The science of agentic AI - What leaders should know

About

This document presents a Critical Discourse Analysis focused on AI literacy, specifically targeting the role of metaphor and anthropomorphism in shaping public and professional understanding of generative AI. The analysis is guided by a prompt that draws from cognitive linguistics (metaphor structure-mapping) and the philosophy of social science (Robert Brown's typology of explanation). 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.

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Analysis Metadata

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: 1.15 Tokens: input=1010, output=10803, total=11813 Source Type: brief Published: Analyzed At: 2025-11-09T11:29:43+00:00Z Framework: metaphor Framework Version: 4.0

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Task 1: Metaphor and Anthropomorphism Audit

Description

For each of the major metaphorical patterns identified, this audit examines the specific language used, the frame through which the AI is being conceptualized, what human qualities are being projected onto the system, whether the metaphor is explicitly acknowledged or presented as direct description, and—most critically—what implications this framing has for trust, understanding, and policy perception.

1. AI as an Autonomous, Intentional Actor

Quote: "agentic AI will use LLMs as a starting point for intelligently and autonomously accessing and acting on internal and external resources such as databases, financial accounts and transactions, travel services and more."

• Frame: Model as an independent agent
• Projection: The human qualities of autonomy, intelligence, and deliberate action are projected onto the AI system's operations.
• Acknowledgment: Unacknowledged. Presented as a direct description of the technology's function.
• Implications: This framing establishes the AI as a proactive entity, not a tool. It elevates its status from a passive information processor to an active participant in consequential domains, which can lead to overestimation of its capabilities and an underestimation of the risks associated with its automated execution of complex tasks.

2. AI as an Obedient Subordinate

Quote: "enterprises are advised to provide explicit instructions or prompts to agentic AI... such an agent should be told to never share my broader financial picture..."

• Frame: Model as a subordinate that understands instructions
• Projection: The human capacity for understanding and obeying semantic commands, especially negative constraints ('never share').
• Acknowledgment: Unacknowledged. The language treats instructing an AI as analogous to instructing a human assistant.
• Implications: This metaphor simplifies the complex and brittle process of programming constraints into a simple act of 'telling.' It creates a false sense of security, implying that natural language instructions are sufficient to create robust safety boundaries, while obscuring the technical reality of rigorous, formal specification and testing required to prevent failures.

3. AI as Possessing Human Intuition

Quote: "Here, a core challenge will be specifying and enforcing what we might call “agentic common sense”."

• Frame: Model as a being with social intuition
• Projection: The deeply ingrained, culturally learned, and contextually aware judgment that constitutes human common sense.
• Acknowledgment: Partially acknowledged through scare quotes around "agentic common sense," but the term itself frames the problem in a deeply anthropomorphic way.
• Implications: Framing the challenge as one of 'specifying common sense' suggests it is a knowable, codifiable thing that can be taught to a machine. This misrepresents the problem. The real challenge is creating systems that are robust to the infinite edge cases that human common sense handles implicitly. This frame makes the problem seem more tractable than it is, potentially leading to premature deployment of systems in unpredictable environments.

4. AI as a Cognitive Being That Learns and Infers

Quote: "we can’t expect agentic AI to automatically learn or infer them [informal behaviors] from only a small amount of observation."

• Frame: Model as a mind that learns like a human
• Projection: The human cognitive processes of learning (gaining knowledge through experience) and inference (drawing logical conclusions from evidence).
• Acknowledgment: Unacknowledged. The words 'learn' and 'infer' are used as direct descriptors of the AI's process, implying a human-like cognitive mechanism.
• Implications: This language implies the AI has a generalizable learning capability that mirrors human cognition. While the sentence is a caution, its anthropomorphic framing subtly suggests that with more observation, it could learn and infer like a human. This obscures the fact that the model's 'learning' is statistical pattern-matching, not the development of abstract understanding, making it prone to nonsensical errors that a human would never make.

5. AI as a Skilled Human Negotiator

Quote: "Sometimes we will want agentic AI to not just execute transactions on our behalf, but to negotiate the best possible terms."

• Frame: Model as a strategic bargainer
• Projection: The complex human skill of negotiation, which involves strategic thinking, empathy, understanding unspoken cues, and balancing competing interests.
• Acknowledgment: Unacknowledged. Presented as a straightforward capability to be desired and implemented.
• Implications: This framing inflates the AI's capability from a transactional tool to a strategic partner. It suggests the AI can represent a user's interests in a dynamic, adversarial context. This creates unrealistic expectations and hides the risk that the AI, by optimizing for a narrowly defined 'best term' (e.g., price), might ignore other critical factors (e.g., quality, vendor reliability, ethical considerations) that a human negotiator would intuitively balance.

