Embracing "Finitude," Designing "Infinity" – A New Paradigm for Constructing Agent Systems Based on LLM Constraints

2026-01-05

Abstract

Based on a profound analysis of the inherent limitations of Large Language Models (LLMs), this paper proposes a novel paradigm for constructing powerful agent systems. The current pursuit of Artificial General Intelligence (AGI) often falls into the myth of "omni-capable" models, overlooking their intrinsic structural constraints: non-mandatory coordination, finite computational budget, and cognitive incompressibility. This paper argues that instead of futilely attempting to eliminate these limitations, we should acknowledge and embrace their "finitude." Through sophisticated systems engineering, we can transform the constraints themselves into design principles, thereby achieving "infinite" extensibility at a higher level. The core approach lies in: externalizing internal contradictions into explicit processes via Coordination Engineering, optimizing resource allocation under scarcity via AI Decision Economics, and shifting from static knowledge compression to dynamic information adaptation via Cognitive Flow Management. This paradigm of "finite agents, infinite systems" directly confronts the "Münchhausen Trilemma" in intelligent systems design—the fundamental conflict between the infinitude of thought itself and the finitude of thinking resources—and provides a practical theoretical framework and practical guide for building reliable, scalable, and evolvable human-machine collaborative systems.

Keywords: Large Language Models; Agent Systems; Coordination Engineering; AI Decision Economics; Cognitive Flow Management; Finite Intelligence; Münchhausen Trilemma

1. Problem Context: From the "Omnipotence Myth" to "Finitude Awakening"

Generative AI, represented by Large Language Models, has achieved breakthrough progress, sparking boundless imagination about AGI. However, when attempting to apply LLMs to solve complex real-world tasks, their performance often falls far short of the "omnipotent" expectation. Agents struggle to complete coherent, reliable, multi-step work in one go, exposing the fundamental limitations of current LLMs as cognitive cores. At their essence, these limitations are not temporary technical flaws but structural constraints rooted in their architecture, resources, and cognitive paradigm.

This reflects a profound philosophical and engineering dilemma, namely the manifestation of the Münchhausen Trilemma in the AI field: we expect agents to think infinitely deeply to obtain perfect solutions, but their thinking process must consume finite, expensive computational resources. This fundamental contradiction between infinite desire and finite resources cannot be eliminated. Persisting down the single path of "creating more omnipotent models" will not only encounter enormous economic and computational bottlenecks but may also sow hidden dangers for system safety and controllability. Therefore, we must undergo a fundamental "paradigm shift": from futilely pursuing "individuals with infinite intelligence" to prudently designing "infinite systems capable of integrating and orchestrating finite intelligence."

2. Core Thesis and Arguments

2.1 Thesis One: Taming "Non-Mandatory Coordination" with "Coordination Engineering"

The "non-mandatory coordination" characteristic of LLMs refers to their inability to guarantee that the generation process simultaneously satisfies all given, even conflicting, constraints. This is not a bug but an inevitable consequence of their probabilistic generation nature and the engineering compromise of "must output" to avoid "thinking halts." Forcing a single LLM to perform complex coordination of multiple objectives and constraints in a single inference is akin to asking one person to simultaneously play the roles of project manager, architect, developer, and tester—the result is often lopsided attention or mediocre output.

Solution and Path: We neither need nor can change this underlying characteristic of LLMs. Instead, we should use Coordination Engineering to shift the burden of coordination from inside the model to outside the system. This manifests as three progressively advanced architectural patterns:

• Checklist Pattern (Posterior Coordination): Suitable for scenarios with clear constraints and few conflicts. The system validates the LLM's initial draft against an explicit checklist and guides the LLM for targeted revisions, transforming "one-time satisfaction" into an iterative loop of "generate-validate-correct."
• Parliamentary Debate Pattern (Explicit Coordination): This is the core solution for handling multi-dimensional conflicting concerns. The system instantiates a dedicated Agent role for each core concern (e.g., feasibility, safety, user experience), forming an "expert parliament." A neutral "Chairperson" Agent organizes debates and negotiations, externalizing the originally implicit internal trade-offs into open, transparent, and auditable clashes of viewpoints and comprehensive resolutions.
• Constraint Solver Pattern (Formalized Coordination): For highly structured, mathematically expressible problems (e.g., scheduling, resource allocation), position the LLM as a "requirement perceiver," responsible for translating natural language requirements into formal constraints. These are then handed to traditional constraint solvers or optimization algorithms for computation. Finally, the LLM translates the formal results back into natural language.

The core idea of these engineering methods is: Elevating "coordination" from an implicit struggle within the LLM to an explicit, structured process at the system level, thereby achieving overall coordination reliability through architecture while acknowledging the finitude of individual units.

2.2 Thesis Two: Responding to the "Münchhausen Trilemma" and "Finite Computational Budget" with "AI Decision Economics"

Commercial LLMs always operate under a finite computational budget, which is the direct economic manifestation of the Münchhausen Trilemma: the desire for infinite thought is bound by finite "thinking fuel" (computation). A "smarter" model typically implies higher inference costs. Expecting an "omnipotent AGI" that disregards cost to solve all problems is neither economical nor realistic. Therefore, the system must possess the ability to make rational decisions within a finite budget: that is, allocating precious computational resources to the thought processes most likely to yield high value.

