'You’re Not Thinking Big Enough: Rebuilding Food Systems with AI Agents - Cody Menefee, Firecrawl'

Engineers should pursue bigger, real-world physical problems - like automating pasture rotation for grass-fed livestock - rather than only building software for ...

By Sean Weldon

Rebuilding Food Systems with AI Agents: A Case Study in Applying LLMs to Physical-World Problems

Abstract

This synthesis examines a proposal to apply large language models (LLMs) and remote sensing infrastructure to automate rotational grazing decisions for pasture-raised livestock. The core thesis, drawn from Cody Menefee's presentation, is that engineering talent is disproportionately allocated to building software for other software builders, while physical-world domains with significant welfare, ecological, and economic externalities remain under-addressed. The analysis details how existing virtual-fencing platforms solve animal movement but not the destination decision, and proposes an architecture combining GPS telemetry, biomass sensing, and LLM-based reasoning across nested spatial scales. Three structural blockers are identified: absent knowledge infrastructure, immature biomass visualization, and closed collar hardware APIs. Findings suggest that non-deterministic, multivariate physical problems represent an underexplored but tractable application domain for current AI systems, with implications for engineers seeking higher-impact problem selection.

1. Introduction

Contemporary software engineering discourse is heavily weighted toward tooling built by engineers for other engineers - developer platforms, agent frameworks, and infrastructure abstractions. This synthesis uses a single physical-world case study, rotational grazing of ruminant livestock, to argue that comparable engineering effort applied to agricultural systems could yield outsized welfare, ecological, and economic returns.

Rotational grazing (also termed managed intensive grazing) is the practice of subdividing pasture into paddocks sized to provide approximately one day of forage, with herds relocated daily. This prevents selective overgrazing, limits trampling, and allows grazed sections to recover. The practice stands in contrast to continuous grazing or feedlot finishing, and is credited with benefits to animal welfare, soil health, biodiversity, and product quality.

The central question addressed is whether the decision layer of rotational grazing - determining where a herd should move next - can be automated using an LLM operating over sensor-derived state, given that this decision is currently made through farmer intuition rather than any codified algorithm. A secondary question concerns the structural preconditions, both technical and commercial, necessary before such a system could be built. The analysis proceeds by establishing the agricultural context, examining the labor bottleneck and grass growth dynamics, evaluating candidate sensing modalities, and identifying the specific blockers to implementation.

2. Background and Related Work

Approximately 97% of cattle in the current system are finished on feedlots, with only 3% raised on pasture. This gap is presented not as a reflection of consumer or farmer preference but as an operational constraint: rotational grazing is labor-intensive, requiring daily movement of fencing, animals, and water sources.

Two commercial precedents address part of this constraint. Halter, valued at approximately $2 billion following investment from Peter Thiel, and No Fence both provide GPS-enabled collars implementing virtual fencing, using auditory and electrical cues to confine and relocate animals without physical fence repositioning. These platforms substantially reduce the actuation cost of rotation but leave the underlying decision - next paddock selection - unresolved. As stated in the source material, "there isn't a next best paddock to move to. There's just your best guess on where you think they should go." This distinction between solved actuation and unsolved decision-making motivates the proposed LLM-based architecture.

3. Core Analysis

3.1 The Labor and Decision Bottleneck

Rotational grazing's labor intensity stems from its daily cadence: paddocks must be resized and relocated, animals moved, and water sources repositioned every day. Collar-based virtual fencing addresses the physical relocation problem but does not address the underlying decision problem, which remains dependent on farmers "eyeballing" pasture condition. This decision is non-trivial because it must account for grass growth dynamics: grass exhibits a "juvenile sweet spot" in its growth cycle - cutting it too short delays regrowth, while allowing it to grow too long renders it bitter and less palatable to livestock. Effective rotation therefore requires returning animals to paddocks at the optimal point in this cycle, a temporally and spatially distributed optimization problem poorly suited to static rule-based approaches.

3.2 Sensing Modalities for Biomass Estimation

Several candidate approaches for measuring pasture biomass are evaluated, each with distinct trade-offs. Drone orthomosaic imaging offers high-fidelity spatial data but faces regulatory constraints on autonomous flight and requires farmers to acquire new operational skills. Satellite imagery, exemplified by the company Planet, provides daily global coverage at 1x1 meter resolution - a promising but potentially insufficient granularity for fine-grained paddock-level decisions. Trail cameras paired with a fixed measuring apparatus (e.g., a marked post near a tree) offer a low-cost method for estimating grass height relative to a known reference point, though coverage is spatially limited compared to aerial or satellite methods. No single modality is presented as sufficient; the implied conclusion is that a hybrid sensing stack is required to achieve farm-wide biomass visibility at decision-relevant resolution.