6. AI as a Social Actor with Moral Considerations

Quote: "humans often incorporate social considerations like fairness into what otherwise might be purely calculations of self-interest... we might expect agentic AI to behave similar to people in economic settings..."

• Frame: Model as a social being with values
• Projection: The human capacity to possess and act upon social and ethical values like 'fairness'.
• Acknowledgment: Unacknowledged. The expectation of 'fair' behavior is presented as a natural consequence of the training data.
• Implications: This suggests that complex ethical behaviors like fairness can be passively absorbed from data, creating a dangerously misleading equivalence between pattern-matching human text and possessing genuine ethical reasoning. It encourages over-trust in the model's 'moral compass' and abdicates responsibility from developers to explicitly design and test for fair outcomes, potentially leading to systems that replicate and amplify societal biases under a veneer of emergent 'fairness'.

Task 2: Source-Target Mapping

Description

For each key metaphor identified in Task 1, this section provides a detailed structure-mapping analysis. The goal is to examine how the relational structure of a familiar "source domain" (the concrete concept we understand) is projected onto a less familiar "target domain" (the AI system). By restating each quote and analyzing the mapping carefully, we can see precisely what assumptions the metaphor invites and what it conceals.

Mapping 1: Human Agent to AI System Operation

Quote: "agentic AI will use LLMs as a starting point for intelligently and autonomously accessing and acting on internal and external resources..."

• Source Domain: Human Agent
• Target Domain: AI System Operation
• Mapping: The relational structure of a person making choices and taking actions in the world (autonomy, intelligence, acting) is mapped onto the AI's process of executing code based on triggers and inputs. The AI is framed as the subject performing the action.
• What Is Concealed: This mapping conceals the fact that the AI has no will, desire, or consciousness. Its 'actions' are predetermined outputs of a computational process. It obscures the role of the human programmers who designed the system and the constraints of the data it was trained on, attributing the locus of control to the artifact itself.

Mapping 2: Human Instruction/Command to System Configuration/Programming

Quote: "...such an agent should be told to never share my broader financial picture..."

• Source Domain: Human Instruction/Command
• Target Domain: System Configuration/Programming
• Mapping: The social interaction of telling a subordinate a rule is mapped onto the technical process of setting a parameter or writing a line of code for a software system. The mapping implies comprehension and compliance on the part of the AI.
• What Is Concealed: It conceals the brittleness of the instruction. A human understands the intent behind 'never share my financial picture' and can apply it to novel situations. The AI only understands a specific, programmed constraint and can easily fail if a situation arises that isn't perfectly covered by the rule (e.g., sharing data that allows the financial picture to be inferred). It hides the massive technical overhead required to make such a 'rule' robust.

Mapping 3: Human Common Sense to AI Heuristics and Guardrails

Quote: "Here, a core challenge will be specifying and enforcing what we might call “agentic common sense”."

• Source Domain: Human Common Sense
• Target Domain: AI Heuristics and Guardrails
• Mapping: The vast, implicit, and context-aware knowledge base that humans use to navigate the world is mapped onto a set of explicit, formal rules to be programmed into an AI. It suggests that common sense is a body of knowledge to be transferred, rather than an emergent property of embodied experience.
• What Is Concealed: This mapping conceals the fundamental difference between tacit knowledge and explicit information. It hides the impossibility of ever fully specifying the millions of unwritten rules that govern human interaction. It reframes an intractable problem (creating genuine understanding) as a merely difficult one (codifying common sense).

Mapping 4: Human Learning/Inference to Statistical Pattern-Matching

Quote: "...we can’t expect agentic AI to automatically learn or infer them [informal behaviors] from only a small amount of observation."

• Source Domain: Human Learning/Inference
• Target Domain: Statistical Pattern-Matching
• Mapping: The cognitive process of a human observing behavior and abstracting general principles from it is mapped onto a model's process of adjusting its internal weights based on data input. It equates statistical correlation with conceptual understanding.
• What Is Concealed: It conceals that the model is not 'learning' or 'inferring' in a human sense. It has no model of the world, no understanding of causality, and no ability to generalize outside of its training distribution. This makes its 'learning' superficial and prone to nonsensical errors that reveal a total lack of true comprehension.

Mapping 5: Human Negotiation to Multi-objective Optimization

Quote: "...we will want agentic AI to not just execute transactions on our behalf, but to negotiate the best possible terms."

• Source Domain: Human Negotiation
• Target Domain: Multi-objective Optimization
• Mapping: The strategic, psychological, and social activity of human negotiation is mapped onto a computational process of optimizing a predefined utility function (e.g., minimizing cost, maximizing speed). The AI is framed as a skilled bargainer.
• What Is Concealed: It conceals the simplified nature of the AI's 'negotiation.' A human negotiator considers reputation, long-term relationships, non-monetary value, and social context. The AI optimizes only for the variables it was given, potentially leading to 'wins' that are pyrrhic because they damage relationships or ignore crucial unquantified factors. It hides the AI's lack of true strategic thought.