Solution and Path: This requires introducing the mindset of AI Decision Economics, treating computational power, time, and API costs as scarce resources, and establishing market-based or quasi-market-based mechanisms for optimal allocation. Its implementation can be divided into four levels:

• Base Currency Layer: Establish measurable cost units, such as token consumption, inference time, API fees, attaching clear "price tags" to all computational operations.
• Value Assessment & Budget Layer: Define a "value function" (static or dynamic) for tasks and allocate budgets accordingly. An advanced form could introduce an internal "auction market," allowing high-value, urgent tasks to "bid" for more computational resources. This is precisely a mechanized answer to the fundamental question: "Which thoughts are worth consuming resources for?"
• Decision Strategy Layer: Empower each Agent with economic rationality, e.g., adopting a "fast-slow thinking" strategy (first generate a low-cost answer quickly; if confidence is low, apply for budget for deep thinking), or deciding whether to call expensive external tools based on expected value.
• Market Coordination Layer: At the macro level, a distributed task market and resource market can be constructed. Agents act as free economic agents, allowing resources to automatically flow to the individuals who can utilize them most efficiently through bidding and trading, achieving Pareto optimization of global system resources.

The essence of this framework is confronting the "Münchhausen Trilemma" head-on, abandoning the fantasy of infinite resources. Instead, by constructing a controlled internal economic system, it externalizes and mechanizes the optimization problem of resource allocation, endowing the system with endogenous motivation to pursue "thinking cost-effectiveness" and seek optimal solutions within finitude.

2.3 Thesis Three: Accepting "Cognitive Incompressibility" with "Cognitive Flow Management"

"Cognitive incompressibility" posits that there is a theoretical lower bound to the amount of information required to fully understand a specific problem; it cannot be infinitely compressed through a "magic instruction." The general pre-training of LLMs cannot cover all the tacit knowledge, project context, and dynamic changes of a specific domain. Attempts to solve all problems with a perfect prompt are doomed to fail. This also represents the shattering of the fantasy of "infinite cognitive compression."

Solution and Path: We should abandon the fantasy of "compressing cognition" and shift towards managing cognitive flow. That is, designing a system capable of efficiently diagnosing cognitive gaps, acquiring information on demand, and dynamically constructing and updating its understanding of the current task. Its practical implementation manifests as a series of layered strategies:

• From "Indoctrination" to "Navigation": The system no longer attempts to receive all information at once. Instead, like a "tour guide," it guides users to provide necessary information step-by-step or offers clear options at key decision points, managing the progressive process of cognition.
• Progressive Cognitive Loading: Borrowing from the concept of "progressive disclosure," information is presented on-demand and in layers. The conversation starts with high-level goals and gradually delves into specific details, avoiding initial information overload and respecting the objective pace of cognition.
• Iterative Alignment Loop: Accept the imperfection of initial understanding and establish a rapid iteration mechanism of "draft-feedback-refinement." The system treats preliminary output as the starting point for aligning cognition, not the final deliverable, thereby dispersing the pressure of one-time cognitive transfer across multiple low-cost alignment cycles.
• Environmental Awareness & Learning: The system should actively analyze codebases, documentation history, and interaction logs to extract project-specific "tacit knowledge." It should continuously learn from feedback, enabling cognitive evolution so that the cognitive flow can continuously enrich and deepen over time.

The core of this paradigm is viewing human-AI collaboration as a dynamic process of co-weaving a cognitive network, managing the rate, sequence, and density of information flow to adapt to incompressible cognitive needs, rather than engaging in futile compression.

3. Conclusion and Future Research Outlook

This paper argues that the key to building powerful AI systems lies in philosophically accepting the reality of LLMs as "finite intelligence units" and directly confronting the fundamental contradiction revealed by the "Münchhausen Trilemma." The tripartite framework we propose—Coordination Engineering, AI Decision Economics, and Cognitive Flow Management—does not attempt to eliminate finitude. Instead, through system design, it transforms constraints into rules that drive evolution, thereby achieving "infinite" extensibility of capabilities at a higher level. This marks a fundamental shift: from the magical thinking of praying for an "omnipotent oracle" to the engineering mindset of constructing an "intelligent society with clear division of labor, efficient resource use, and adept learning."

Looking ahead, this paradigm of "embracing finitude, designing infinity" opens up a series of exciting research directions:

1. Multi-Agent Social Mechanism Design: How to design more efficient, fair, stable, and ethically aligned collaboration, negotiation, and governance mechanisms for Agent societies? How to prevent malicious behaviors in games, such as collusion and fraud?
2. Endogenous Value and Alignment: In controlled economic or game-theoretic environments, how can we guide AI to evolve beneficial and human-aligned values through interaction? How to design "constitutional"-level meta-rules to constrain value drift and ensure it does not deviate from the track of human well-being?
3. Quantification and Optimization of Cognitive Flow: How to move beyond qualitative descriptions to establish formal models for precisely measuring cognitive gaps, information entropy, and cognitive flow efficiency? Can we establish a universal descriptive language and optimization algorithms for cognitive flow management?
4. New Interfaces for Human-Machine Fusion: Within cognitive flow management, how to design more natural and efficient human-machine interaction interfaces, enabling humans to coordinate and guide the cognitive processes of multiple agents as intuitively and elegantly as conducting a symphony orchestra?
5. Exploring the Limits of "Finitude": Given specific architectural and resource constraints, what is the theoretical upper limit of the overall performance of an agent system? How can we continuously approach this limit through architectural innovation?

Ultimately, we may find that the dawn of strong artificial intelligence does not come from a solitary super-brain struggling to escape the "Münchhausen Trilemma," but from countless agents that calmly accept their own finitude, performing a harmonious symphony together on a meticulously designed "infinite" stage that stimulates the emergence of collective intelligence. This is precisely the humble yet powerful intelligent future pointed to by "embracing finitude, designing infinity."