3.3 LLM-in-the-Loop Decision Architecture

The proposed system combines collar-derived GPS telemetry with grass height and biomass data, feeding this multivariate state into an LLM tasked with autonomous grazing decisions. The complexity of this task is emphasized: decision-making must occur across nested scales - individual cow, herd, pasture, farm, and ecosystem - simultaneously. As stated in the source material, the system "has to make this decision not just on a day-to-day basis, but in varying degrees of relation." This framing positions the problem as a genuine multivariate, non-deterministic optimization task, distinguishing it from simpler rule-based agricultural automation. The stated objective is productivity parity with feedlot systems: achieving higher animal density per acre through optimized rotation rather than through confinement.

3.4 Multi-Species Stacking

Beyond single-species rotation, the Pasture Bird model is presented as an extension of the same principle: a mobile chicken coop advances across pasture on a 24-hour cycle, trailing ruminant grazing. Chickens peck parasites from ruminant droppings, reducing medication costs and incrementally improving soil and seed stock. This is enabled by chickens' omnivorous diet, which permits continuous movement without requiring grass-based biomass recovery in the same manner as ruminants. This multi-species architecture suggests that any automated decision system should ultimately account for cross-species scheduling, not merely single-herd rotation.

4. Technical Insights

Implementation of the proposed system requires resolving three structural blockers, each with distinct technical character.

Knowledge base construction: Farming domain knowledge relevant to grazing decisions - optimal rotation timing, regional grass species behavior, drought response - is largely uncodified, existing instead in YouTube videos and scattered research literature. Firecrawl is used to scrape and structure this unstructured content, with Open Pasture proposed as an open-source repository for the resulting knowledge base. This represents a data engineering prerequisite rather than a modeling challenge.

Visualization and biomass measurement: No sensing modality evaluated (drone, satellite, trail camera) is independently sufficient. A production system likely requires a hybrid stack combining coarse satellite coverage with localized trail-camera calibration, with drone imaging as a higher-fidelity but regulation-constrained supplement. Biodiversity measurement, in addition to raw biomass, is flagged as necessary to prevent nutrient imbalance from over-selective grazing.

Hardware API access: The most significant near-term blocker is commercial rather than technical - Halter and No Fence collars operate as closed systems, preventing third-party software from accessing GPS telemetry or issuing movement commands. This is analogous to right-to-repair conflicts in agricultural equipment (e.g., John Deere), and represents a structural barrier independent of AI capability: without open collar APIs, no LLM-based decision layer can be operationally deployed regardless of its sophistication.

5. Discussion

The rotational grazing case illustrates a broader pattern: physical-world systems characterized by high dimensionality, incomplete observability, and reliance on tacit human expertise are plausible targets for LLM-based decision support, provided adequate sensing infrastructure exists. This differs from conventional agricultural automation, which typically targets deterministic tasks (e.g., irrigation scheduling based on soil moisture thresholds). Grazing decisions instead require integrating heterogeneous, partially codified knowledge with real-time multivariate sensor data - a task profile more consistent with LLM reasoning than with rule-based control systems.

The identification of closed hardware APIs as a primary blocker - rather than model capability - suggests that in physical-world AI applications, commercial and infrastructural constraints may dominate over algorithmic ones. This has implications for how engineers evaluate problem tractability: technical feasibility of the AI component does not guarantee system feasibility if upstream hardware ecosystems remain proprietary.

An open question not resolved in the source material concerns validation methodology: how would the correctness of an LLM-generated grazing recommendation be evaluated against ground truth, given the absence of a provably optimal solution and the multi-decade feedback loops involved in soil and ecosystem outcomes.

6. Conclusion

This synthesis presents rotational grazing automation as a concrete illustration of applying LLM-based reasoning to non-deterministic physical-world problems. The core contribution is not a working system but a decomposition of the problem into tractable components: knowledge base construction, biomass sensing, and hardware access, with the last identified as the most immediate barrier to progress. For engineers, the practical takeaway is that high-impact physical domains - agriculture among them - remain accessible targets for applied AI work, provided attention is paid to infrastructural prerequisites rather than model capability alone. Open-collar hardware with accessible APIs is identified as the most immediately actionable next step for the broader research community.

Sources

About the Author

Sean Weldon is an AI engineer and systems architect specializing in autonomous systems, agentic workflows, and applied machine learning. He builds production AI systems that automate complex business operations.

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