Mapping 6: Human Social Behavior to AI Output Generation

Quote: "...we might expect agentic AI to behave similar to people in economic settings – indeed, there is already a small but growing body of research confirming this phenomenon."

• Source Domain: Human Social Behavior
• Target Domain: AI Output Generation
• Mapping: The behavior of humans in social contexts, driven by complex psychology, cultural norms, and internal states (like a sense of fairness), is mapped onto the text output of a language model. It suggests the model's output is an expression of an internal state similar to a human's.
• What Is Concealed: It conceals that the AI is merely mimicking patterns from its training data. It doesn't have a sense of fairness; it generates text that is statistically similar to human text that discusses fairness. This mimicry can be shallow and inconsistent. The mapping hides the absence of genuine subjectivity, intentionality, or ethical grounding.

Task 3: Explanation Audit (The Rhetorical Framing of "Why" vs. "How")

Description

This section audits the text's explanatory strategy, focusing on a critical distinction: the slippage between "how" and "why." Based on Robert Brown's typology of explanation, this analysis identifies whether the text explains AI mechanistically (a functional "how it works") or agentially (an intentional "why it wants something"). The core of this task is to expose how this "illusion of mind" is constructed by the rhetorical framing of the explanation itself, and what impact this has on the audience's perception of AI agency.

Explanation 1

Quote: "LLMs do not operate directly on the words, sentences and images we use to communicate. They instead compute and manipulate abstract representations of such content (known as embeddings) meant to preserve similarity of meaning."

• Explanation Types:
  • Theoretical: Embeds behavior in a deductive or model-based framework, may invoke unobservable mechanisms such as latent variables or attention dynamics
  • Functional: Explains a behavior by its role in a self-regulating system that persists via feedback, independent of conscious design
• Analysis: This is a purely mechanistic explanation of how the system works. It uses a Theoretical framework (embeddings in latent space) to describe the function (preserving similarity of meaning) of a core component. There is no agential language here; the LLM 'computes and manipulates,' which are mechanical processes. This passage serves to ground the concept in scientific language before the text pivots to more anthropomorphic descriptions.
• Rhetorical Impact: This framing establishes technical credibility with the audience of 'leaders.' By starting with a seemingly sophisticated, mechanistic explanation, it lends an air of scientific authority to the subsequent, more speculative and agential claims. It makes the technology seem understandable and grounded, even as the later descriptions become highly metaphorical.

Explanation 2

Quote: "Thus, when content or context are shared across agentic AI systems, drawing precise boundaries around sensitive or private information like financial data will require careful handling."

• Explanation Types:
  • Empirical Generalization (Law): Subsumes events under timeless statistical regularities, emphasizes non-temporal associations rather than dated processes
• Analysis: This explanation functions as a general rule or law about the system's behavior: when embeddings are shared, then drawing boundaries is hard. This explains how a problem arises from the system's architecture. However, the phrasing 'drawing precise boundaries' begins a subtle shift. It frames the problem as a human action on the system, but it sets the stage for the agential idea that the AI itself might fail to respect these boundaries.
• Rhetorical Impact: This passage frames a fundamental technical limitation as a manageable operational challenge ('requires careful handling'). It normalizes the risk, making it seem like a matter of procedure rather than a deep, unsolved research problem. This reassures leaders that the risks are known and can be mitigated through process, rather than requiring a fundamental change in the technology.

Explanation 3

Quote: "we can’t expect agentic AI to automatically learn or infer them [informal behaviors] from only a small amount of observation."

• Explanation Types:
  • Dispositional: Attributes tendencies or habits such as inclined or tends to, subsumes actions under propensities rather than momentary intentions
  • Genetic: Traces origin or development through a dated sequence of events or stages, showing how something came to be
• Analysis: This explanation slips from how to why. The genetic part explains how the AI 'learns' (from observation), but the framing is dispositional, attributing a tendency or capacity ('to learn,' 'to infer') to the AI. It explains why the AI fails (insufficient observation) by appealing to a human-like learning process. It obscures the mechanistic reality that the model lacks the architecture for genuine inference, regardless of the amount of data.
• Rhetorical Impact: This framing subtly manages expectations while preserving the AI's perceived intelligence. By blaming the failure on 'only a small amount of observation,' it implies that the AI has the inherent capacity to learn common sense, and the problem is merely one of scale. This encourages continued investment and experimentation under the belief that the limitation is temporary, not fundamental.

Explanation 4

Quote: "Given that LLMs are trained on human-generated data, we might expect agentic AI to behave similar to people in economic settings..."

