The science of agentic AI - What leaders should know
This document presents a Critical Discourse Analysis (CDA) generated from a prompt designed to deconstruct a text's linguistic architecture. The prompt mandates an analysis grounded in Systemic Functional Linguistics (specifically transitivity) and critical theories of lexical choice. The primary objective is to reveal how the text constructs a specific version of social reality by strategically managing agency, embedding ideological values in seemingly neutral words, and positioning participants in specific power relationships. This audit functions to unmask the ideological work of the text, moving beyond its surface meaning to expose how it shapes a reader's understanding of responsibility, power, and "common sense." All findings and summaries below were generated from detailed system instructions provided to a large language model and should be read critically as interpretive outputs—not guarantees of factual accuracy or authorial intent.
Source Title: The science of agentic AI: What leaders should know Source URL: https://www.theguardian.com/business-briefs/ng-interactive/2025/oct/27/the-science-of-agentic-ai-what-leaders-should-know Model: gemini-2.5-pro Temperature: 1.0 Tokens: input=1010, output=10803, total=11813 Source Type: brief Published: Analyzed At: 2025-11-09T11:31:35+00:00Z Framework: cda-soft Framework Version: 2.1
Task 1: Agency and Accountability Audit​
This task, the Agency and Accountability Audit, investigates how agency—the power to act—is linguistically constructed, obscured, or strategically assigned within the text. The analysis identifies who (or what) is presented as an actor, what kind of processes they engage in, and what linguistic mechanisms (like passive voice or using abstract nouns as actors) are used to manage responsibility and shape the reader's understanding of causation and power.
Instance 1: Personifying AI as an Autonomous Historical Actor​
Quote: "While generative AI seems to have arrived instantaneously and matured rapidly in just the last few years, it in fact is firmly grounded in decades of foundational science..."
Participant Analysis: The key participant is 'generative AI' (Subject), connected by a material-like process ('arrived', 'matured'). Humans (engineers, investors, data labelers) who are mandatory for its creation are absent.
Agency Assignment: Agency is delegated to the technology itself. It is positioned as an autonomous entity that 'arrives' and 'matures' on its own timeline, obscuring the human and corporate actions that drive its development.
Linguistic Mechanism: Abstract or inanimate actors
Agency Strategy: Personification
Power Analysis: This construction benefits the corporations developing AI. It frames technological development as a natural, inevitable process akin to biological growth, removing it from the realm of human choice, corporate strategy, and political contestation. This makes it difficult to question the direction or pace of development, as one cannot argue with a force of nature.
Interpretive Claim: This framing naturalizes corporate-driven technological development as a historical inevitability, erasing the immense human labor, capital investment, and strategic decisions that produce it.
Instance 2: Erasure of the Advisor via Agentless Passive​
Quote: "In the meanwhile, enterprises are advised to provide explicit instructions or prompts to agentic AI..."
Participant Analysis: The key participant is 'enterprises' (Goal, but grammatical Subject), which is acted upon by an unstated process ('are advised'). The process is relational. The mandatory but absent participant is the Actor who is doing the advising (e.g., 'we advise', 'experts advise').
Agency Assignment: Agency is obscured. By omitting the agent who performs the act of advising, the advice is presented as a neutral, objective necessity rather than a subjective recommendation from a specific source (like an Amazon-affiliated scholar).
Linguistic Mechanism: Agentless passive
Agency Strategy: Erasure
Power Analysis: This construction elevates the authority of the advice and the speaker. It transforms a specific viewpoint into a universal, common-sense imperative. This serves the interest of the speaker and their institution by positioning their perspective as unquestionable expert guidance, preventing questions about potential biases or conflicts of interest.
Interpretive Claim: By erasing the source of the advice, the text transforms a subjective recommendation from an interested party into an objective, universal mandate for business leaders.
Instance 3: Delegating Knowledge Production to an Abstract Field​
Quote: "The field of behavioral game theory studies how humans play in settings involving bargaining, trading, negotiation and other strategic interactions."
Participant Analysis: The participant 'The field of behavioral game theory' is the Actor in a mental/verbal process ('studies'). The human researchers who actually perform the studies are absent.
Agency Assignment: Agency is delegated from individual, fallible humans to an abstract, institutional entity ('The field'). This collectivizes and depersonalizes the act of scientific inquiry.
Linguistic Mechanism: Abstract or inanimate actors
Agency Strategy: Delegation
Power Analysis: This benefits the speaker's argument by lending it an air of impersonal, scientific objectivity. It's not just 'some researchers' who found something; an entire 'field' acts with a unified purpose. This framing makes the knowledge presented seem more stable, complete, and authoritative than it might be, discouraging challenges to the interpretation offered.
Interpretive Claim: This framing constructs scientific knowledge as an autonomous and objective product of an abstract field, erasing the human researchers and a more complex reality of academic debate and uncertainty.
Instance 4: Rendering Problem-Solving as an Impersonal Process​
Quote: "The challenges discussed here – privacy boundaries in embedding space, agentic common sense and agent-to-agent negotiation – are a few of the scientific hurdles that must be sorted out as AI becomes more autonomous."
