle assessments of agentic pipelines and incentives for efficient planning and memory.

12. Limitations and Scope

This is a conceptual synthesis rather than a single empirical study. It integrates technical descriptions with sociological theory to guide practice. Future work should triangulate with sector-specific case studies and quantitative evaluations.

13. Conclusion

Agentic AI is more than a weekly headline. It is an institutional reconfiguration of how goals are set, plans are executed, and accountability is assigned in digitally mediated organizations. Through Bourdieu’s capitals we see how unequal resources shape who builds trustworthy agents; through world-systems theory we locate agentic infrastructures in a stratified global economy; through institutional isomorphism we explain rapid convergence—both the benefits of coordination and the dangers of shared blind spots.

For managers in technology, tourism and hospitality, education, and public services, the message is practical: treat agents as socio-technical systems with owners, metrics, and rights of appeal; invest in cultural and symbolic capital to earn trust; participate in standards to avoid dependency; and stage deployment with human-centered safeguards. Done well, agentic AI can compress operational friction, scale personalization, and free human attention for creativity and care. Done poorly, it amplifies opacity, concentrates power, and alienates the very people it is meant to serve. The difference lies not only in the models we choose but in the institutions we build around them.

Hashtags

#AgenticAI #AutonomousSystems #AIGovernance #DigitalPoliticalEconomy #ResponsibleInnovation #ManagementTechnology #SocioTechnicalSystems

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