• Explanation Types:
  • Genetic: Traces origin or development through a dated sequence of events or stages, showing how something came to be
  • Empirical Generalization (Law): Subsumes events under timeless statistical regularities, emphasizes non-temporal associations rather than dated processes
• Analysis: This is a hybrid explanation that uses a mechanistic cause (how it's made: trained on human data) to justify an agential prediction (why it acts a certain way: it will behave like people). The slippage is in the verb 'behave.' The explanation moves from the origin of the data (genetic) to a general law about its output (empirical generalization), but the result is described as human-like behavior, implying intent, social awareness, and psychological similarity.
• Rhetorical Impact: This framing creates a powerful and appealing justification for trusting the AI in complex social situations. It suggests that, by its very nature, the AI will inherit a type of human wisdom or reasonableness. This lowers the perceived risk of deploying it in roles like negotiation, as it reassures leaders that its actions will be recognizably human and thus predictable and understandable.

Explanation 5

Quote: "...ask the AI to check with humans in the case of any ambiguity."

• Explanation Types:
  • Intentional: Refers to goals or purposes and presupposes deliberate design, used when the purpose of an act is puzzling
  • Reason-Based: Gives the agent’s rationale or argument for acting, which entails intentionality and extends it by specifying justification
• Analysis: This explanation is almost entirely agential, prescribing a solution that treats the AI as a being with intention and reason. The phrase 'ask the AI to check' implies the AI can recognize its own state of 'ambiguity' (a form of metacognition) and then form the intention to consult a human. This is a clear explanation of why the AI should act (to resolve ambiguity), framed as if the AI has a mind that can reason about its own uncertainty.
• Rhetorical Impact: This makes the solution to AI risk seem incredibly simple and intuitive. It frames safety as a conversational or managerial task ('just ask it to check with you') rather than a complex engineering one. It gives leaders a false sense of control, making them feel they can manage an autonomous agent through simple directives, much like a human employee, thereby obscuring the immense difficulty of programming reliable uncertainty-detection and escalation protocols.

Task 4: AI Literacy in Practice: Reframing Anthropomorphic Language

Description

Moving from critique to constructive practice, this task demonstrates applied AI literacy. It selects the most impactful anthropomorphic quotes identified in the analysis and provides a reframed explanation for each. The goal is to rewrite the concept to be more accurate, focusing on the mechanistic processes (e.g., statistical pattern matching, token prediction) rather than the misleading agential language, thereby providing examples of how to communicate about these systems less anthropomorphically.

Original Quote	Mechanistic Reframing
"agentic AI will use LLMs as a starting point for intelligently and autonomously accessing and acting on internal and external resources..."	Systems designated as 'agentic AI' will use LLMs to generate sequences of operations that automatically interface with other software and data sources.
"...such an agent should be told to never share my broader financial picture..."	The system's operating parameters must be configured with explicit, hard-coded rules that prevent it from accessing or transmitting financial data outside of a predefined transactional context.
"Here, a core challenge will be specifying and enforcing what we might call “agentic common sense”."	A core challenge will be engineering a vast and robust set of behavioral heuristics and exception-handling protocols to ensure the system operates safely in unpredictable environments.
"...we can’t expect agentic AI to automatically learn or infer them [informal behaviors] from only a small amount of observation."	Current models cannot reliably generalize abstract social rules from small datasets; their output is based on statistical pattern-matching, which does not equate to inferential reasoning.
"...we will want agentic AI to... negotiate the best possible terms."	We will want to configure these automated systems to optimize for specific, measurable outcomes within a transaction, such as minimizing price or delivery time.
"we might expect agentic AI to behave similar to people in economic settings..."	Because these models are trained on text describing human interactions, their text outputs may often mimic the patterns found in human economic behavior.
"...ask the AI to check with humans in the case of any ambiguity."	The system should be designed with uncertainty quantification mechanisms that trigger a request for human review when its confidence score for an action falls below a specified threshold.

Critical Observations

Description

This section synthesizes the findings from the previous tasks into a set of critical observations. It examines the macro-patterns of agency slippage (the shift between treating AI as a tool vs. an agent), how cognitive metaphors drive trust or fear, and what actual technical processes are obscured by the text's dominant linguistic habits.