Participant Analysis: 'The challenges' are the Goal (and grammatical Subject) of the clause. The process is material ('must be sorted out'). The Actor who must do the 'sorting out' (engineers, policymakers, ethicists, the public) is completely absent.
Agency Assignment: Agency is obscured through an agentless passive construction combined with a modal verb ('must be'). This presents the resolution of complex socio-technical issues as a necessary but agentless process.
Linguistic Mechanism: Agentless passive
Agency Strategy: Erasure
Power Analysis: This construction depoliticizes highly contentious issues. By removing the actors responsible for solving these 'hurdles', the text avoids questions of who should be involved in these decisions, whose values should be prioritized, and who is accountable if the solutions fail. It serves the interests of technologists who prefer to solve such problems internally, without public or regulatory oversight.
Interpretive Claim: This framing transforms contentious socio-technical problems into passive, technical hurdles to be overcome, erasing the human actors responsible for solving them and foreclosing debate about who gets to decide.
Instance 5: Institutionalizing Action to Obscure Individuals​
Quote: "...leaders should encourage their organizations to be explicit about the constraints they want agentic AI to obey..."
Participant Analysis: The participants are 'leaders' (Actor), 'organizations' (Actor in the embedded clause), and 'agentic AI' (Actor in the final embedded clause). The processes are mental ('encourage') and verbal ('be explicit'). While leaders are named, the actual work is delegated to the amorphous 'organizations'.
Agency Assignment: Agency is diffused. The responsibility for action is spread from specific, accountable 'leaders' to faceless, collective 'organizations'. The leader's role is merely to 'encourage,' a weak action, while the difficult task of defining constraints is assigned to an entire institution.
Linguistic Mechanism: Collective or institutional actors
Agency Strategy: Diffusion
Power Analysis: This diffuses accountability. If an organization fails to set proper constraints, it is a collective, bureaucratic failure, not the fault of any individual leader who did the 'encouraging'. It allows leaders to appear proactive while offloading direct responsibility for the complex and high-stakes task of implementation. This protects the managerial class from direct blame for subsequent failures.
Interpretive Claim: By diffusing responsibility from individual leaders to amorphous organizations, this construction creates an accountability shield for the managerial class.
Task 2: Values and Ideology Audit​
This task, the Values and Ideology Audit, functions as a Lexical Choice Analysis. It identifies specific words or phrases that carry ideological weight. The analysis examines features like semantic prosody (the 'aura' of a word), metaphorical framing, and cultural stereotypes. For each choice, the analysis provides several alternative framings to demonstrate how a different word would promote a different worldview, ultimately revealing the value system the original text reinforces and which perspectives it validates or marginalizes.
Instance 1: Framing Current AI as Child's Play​
Quote: "...“traditional” generative AI, like large language models (LLMs), provides a safe sandbox for individual users of chatbots..."
Lexical Feature Type: Metaphorical framing
Alternative Framings:
| Alternative Phrasing | Values Promoted |
|---|---|
| a limited-functionality tool | This emphasizes utility and technical limitations, promoting a value system of practical application and engineering realism. |
| a controlled testing environment | This highlights caution, risk management, and scientific procedure, promoting a value system of safety and systematic development. |
| a powerful information-processing system | This centers the technology's capabilities and potential impact, promoting a value system of technological power and significance. |
Value System: The 'safe sandbox' metaphor reinforces a value system of playful, harmless experimentation. It trivializes the current state of AI, framing it as a toy. This implicitly constructs the next stage ('agentic AI') as the 'real' or 'adult' version, creating a narrative of linear progress toward more powerful and consequential applications. It centers the value of innovation and advancement over the existing utility or risks of current systems.
Inclusion/Exclusion: This framing validates the perspective of developers and futurists who are focused on the next frontier of AI. It excludes and marginalizes the perspectives of those who see current LLMs as already powerful and potentially harmful tools with significant social, economic, and ethical implications. It appeals to an audience eager for the 'next big thing'.
Instance 2: Naturalizing Corporate Policy as 'Common Sense'​
Quote: "Here, a core challenge will be specifying and enforcing what we might call “agentic common sense”."
Lexical Feature Type: Euphemism or dysphemism
Alternative Framings:
| Alternative Phrasing | Values Promoted |
|---|---|
| programmable ethical constraints | This invokes a world of ethics, rules, and programming, suggesting a need for formal deliberation and moral reasoning. |
| corporate liability protocols | This centers legal and financial risk, promoting a value system where accountability and the consequences of failure are paramount. |
| user-defined operational boundaries | This emphasizes user control and customization, promoting a value system of individual autonomy and empowerment. |
Value System: This lexical choice reinforces a technocratic worldview where complex, contested social and ethical norms can be reduced to a set of simple, obvious rules ('common sense') that merely need to be articulated. It assumes a universal consensus on behavior that does not exist. It values efficiency and simplicity over messy negotiation and democratic debate about what constitutes acceptable automated behavior.