Agency Slippage

The text systematically oscillates between mechanistic and agential framing, a rhetorical strategy that serves to build credibility and then translate it into a compelling vision of autonomous capability. The oscillation is not random; it follows a distinct pattern of mechanical→agential slippage. The piece begins by grounding the technology in the complex, non-intuitive mechanics of 'embeddings' and 'abstract representations.' This initial framing is technical and objectifying, treating the LLM as a computational artifact. It serves as a scientific anchor, assuring the leadership audience that the discussion is based on rigorous engineering. However, once this foundation is laid, the text almost immediately pivots to a deeply agential frame. For instance, the challenge of data leakage from embeddings is reframed as a problem of needing to 'tell' an 'agent' what not to share. The discussion of system limitations similarly starts with a quasi-technical constraint—difficulty generalizing from small data—but is articulated using the cognitive verbs 'learn' and 'infer.' This consistent mechanical→agential directionality performs a crucial rhetorical function: it launders the unfamiliar and potentially alienating nature of the technology through the legitimizing language of science, and then re-presents its function in familiar, human-centric terms. The strategic function of this ambiguity is to make a radical technological leap seem like a natural, manageable evolution. By describing the AI as an 'agent' that can be 'told' things and can 'negotiate,' it makes the technology legible and controllable to a non-technical leader. The active voice ('agentic AI will... act') dominates when describing capabilities, while passive or cautionary framings appear when discussing risks, yet even these warnings are couched in agential terms ('ask the AI to check'). This slippage appears deliberate, designed to inspire confidence and excitement while framing the immense associated risks as simple matters of management and instruction, akin to onboarding a new, slightly naive employee.

Metaphor-Driven Trust

The text leverages biological and cognitive metaphors not merely to explain, but to transfer credibility and build trust in a technology that is inherently abstract and unpredictable. The most potent metaphors—'common sense,' 'learning,' 'negotiating,' and 'fairness'—function by borrowing the deep-seated cultural authority and reliability of the human faculties they name. The 'agentic common sense' metaphor is particularly powerful. Human common sense is the bedrock of social trust; it is the implicit guarantee that others will act in predictable, reasonable ways. By framing the AI's safety problem as one of instilling 'common sense,' the text suggests the system can achieve a similar level of intuitive reliability. This makes the risky proposition of granting autonomy to the AI seem plausible, activating a leader's belief in manageable, sensible behavior. Similarly, 'negotiation' borrows from the concept of a loyal, skilled human advocate working on one's behalf. It reframes a brittle optimization process as a sophisticated act of representation, building trust that the AI's actions will be aligned with the user's best interests. This becomes especially credible to a business audience accustomed to relying on agents and delegates. These metaphors make counterintuitive claims believable. The claim that a system which only matches statistical patterns can act with 'fairness' would be difficult to accept if stated in mechanical terms ('the system's output statistically correlates with text labeled as fair'). But by projecting the human concept of fairness onto the machine, the text encourages the audience to trust that the AI has an emergent ethical compass. This creates long-term vulnerability. When a system framed as having 'common sense' makes a nonsensical and catastrophic error, the resulting backlash is not just disappointment but a feeling of betrayal. The metaphor creates an expectation of genuine understanding, so its inevitable failure to meet that standard is perceived not as a technical limitation but as a breach of trust, potentially leading to hasty and ill-conceived policy responses.

Obscured Mechanics

The pervasive use of anthropomorphic metaphors systematically conceals the mechanical realities and inherent limitations of agentic AI systems, creating a dangerously incomplete picture for decision-makers. Each metaphor acts as a lens that brings a human-like capability into focus while pushing the complex, messy, and often fallible engineering into the shadows. The 'agentic common sense' metaphor is a prime example. It completely hides the astronomical difficulty of creating robust safety systems. What is obscured is the 'brittle-rules problem': the need for human engineers to anticipate and manually code thousands of explicit constraints and exception-handling routines to prevent foreseeable failures. The metaphor suggests a flexible, general intelligence, while the reality is a rigid, hand-curated logic tree. It also conceals the immense human labor—from ethicists to red-teamers—required to even attempt to approximate this 'common sense.' The 'negotiation' metaphor similarly conceals the underlying mechanics of multi-objective optimization. It hides the fact that the AI has no true understanding of the negotiation's context or stakes. It cannot, for instance, know that accepting a slightly higher price from a reliable, long-term supplier is better than taking the absolute lowest price from a fly-by-night operator unless those specific variables (reliability, etc.) have been painstakingly quantified and included in its utility function. The metaphor obscures the critical role of human judgment in defining the very terms of 'success' for the AI. Furthermore, the overall framing of intelligent, autonomous agents obscures the system's fundamental dependency on vast, often problematic training data and immense computational resources. The environmental cost, the embedded biases of the training data, and the system's inability to function outside the statistical patterns of that data are all rendered invisible. If these metaphors were replaced with mechanical language—'The system will execute a sequence of pre-programmed heuristics to optimize for price within a set of user-defined constraints' instead of 'The agent will negotiate for you'—the audience's understanding would shift dramatically. The system's limitations, its dependency on its programming, and the locus of responsibility (the human designer) would become immediately apparent, forcing a more sober assessment of its true capabilities and risks.