Inclusion/Exclusion: This framing includes the perspective of engineers and managers who seek clear, simple specifications for complex problems. It excludes and renders unthinkable the perspectives of ethicists, lawyers, and social scientists who would argue that 'common sense' is culturally specific, context-dependent, and often contradictory. It delegitimizes the idea that these 'policies' should be sites of public contestation.
Instance 3: Downplaying Risk with Euphemism​
Quote: "What will be some of the challenges with entrusting agentic AI with consequential actions like accessing sensitive data, or autonomously making financial transactions?"
Lexical Feature Type: Euphemism or dysphemism
Alternative Framings:
| Alternative Phrasing | Values Promoted |
|---|---|
| high-risk operations | This frames the actions through the lens of risk management, foregrounding the potential for negative outcomes and promoting caution. |
| legally liable decisions | This centers the legal framework, highlighting accountability and the potential for legal recourse if things go wrong. |
| potentially catastrophic failures | This uses stronger, more emotive language to emphasize the severity of the worst-case scenarios, promoting a value system of extreme prudence. |
Value System: The term 'consequential actions' is a sterile, neutral euphemism. It reinforces a value system of dispassionate, technical management. It acknowledges that the actions have effects, but it strips away the emotional and ethical weight of those effects (e.g., financial ruin, privacy violations). This values a detached, 'objective' perspective that is conducive to moving forward with development, rather than getting bogged down in fear or ethical quandaries.
Inclusion/Exclusion: This framing validates a managerial and technical audience that is trained to think in terms of abstract systems and consequences. It marginalizes the perspective of the individuals who would be directly affected by these actions, for whom the consequences are not abstract but deeply personal and potentially devastating. It excludes emotional and ethical language from the discussion.
Instance 4: Framing Knowledge as Physical Control​
Quote: "Eventually, we will have a deeper scientific grip on what information about the original data or content might be “leaked” by embeddings..."
Lexical Feature Type: Metaphorical framing
Alternative Framings:
| Alternative Phrasing | Values Promoted |
|---|---|
| a more thorough scientific understanding of | This promotes a value of intellectual comprehension and knowledge for its own sake. |
| more robust predictive models for | This values practical application and the ability to predict outcomes, an engineering-focused perspective. |
| stronger regulatory agreements concerning | This frames the problem as social and political, valuing governance, consensus, and public safety. |
Value System: The metaphor of a 'grip' frames scientific knowledge as a form of physical control and mastery. It reinforces a worldview where nature (or in this case, the behavior of an algorithm) is an object to be dominated and controlled through science. This values certainty, power, and finality in knowledge, suggesting that a problem, once 'gripped', is solved permanently.
Inclusion/Exclusion: This framing includes those who believe in a technocratic, solutionist approach to problems. It excludes perspectives that view knowledge as partial, provisional, and interpretive, or those who believe that the solution is not more technical control but rather social and political negotiation. It renders the idea of ongoing uncertainty or irreducible complexity as a temporary state to be overcome.
Instance 5: The Frontier Myth of Technological Progress​
Quote: "With agentic AI, we are in genuinely new territory, with few established scientific and engineering principles to set expectations."
Lexical Feature Type: Metaphorical framing
Alternative Framings:
| Alternative Phrasing | Values Promoted |
|---|---|
| we are facing a novel set of engineering risks | This promotes values of caution, safety, and risk assessment. |
| we are entering an unregulated operational domain | This highlights the legal and political vacuum, promoting a need for governance and oversight. |
| we are creating tools with unpredictable social outcomes | This centers the societal impact, promoting values of social responsibility and ethical foresight. |
Value System: The 'new territory' metaphor invokes the frontier myth, framing technological development as a form of exploration and pioneering. This value system prizes bravery, innovation, and discovery. It romanticizes the uncertainty and lack of rules, presenting it as an exciting opportunity rather than a dangerous liability. It assumes that this territory, like a literal frontier, is empty and available for conquest, ignoring the social world it will disrupt.
Inclusion/Exclusion: This framing validates the self-image of tech entrepreneurs and developers as pioneers and explorers. It appeals to an audience that values disruption and 'moving fast and breaking things'. It excludes and marginalizes the voices of those who would advocate for a more cautious, deliberate, and regulated approach, casting them as timid or anti-progress.
Task 3: Participant Positioning Audit​
This task, the Participant Positioning Audit, is an Interpersonal/Relational Analysis. It examines how the text positions participants (the speaker, the audience, and third parties) in relation to one another. It analyzes linguistic mechanisms like pronoun choice ('I', 'we', 'they'), register/formality, and how other voices are represented. The goal is to understand what social distance, authority, or solidarity is being constructed and whose perspective is being naturalized as 'common sense.'
Instance 1: Expert Positioning Through a Didactic Title​
Quote: "The science of agentic AI: What leaders should know"
Positioning Mechanism: Presupposition
Relationship Constructed: This constructs a clear hierarchy of knowledge. The text/author is positioned as the expert possessor of essential 'science'. The reader is positioned as a 'leader' but also as a student who is in need of this knowledge. The social distance is that of an expert briefing a powerful but less-informed client.