Context Sensitivity

The deployment of metaphor in this text is not uniform but highly sensitive to its context, audience, and rhetorical goals, revealing a sophisticated strategy for persuading a leadership audience. The text operates in several registers, and the density of anthropomorphism varies accordingly. It opens with a technical register to establish credibility, describing 'embeddings' and 'abstract representations' in mechanistic terms. Here, metaphor is sparse. This creates a foundation of scientific rigor. However, once the text pivots to discussing the application and implications of the technology—the sections most relevant to a business leader—the metaphor density skyrockets. Capabilities are almost exclusively described using agential language: 'intelligently and autonomously accessing and acting,' 'negotiate the best possible terms.' This shift is strategic. The initial technical language serves as a gateway, and once the audience's trust is secured, the text transitions to a more persuasive, visionary register that uses anthropomorphism to make the technology's value proposition clear and compelling. The use of metaphor is also predictive. High-stakes capabilities that involve autonomy and interaction with the real world (finance, negotiation) are described with the most powerful human metaphors. In contrast, discussions of risk and mitigation, while still using agential language, are framed as simple instructional acts ('tell the agent,' 'ask the AI to check'). This choice domesticates the risk, making it seem as manageable as supervising a human employee. The text strategically avoids metaphor when it might undermine the core message. For example, it never delves into the mechanical details of how an instruction like 'don't share my financial picture' would be implemented, because doing so would reveal the immense complexity and fragility of the process, thereby contradicting the message of simple, agent-like controllability. This strategic variation—mechanistic for credibility, agential for capability, and instructional for safety—reveals the text's primary function not as a neutral scientific explanation, but as a persuasive document designed to build excitement and confidence among leaders, encouraging adoption while minimizing the perceived complexity and fundamental risks of the technology.

Conclusion

Description

This final section provides a comprehensive synthesis of the entire analysis. It identifies the text's dominant metaphorical patterns and explains how they construct an "illusion of mind." Most critically, it connects these linguistic choices to their tangible, material stakes—analyzing the economic, legal, regulatory, and social consequences of this discourse. It concludes by reflecting on AI literacy as a counter-practice and outlining a path toward a more precise and responsible vocabulary for discussing AI.

Pattern Summary

The discourse within the provided text is built upon a system of two dominant and interconnected anthropomorphic patterns: 'AI as a Controllable Subordinate' and 'AI as an Autonomous Social Actor.' The foundational pattern is that of the controllable subordinate. The text repeatedly frames the AI as an entity that can be 'told,' 'instructed,' and 'asked' to perform or refrain from actions. This establishes a baseline of human control and hierarchical relationship, making the AI appear manageable and non-threatening. For an audience of leaders, this metaphor is deeply resonant, mapping directly onto the familiar paradigm of delegation and management. Building directly upon this foundation is the second, more aspirational pattern of the AI as an autonomous social actor. This pattern attributes to the system complex human capabilities such as possessing 'common sense,' the ability to 'negotiate,' and the capacity to incorporate social values like 'fairness.' These two patterns work in concert. The 'subordinate' frame mitigates the fear associated with the 'autonomous actor' frame. The AI's potential for independent action is rendered safe by the assurance that it remains fundamentally under human instruction. One cannot work without the other; an uncontrollable autonomous actor is a threat, while a mere subordinate lacks the advanced capabilities the text seeks to promote. This metaphorical system is not a simple one-to-one mapping but a sophisticated rhetorical structure. It first domesticates the technology by placing it within a familiar power dynamic, and then, from that position of perceived safety, elevates its capabilities into the realm of human social and cognitive prowess. Removing the 'subordinate' pattern would make the 'social actor' seem dangerously unpredictable, while removing the 'social actor' would leave a mere tool, unworthy of the 'agentic' label and the strategic excitement it is meant to generate.

Mechanism of Illusion: The "Illusion of Mind"

The 'illusion of mind' in this text is constructed through a deliberate rhetorical architecture that guides the reader from a concrete mechanical concept to an abstract agential one. The central sleight-of-hand is the strategic use of the mechanistic explanation of 'embeddings' as a bridge to anthropomorphism. The process begins by offering a technical-sounding, non-threatening description of how LLMs work: they 'compute and manipulate abstract representations.' This anchors the concept in science and creates an impression of transparency. However, this mechanical foundation is immediately used to launch into a discussion where the system is treated as a cognitive agent. The causal chain of persuasion is as follows: first, establish that the system operates on 'meaning' (via embeddings). Second, frame challenges and capabilities in terms of this 'meaning,' which naturally invites cognitive and intentional language. For example, the problem of data leakage is no longer a technical issue of vector similarity but a social one of needing to 'tell' an agent what secrets to keep. Third, extend this agency to increasingly complex social behaviors like 'negotiation' and 'common sense.' The audience's susceptibility to this illusion is rooted in a desire for simplicity and control. The immense complexity of statistical machine learning is cognitively taxing; the metaphor of a human-like agent is simple and intuitive. This illusion is amplified by the text’s consistent slippage in its explanatory mode. It repeatedly starts by explaining how a system works (e.g., 'trained on human-generated data') and immediately pivots to explaining why it acts as it does (e.g., 'we might expect [it] to behave similar to people'). This move from a mechanistic cause to an intentional or dispositional effect is the core of the persuasive machine, subtly transforming a complex computational artifact into a predictable and relatable mind.