Whose Reality: The reality of the expert is naturalized. It's a world where there is a definitive 'science' of a new field and that this knowledge is crucial for leadership. It assumes that 'knowing' this science is the key to correct action. The concerns of leaders (presumably business or organizational) are centered.
Power Dynamics: This positioning reinforces the power of technical experts in corporate settings. It empowers the author (an Amazon Scholar) to define the terms of the conversation and to determine what is important for leaders to know. Leaders are empowered to act, but only after being educated by the expert.
Instance 2: Building an In-Group with Inclusive 'We'​
Quote: "We are at the dawn of yet another new era of AI, this time of the agentic variety."
Positioning Mechanism: Pronoun choice
Relationship Constructed: The use of 'we' creates solidarity and a shared identity between the speaker and the intended audience of 'leaders'. It constructs an in-group of informed, forward-looking participants who are together experiencing this 'new era'. This minimizes social distance and builds a sense of shared purpose and destiny.
Whose Reality: This 'we' normalizes the perspective of those inside the tech industry or in corporate leadership. Their reality—one of constant, successive 'new eras' of technological change—is presented as a universal experience. It centers their sense of being at the forefront of history.
Power Dynamics: This pronoun choice empowers the in-group ('we') by defining them as the protagonists of the story of technological progress. It implicitly creates an out-group ('they,' the uninformed public) who are not part of this journey. It reinforces the hierarchy between the technologically savvy and everyone else.
Instance 3: Managing Expectations with Expert Stance​
Quote: "...we can’t expect agentic AI to automatically learn or infer them from only a small amount of observation."
Positioning Mechanism: Epistemic stance markers
Relationship Constructed: The phrase 'we can’t expect' positions the speaker as a seasoned expert managing the expectations of a potentially naive audience. It establishes the speaker as someone with a deep, realistic understanding of the technology's limitations. This creates a relationship of trust, where the audience relies on the expert to distinguish hype from reality.
Whose Reality: The expert's reality, which includes a nuanced understanding of technical limitations, is presented as the only rational one. It presumes a shared goal of making the technology work, and the expert is guiding the 'leader' on how to achieve that. The concerns of a manager trying to implement a new technology are centered.
Power Dynamics: This positioning enhances the speaker's authority. By being the one to define what is and isn't possible ('we can't expect'), the speaker controls the boundaries of the conversation. It empowers the expert to set the agenda and define the problems that need to be solved, reinforcing the idea that the path forward is determined by technical constraints known only to them.
Instance 4: Positioning Social Problems as Technical Hurdles​
Quote: "The challenges discussed here – privacy boundaries in embedding space, agentic common sense and agent-to-agent negotiation – are a few of the scientific hurdles that must be sorted out..."
Positioning Mechanism: Register/formality level
Relationship Constructed: By labeling complex social and ethical issues ('privacy', 'common sense', 'negotiation') as 'scientific hurdles', the text positions them within the domain of science and engineering. This positions scientists and engineers as the legitimate problem-solvers and the audience ('leaders') as managers of a technical project.
Whose Reality: This naturalizes a technocratic worldview where all problems are ultimately reducible to technical specifications and can be solved by scientific methods. It centers the reality of engineers who work on solving defined 'hurdles' and marginalizes the realities of lawyers, ethicists, or policymakers who see these as contested social issues.
Power Dynamics: This move powerfully shapes governance. If privacy is a 'scientific hurdle', then scientists, not courts or legislatures, are the ones to 'sort it out'. This empowers the technical community and the corporations that employ them to self-regulate and create their own solutions, while disempowering and excluding other societal actors and forms of governance.
Instance 5: Assuming Shared Values Through Abstraction​
Quote: "Researchers often find striking yet consistent departures from what formal competitive equilibrium analysis would predict, including that humans often incorporate social considerations like fairness..."
Positioning Mechanism: Presupposition
Relationship Constructed: The text positions 'fairness' as a 'social consideration' that is a 'departure' from a baseline of 'formal competitive equilibrium analysis' and 'calculations of self-interest'. It presupposes that the audience agrees that rational self-interest is the default, and things like fairness are additions or deviations. This aligns the speaker and reader as rational, economically-minded analysts.
Whose Reality: This centers the reality of neoclassical economics, where rational, self-interested calculation is the default model of human behavior. It naturalizes this specific, culturally-bound economic model as the objective baseline from which human 'biases' like fairness depart. Concerns about social justice or equity are positioned as secondary 'considerations' rather than primary goals.
Power Dynamics: This reinforces the authority of economic and computational models of behavior. It empowers those who can perform 'formal competitive equilibrium analysis' and subtly delegitimizes arguments grounded primarily in 'social considerations like fairness' by framing them as deviations from a more fundamental, calculated reality. This can influence how an AI is designed—with self-interest as the core logic and fairness as an 'add-on'.
Task 4: Pattern Synthesis - Discourse Strategies​
This task, Pattern Synthesis - Discourse Strategies, identifies the overarching persuasive strategies that emerge from the micro-level findings in the previous tasks. This section connects specific linguistic features (from Tasks 1, 2, and 3) to show how they combine to form macro-patterns. Each strategy is analyzed for its function within the text and its broader ideological consequence.
Strategy 1: Constructing Technoscientific Inevitability​
Cited Instances:
- Personifying AI as an Autonomous Historical Actor
- The Frontier Myth of Technological Progress
- Positioning Social Problems as Technical Hurdles
Linguistic Patterns: This strategy combines the personification of AI as an autonomous agent that 'arrives' and 'matures' on its own (as in 'Personifying AI as an Autonomous Historical Actor') with metaphors of discovery and exploration (as in 'The Frontier Myth of Technological Progress'). This pairing creates a powerful narrative of technological development as a natural, unstoppable force. By then framing the resulting societal challenges as 'scientific hurdles' (as in 'Positioning Social Problems as Technical Hurdles'), the strategy completes the cycle: the force is natural and inevitable, and the only appropriate response is a technical, scientific one.
Textual Function: This strategy works to foreclose any fundamental debate about whether agentic AI should be developed or how it should be governed. It moves the conversation from 'if' to 'when' and 'how'. It requires the reader to accept the premise that technological advancement is a quasi-natural process outside the realm of democratic or political control. Any opposition is thus framed as irrational or backward-looking.
Ideological Consequence: The consequence is the depoliticization of technology. It naturalizes a worldview in which corporate-led innovation is the primary engine of history. This serves the interests of technology companies by creating a permissive environment for development, free from the constraints of regulation, public debate, or ethical deliberation that might slow it down.
Strategy 2: Forging an Expert-Managerial In-Group​
Cited Instances:
- Expert Positioning Through a Didactic Title
- Building an In-Group with Inclusive 'We'
- Erasure of the Advisor via Agentless Passive
Linguistic Patterns: This strategy begins by establishing a knowledge hierarchy through its title ('Expert Positioning Through a Didactic Title'), which separates the expert-author from the leader-student. It then immediately collapses this distance by using an inclusive 'we' ('Building an In-Group with Inclusive 'We''), creating a unified bloc of informed actors. This in-group is then given seemingly objective instructions through agentless passives ('Erasure of the Advisor via Agentless Passive'), which makes the expert's advice feel like a universal imperative for the group to follow.
Textual Function: This strategy constructs a specific subject position for the reader: a manager who is part of a knowledgeable elite, guided by expert advice to navigate an inevitable future. It builds solidarity and a sense of shared responsibility among this class, while implicitly excluding the general public, regulators, or workers from the conversation. The text functions as a briefing document for this ruling stratum.
Ideological Consequence: This reinforces a technocratic and managerial ideology where important societal decisions are best left to a small group of knowledgeable experts and powerful leaders. It naturalizes a social hierarchy where this in-group is empowered to make decisions on behalf of an absent and silent majority, thereby undermining democratic principles of participation and oversight.
Strategy 3: Naturalizing Corporate Governance as 'Common Sense'​
Cited Instances:
- Naturalizing Corporate Policy as 'Common Sense'
- Rendering Problem-Solving as an Impersonal Process
- Downplaying Risk with Euphemism
Linguistic Patterns: This strategy operates by taking complex, high-stakes issues and linguistically stripping them of their political and ethical content. It re-brands complex corporate risk policies as simple 'agentic common sense' ('Naturalizing Corporate Policy as 'Common Sense''). It then erases the human agents responsible for creating these policies, presenting their resolution as an impersonal necessity ('Rendering Problem-Solving as an Impersonal Process'). This is made more palatable by using sterile euphemisms like 'consequential actions' ('Downplaying Risk with Euphemism'), which minimize the perceived danger.
Textual Function: The textual function is to make the immense challenge of governing AI seem manageable, non-controversial, and fundamentally technical. It provides leaders with a vocabulary that transforms messy problems of power, ethics, and liability into clean engineering specifications. It enables a conversation about implementation while avoiding a conversation about fundamental principles or potential harms.
Ideological Consequence: This strategy promotes an ideology where corporate self-governance is sufficient and preferable to external regulation. By framing these issues as matters of 'common sense' to be 'sorted out' by the enterprise itself, it normalizes the idea that corporations are the best-suited actors to manage the risks of their own technologies, effectively arguing against the need for legal or democratic oversight.
Critical Observations​
Distribution of Agency​
The distribution of agency in this text is systematically asymmetrical to erase human accountability and naturalize technological determinism. Agency is consistently withdrawn from the human creators of AI—engineers, corporate strategists, investors—and delegated to abstract or personified entities. 'Generative AI' is an actor that 'arrives' and 'matures', while 'science' and 'the field' are agents that 'consider' and 'study'. In contrast, the human participants—'leaders' and 'enterprises'—are granted a highly circumscribed form of agency: not to create, direct, or question the technology, but merely to 'navigate' its arrival and 'encourage' their organizations to set 'constraints'. This pattern constructs a world where technology is an autonomous force of nature and humans are merely reactive managers. The ideological payoff is the near-total erasure of accountability; since no one is building it, no one can be held responsible for its consequences.