Material Stakes

• Selected Categories: Economic, Regulatory/Legal
• Analysis: The metaphorical framing in this text has tangible consequences in economic and legal domains, shaping investment decisions and liability frameworks. Economically, the 'AI as a Skilled Negotiator' metaphor is a powerful driver of investment and corporate adoption. Leaders reading this are encouraged to see agentic AI not as a simple automation tool, but as a digital substitute for skilled human labor in high-value roles like procurement and sales. This framing can lead to inflated valuations and resource allocation based on a misunderstanding of the technology's capabilities. A firm might invest millions in a system expected to 'negotiate,' only to suffer significant losses when the system optimizes for a poorly specified variable (e.g., lowest unit price) at the expense of crucial unquantified factors like supplier relationships or product quality, which a human negotiator would intuitively balance. The metaphor obscures the reality of brittle optimization, creating a direct path from linguistic hype to economic risk. In the Regulatory and Legal sphere, the 'AI as a Subordinate with Common Sense' frame has profound implications for liability. When an autonomous system inevitably causes harm—for example, by sharing sensitive data it wasn't explicitly 'told' not to—this framing shifts the burden of responsibility. The language suggests the AI is an entity that can be 'instructed,' positioning its failure as a managerial one. This benefits developers and vendors, who can argue they provided a capable agent that the user failed to 'train' or 'constrain' properly. A legal framework influenced by this thinking might move away from product liability, where the manufacturer is responsible for foreseeable misuse, toward a model that treats the AI as a legal agent, with the user bearing the liability for its actions. Replacing 'the agent should be told' with 'the system must be programmed with explicit safety constraints' correctly places the onus on the engineers to build a safe product, a shift that threatens the business models of those who profit from deploying minimally-governed systems.

Literacy as Counter-Practice: AI Language Literacy

Practicing AI literacy, as demonstrated by the reframing exercises in Task 4, functions as a direct counter-practice to the risky material consequences engendered by anthropomorphic language. The core principle underlying these reframings is the deliberate re-centering of mechanism and responsibility. By replacing 'the agent should be told' with 'the system must be configured with hard-coded rules,' we perform a crucial act of resistance. This move counters the legal and economic stakes directly. It dismantles the narrative that could allow a manufacturer to shift liability to a user, instead asserting that safety is an engineered property for which the creator is responsible. Similarly, reframing 'negotiation' as 'multi-parameter optimization' undermines the economic hype that drives misallocated investment. It forces a more rigorous, technical conversation about what is actually being optimized and what crucial context is being ignored, thereby preventing catastrophic business decisions based on a fantasy of digital personhood. Systematically adopting these practices requires a conscious professional and ethical commitment. It means researchers, developers, and journalists must actively choose precise, mechanistic language, even when it is less evocative or exciting. This practice would face immense resistance. Marketing departments thrive on the simplicity and power of the agent metaphor. Executives and investors are often more receptive to compelling narratives than to dry, technical descriptions of limitations. Therefore, adopting precision is a political act. It challenges the incentive structures that reward hype over accuracy. It threatens the strategic ambiguity that allows companies to maximize adoption while minimizing liability. AI literacy, in this context, is not just about clearer communication; it is a form of linguistic discipline that serves as a bulwark against the legal and economic harms that arise when the 'illusion of mind' is allowed to dictate policy and investment.

Path Forward

To foster a more responsible discourse around agentic AI, particularly for a leadership audience, the path forward requires a fundamental shift in vocabulary and institutional practice, moving from the language of delegation to the language of systems engineering. Instead of describing these systems as 'agents' we 'entrust,' the discourse community should adopt a vocabulary centered on 'automated systems' that are 'configured,' 'constrained,' and 'audited.' Terminology like 'agentic common sense' should be replaced with 'robustness and safety engineering,' and 'negotiation' should be specified as 'constrained optimization protocols.' This vocabulary is superior because it foregrounds the technical and ethical responsibilities of the system's creators and deployers. It replaces a misleading social metaphor with a precise engineering one, forcing conversations to focus on concrete questions of safety, testing, and validation rather than vague notions of 'trust' and 'understanding.' To support this shift, institutional changes are necessary. For instance, influential industry publications and analyst firms targeting leaders could mandate that any article discussing 'agentic' capabilities must include a 'Mechanism & Limitations' section. This section would require authors to translate agential claims into their underlying computational processes and explicitly state the potential failure modes. Similarly, a standard for 'Model Cards' or 'System Data Sheets' could be expanded to include a section on 'Metaphorical Claims,' where vendors are required to justify their use of anthropomorphic language and detail the technical reality behind it. The trade-off would be a loss in narrative simplicity and perhaps some of the breathless hype that currently drives the market. However, the gain would be a more mature, realistic, and ultimately safer ecosystem. This shift is critical for effective governance. A public and political sphere that understands these systems as complex, brittle artifacts is far more likely to demand meaningful transparency, robust safety standards, and clear lines of accountability—the essential pillars for navigating a future where automation plays an increasingly consequential role in our lives.