Naturalized Assumptions​
The text's argument rests on a bedrock of two powerful, naturalized assumptions. The first is a belief in linear, inevitable technological progress, framed as a series of 'new eras' arriving like the 'dawn'. This assumption is embedded in the text's entire framing and is never questioned. The second, and perhaps more crucial, assumption is that all problems associated with technology are fundamentally technical in nature. Challenges like privacy, fairness, and negotiation are not presented as political, ethical, or social contests to be resolved through democratic debate, but as 'scientific hurdles' requiring a 'deeper scientific grip'. For the text to be persuasive, a reader must accept that progress is a given and that science, not politics, is the appropriate arena for problem-solving. These assumptions serve to legitimize a technocratic elite as the sole arbiters of the future, while invalidating public participation or political oversight.
Silences and Absences​
The most profound feature of this text is its structural silence on power, profit, and people. There is a complete absence of any participant who is not a technologist or a corporate leader. Workers whose jobs may be automated, citizens whose data will be accessed, and consumers who will be subject to AI-driven negotiation are all missing. Conspicuously absent is any mention of government or regulatory bodies as relevant actors. Furthermore, the entire discourse is divorced from the political economy of AI development; the profit motive, market competition, and specific corporate interests of the author's employer (Amazon) are never mentioned. This strategic silence performs crucial ideological work: it frames the development of agentic AI as a pure, scientific endeavor for the abstract benefit of 'enterprises', rather than a strategic corporate project aimed at market dominance and profit extraction. The absence of society makes the technocratic solution appear not only logical but also uncontested.
Coherence of Ideology​
The ideological frame of the text is exceptionally coherent and resilient, with its components locking together in a self-reinforcing loop. The personification of AI as an autonomous agent (erasing human creators) logically necessitates the positioning of human leaders as reactive managers, not directors. This, in turn, requires that the problems AI creates are framed as technical 'hurdles' because managers overseeing a technical process need technical solutions. This technical framing then validates the authority of the expert (the 'Amazon Scholar') to provide the necessary guidance. The entire structure is built on the initial premise of technological inevitability. The system is so tightly woven that to question any single part—for instance, to suggest a problem is political, not technical—threatens to unravel the whole logic. This coherence makes the worldview difficult to challenge from within, as it presents a closed, internally consistent reality that marginalizes all alternative vocabularies and perspectives.
Conclusion​
Primary Strategies​
The text systematically constructs its reality through three interlocking discourse strategies. The foundational strategy is that of 'Technoscientific Inevitability,' which personifies AI and frames its development as a natural, unstoppable force, thereby removing it from the sphere of human choice. This strategy enables a second, 'Forging an Expert-Managerial In-Group,' which positions corporate leaders and technical experts as the sole legitimate actors responsible for navigating this preordained future. This in-group is then guided by a third strategy, 'Naturalizing Corporate Governance as Common Sense,' which transforms complex, high-stakes ethical and political decisions into simple technical problems of articulating 'constraints' and 'common sense.' The strategies work as a system: inevitability necessitates management by an elite, and this elite requires a depoliticized, technical vocabulary to do its work without external interference.
Construction of Social Reality​
Together, these linguistic strategies construct a sterile, depoliticized version of social reality. In this world, the primary agents of history are not people or social movements, but disembodied technologies and scientific fields. Human society is neatly divided into two groups: a small, knowledgeable class of experts and leaders who think and act, and an unmentioned, passive majority who are acted upon. Causality is driven by technological advancement, not political or economic interests. Problems are not sites of social conflict but are 'hurdles' to be overcome through more science and better management. The range of possible futures is narrowed to one: the successful implementation and adaptation to an inevitable technological shift. It is a world where the key questions are 'how' and 'when', but never 'why' or 'says who'.
Power Implications​
This constructed reality profoundly benefits the technology corporations, like the author's employer Amazon, that are driving the development of agentic AI. By framing technological advance as inevitable and its problems as purely technical, this discourse delegitimizes and preempts calls for public oversight, democratic debate, or governmental regulation. It centralizes power in the hands of a small technocratic class, empowering them to make decisions with vast societal consequences while remaining shielded from accountability. Policy options that might prioritize social stability, worker protections, or consumer rights over rapid deployment become unthinkable. The material consequence is a dramatic acceleration of corporate power to reshape society according to its own commercial interests, under the guise of neutral, scientific progress.