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Raw JSON: 2025-11-09-the-science-of-agentic-ai-what-leaders-should-know-metaphor-17ee0b.json Analysis Framework: metaphor v4.0 Generated: 2025-11-09T11:29:43+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.

Examining the Text's Core

I'm currently focused on a close reading of the text, meticulously annotating passages related to "agentic AI." I'm looking for the key moments where the text directly addresses the topic, looking for direct or indirect references to autonomy and intentionality, which I'll analyze for potential biases or rhetorical devices.

Initiating Analysis Structure

I've moved on to a critical discourse analysis. My focus now is on organizing the analysis. I've begun to structure my findings based on identified metaphors and anthropomorphisms within the text regarding "agentic AI." My initial review has yielded several potential examples, which will be the basis of a deeper examination. I'm focusing on their implications and potential biases, particularly how the language shapes our understanding of AI's capabilities and nature. I'm building a robust list of examples.

Launching the Deep Dive

I'm now fully immersed in the critical analysis, and have begun to map out the structure. I'm actively working through the provided examples, defining each task. I'm building out the schema, currently focused on annotating each example, as well as deconstructing the language of the source/target domains. I am building a list of key examples as the framework for the audit, and examining how specific phrases like "negotiate" are applied in the context of an agentic AI. I'll soon move on to exploring Brown's typology for explanations.

Finalizing Analysis Framework

I've made a final push to refine the overall framework, and am now working on a second pass through Brown's explanation typology. I'm focusing on identifying the explanation types in each quote. The work so far has been an iterative process, so I am refining the initial classifications. My goal is to achieve comprehensive coverage and a deeper understanding of the explanations. I believe that I am on track to meet the deadline.

Developing a Robust Framework

I've crafted a comprehensive analysis framework to deconstruct anthropomorphic language in the text. I've broken down my work into four tasks. I'm actively identifying and classifying key examples based on metaphors, source-target mappings, and explanation types using Brown's typology. I'm now exploring reframing anthropomorphic phrases to improve AI literacy. The goal is to provide a comprehensive, rigorous, and insightful analysis for my report.

Initiating the Critical Review

I've initiated the critical discourse analysis, following the multi-part prompt. I'm deep into reviewing the text, identifying key passages for my analysis. I've begun to build a table of examples, annotating the most relevant sections of the text. I'm carefully examining how the language shapes our understanding of "agentic AI." I've already identified several recurring themes and concepts. I am focused on the metaphors and anthropomorphisms within the text regarding "agentic AI."

Developing Actionable Insights

I'm now fully immersed in the critical analysis, working through the specific tasks. I've successfully identified several key examples of anthropomorphic language within the text, and I am beginning to deconstruct these examples. I'm focusing on the metaphors, source-target mappings, and the implications of the language used, with a goal of constructing a comprehensive framework that addresses each of the specified tasks. I am carefully analyzing the implications of the chosen language.

Developing Deeper Insights

I'm presently building upon the foundation I've laid, focusing on in-depth textual analysis. I'm actively working on the four critical observation essays, aiming to deconstruct the chosen metaphors. My focus is on the language shifts and the subtle ways trust is established through anthropomorphic terms like "negotiation." It's critical to identify the implications behind the obscured mechanics. I'm focusing on crafting a thorough and rigorous analysis.

Developing Actionable Insights

I'm now fully immersed in the critical analysis, working through the specific tasks. I've successfully identified several key examples of anthropomorphic language within the text, and I am beginning to deconstruct these examples. I'm focusing on the metaphors, source-target mappings, and the implications of the language used, with a goal of constructing a comprehensive framework that addresses each of the specified tasks. I am carefully analyzing the implications of the chosen language.

Analyzing Conceptual Shifts

The critical examination is now in progress, focusing on how the text's discourse shapes understanding of "agentic AI." I'm identifying and classifying metaphors, source-target mappings, and applying Brown's typology to explanations. The analysis is structured, and I'm currently working to refine the reframing of anthropomorphic phrases.

Initiating Textual Audit

I've initiated a thorough reading of the provided text, meticulously identifying key passages and phrases concerning "agentic AI." My focus is to build a structured audit, and annotate the key passages, which will be the foundation for an analysis. I will be sure to address all parts of the provided prompt. I've begun to build the framework for this analysis.