Alternative Constructions​
This reality is not inevitable; it is a product of deliberate linguistic choices. An alternative construction would emerge simply by redistributing agency. Instead of 'challenges must be sorted out', one could write, 'Citizens, regulators, and corporate developers must negotiate binding rules for these technologies'. This single change shatters the technocratic frame, introducing contingency, conflict, and a plurality of legitimate actors. Reframing 'agentic common sense' as 'corporate liability frameworks' would shift the focus from a vague, naturalized concept to the concrete political and legal question of who pays when an autonomous agent causes harm. Centering the perspective of a worker facing displacement would replace the sterile language of 'transition' with the lived experience of economic disruption. Such alternatives would construct a reality where the future of technology is not a 'territory' to be conquered by elites, but a terrain of social contestation to be navigated by a whole society.
Raw JSON: 2025-11-09-the-science-of-agentic-ai-what-leaders-should-know-cda-soft-795eb9.json
Framework: cda-soft v2.1
Generated: 2025-11-09T11:31:35+00:00Z
Discourse Depot © 2025 by TD is licensed under CC BY-NC-SA 4.0
Extended Processing Summary​
Gemini refers to the text below as “thought summaries.” Let’s just call it a model-generated process trace. Treat this as a just anotherrhetorical artifact —a way of making the model’s processing legible. The first-person framing of these “thought summaries” is a presentation choice for the user-facing output, not a window into “real”thoughts. These are computational artifacts, not cognitive reports from a quirky, curious or conflicted mind.
Initiating Analysis: Textual Review
I'm currently immersed in a close reading of the "agentic AI" text, seeking to discern its core arguments and intended audience. The Amazon Scholar Q&A format presents interesting challenges. I'm focusing on identifying key discursive strategies and potential biases within the language employed. This initial phase involves comprehensive note-taking and preliminary coding of significant phrases and recurring themes. Next steps will be mapping the text's argumentative structure.
Deconstructing the Discourse: Deep Dive
I'm now conducting a Critical Discourse Analysis, focusing on the text's discursive strategies. My initial read-through revealed an authoritative, forward-looking tone aimed at enterprise leaders. I'm actively identifying instances of agency manipulation, including passive voice, nominalization, and the use of abstract or collective actors. The goal is to understand how these linguistic choices shape the perception of agency and accountability in the context of "agentic AI." I've already begun identifying potential examples. I aim to uncover the underlying power dynamics.
Analyzing Structure: Detailed Review
I've completed my initial audit of agency manipulation, identifying and analyzing two instances in detail. I'm focusing on the linguistic mechanisms employed and their impact on attributing agency and accountability. Currently, I'm working through the remaining instances, paying close attention to how the text positions technology as an active agent while obscuring human involvement. My analysis reveals a consistent pattern of erasure and delegation, subtly shaping the narrative around the development and implementation of "agentic AI." I'm also carefully considering the intended effects of these choices on the target audience.
Examining Erasure: Linguistic Audit
I've completed my initial audit, identifying five key instances of agency manipulation within the text. My analysis highlights recurring strategies of erasure and delegation, where human actors and responsibilities are obscured or attributed to abstract entities. This subtle shift in agency shapes the narrative around "agentic AI" in ways that warrant further investigation. I'm now analyzing the implications of these linguistic choices. I'm focusing on the effect on the target audience.
Initiating Analysis: Textual Review
I've just begun a comprehensive analysis of the "agentic AI" text, focusing on identifying the core arguments and intended audience. The initial reading reveals the Q&A format, presenting both opportunities and challenges. I'm focusing on the discursive strategies and potential biases within the language, noting significant phrases and recurring themes. I'll begin to map the argumentative structure next.
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I'm now diving into the article's discourse, identifying strategic linguistic choices. I've begun to audit agency manipulation and have found several examples. This includes passive voice and nominalization. The goal is to uncover the power dynamics at play. The authoritative, forward-looking tone creates an illusion of inevitable progress. I will identify the role of technology and science as active agents.
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My analysis of agency manipulation continues, with three more instances identified and categorized. The pattern of obscuring human agency persists. I am carefully considering the implications of linguistic choices made. Furthermore, I've started the process of analyzing values and ideology by identifying lexical choices with strong connotations. The goal is to see how the text shapes the narrative around the development and implementation of "agentic AI".
Revising Analysis: Structural Refinement
I'm now refining my analysis, focusing on how specific linguistic choices construct meaning. I've reviewed and revised all five initial agency manipulation instances, strengthening my explanations. My goal is to ensure each instance is clearly linked to its interpretive claim. Next, I'll turn my attention to the values and ideology audit. I'm focusing on the effect these word choices have on the narrative about the implementation of the AI.
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My critical discourse analysis advances; I've expanded my review of agency manipulation, reinforcing the interpretation of each instance. I've begun an in-depth analysis of the values and ideology audit. Furthermore, I've started the process of analyzing values and ideology by identifying lexical choices with strong connotations. The goal is to see how the text shapes the narrative around the development and implementation of "agentic AI".
Refining Analysis: Ideological Impacts
I'm now deeply engaged in refining my analysis, particularly focusing on the ideological implications of the linguistic choices. I'm carefully reviewing the relationship between my instances. The instances are those identified during the agency audit and the values audit. I've begun to consider the potential societal consequences that the use of "consequential actions" might have on the narrative surrounding AI.
Examining Erasure: Linguistic Audit
I'm now fully immersed in refining my analysis, particularly focusing on the ideological implications. I'm carefully reviewing the relationship between my agency audit and my values audit, and how these elements reinforce each other. I'm pondering the potential societal implications of "consequential actions" within the broader AI narrative. Furthermore, I've begun to consider the audience's role in the "new era" of AI.
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My critical discourse analysis of the text continues. I'm focusing on my agency manipulation and values audits and how these combine to construct the text's persuasive power. I'm now integrating participant positioning into my analytical framework, concentrating on how the text positions AI and its human actors. The author and the readers are both positioned to be active in the new era. The positioning creates a sense of shared destiny.
Reviewing the Analysis: Expanding Insights
I'm now expanding my agency analysis to five instances. This includes passive voice, nominalization, and abstract actors. My analysis reveals five key moments where agency is manipulated. This strategic audit further reveals the patterns of erasure and delegation. Furthermore, I've begun to consider the audience. I am focusing on how the text positions AI and its human actors.
Mapping the Narrative: Audit & Analysis
I'm now integrating my analysis, with a focus on synthesizing the patterns across my audits. I've successfully completed the five instances for each section of the CDA, agency, values, and participant positioning, and I am now beginning my synthesis. This is a critical step to identify overarching strategies. I'm carefully reviewing the relationships between the instances. The strategic combination of agency manipulation, value-laden language, and audience positioning, creates a coherent argument.
Refining Analysis: Ideological Impacts
I'm now fully immersed in refining my analysis, particularly focusing on the ideological implications of the linguistic choices. I'm carefully reviewing the relationship between my agency and values audits, and how these elements reinforce each other. I'm pondering the potential societal implications of "consequential actions" within the broader AI narrative. Furthermore, I've begun to consider the audience's role in the "new era" of AI.
Synthesizing Findings: Cross-Audit Review
I'm now integrating my analysis, with a focus on synthesizing the patterns across my audits. I've successfully completed the five instances for each section of the CDA: agency, values, and participant positioning. This is a crucial step to identify overarching strategies. I'm carefully reviewing the relationships between the instances. The strategic combination of agency manipulation, value-laden language, and audience positioning creates a compelling argument.
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The analysis continues to deepen. I'm now comparing and contrasting findings from the three audits to identify overarching patterns. The initial findings highlight the construction of expert authority and the naturalization of corporate policies. I've identified five distinct combinations within my analyses.
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My critical discourse analysis advances. I'm integrating the insights from my agency, values, and participant positioning audits. My current focus is to identify the recurring discursive strategies employed to construct a specific narrative. I've begun to discern several overarching strategies emerging from the synthesis of my three audits.
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I'm now in the final stage of my analysis, where I will integrate the analysis from my agency, values, and participant audits to identify the major themes and discursive strategies. Through the systematic review, the strategic combination of agency manipulation, value-laden language, and audience positioning, creates a coherent argument that aligns with the overall messaging.
Modeling Reality: Strategic Audit
I've completed my initial audit, highlighting instances of agency manipulation, values, and ideological implications, and begun mapping the discursive strategies. I've noted how the text positions technology as the active agent while obscuring human involvement. I'm now synthesizing these findings. I'm evaluating the impacts.
Reviewing the Analysis: Cross-Audit Review
I'm now integrating my analysis, focusing on how these elements create the text's persuasive power. I've begun to synthesize insights from all audits, identifying patterns. My analysis focuses on how agency manipulation, value-laden language, and audience positioning, create a compelling argument. This helps me find overarching strategies.
\n\n My detailed analysis continues, focusing on overarching strategies, and the construction of expert authority. I'm focusing on the synthesis of the strategies within my critical discourse analysis framework. The focus is to identify the overall effectiveness. I've identified recurring discursive strategies employed to construct a specific narrative. The analysis has progressed well.
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My critical discourse analysis advances. I'm now comparing and contrasting findings from the three audits to identify overarching patterns. The goal is to provide a complete picture of how the text shapes the narrative. I'm integrating the insights from my agency, values, and participant positioning audits.
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My analysis is almost complete, and I'm focused on the synthesis from my prior work. I've completed an analysis of each section of my CDA: agency, values, and participant positioning audits. I'm now integrating my analysis, with a focus on synthesizing the patterns across my audits.
Analyzing Strategies: Audit & Review
I'm now integrating my analysis, with a focus on synthesizing the patterns across my audits. I've successfully completed the five instances for each section of the CDA, agency, values, and participant positioning, and I am now beginning my synthesis. This is a critical step to identify overarching strategies. I'm carefully reviewing the relationships between the instances. The strategic combination of agency manipulation, value-laden language, and audience positioning, creates a coherent argument.
Analyzing Narratives: Strategy Audit
I'm now integrating my analysis, and synthesizing all the audits together. The audit confirms how a technoscientific narrative is being created. The strategy highlights the agency. The construction of the authority is centered around the author and the audience. I'm focusing on the effect.