Cost–effectiveness is where the romantic idea of “just use a giant vision‑LLM” runs into the hard edges of drone operations. When we look for explicit economic comparisons between vision‑LLMs used directly on aerial imagery and more structured agentic frameworks, we quickly discover that the literature is still thin: most papers report computational and operational efficiency (latency, success rate, mission duration), but stop short of a full dollar‑per‑mission analysis. Still, the numbers they do provide already hint at how the trade‑offs play out when we try to build something like ezbenchmark into a realistic pipeline.

UAV‑CodeAgents is a useful anchor because it is unambiguously an agentic framework: a team of language and vision‑language model–driven agents using a ReAct loop to interpret satellite imagery, ground natural language instructions, and generate detailed UAV missions in large‑scale fire detection scenarios. Rather than asking a single vision‑LLM to go from pixels to trajectories, the system delegates: one agent reads the task and context, another reasons about waypoints in map space, and others refine plans through iterative “think–act” cycles, all grounded by a pixel‑pointing mechanism that can refer to precise locations on aerial maps. From a cost perspective, this is clearly heavier than a single forward pass through a monolithic VLM, but the paper quantifies why developers might accept that overhead: at a relatively low decoding temperature, UAV‑CodeAgents achieves a 93% mission success rate with an average mission creation time of 96.96 seconds for complex industrial and environmental fire scenarios. Those two numbers—success rate and planning latency—are effectively stand‑ins for mission‑level cost: fewer failed missions and sub‑two‑minute planning windows translate into fewer re‑flights and less human babysitting.

In contrast, work that relies on vision‑LLMs alone for aerial or satellite reasoning generally reports per‑task accuracy and qualitative flexibility, but not system‑level success metrics. A vision‑LLM that can answer “Where are the highest‑risk areas in this scene?” or “Which roofs look suitable for solar?” in a single forward pass is computationally attractive in isolation: one model, one call, no orchestration overhead. However, without an agentic layer to manage tools, refine outputs, and correct itself, any errors must be caught either by humans or by additional guardrail logic that is usually not part of the evaluation. What UAV‑CodeAgents implicitly shows is that we can treat the additional compute for multi‑agent reasoning as a kind of insurance premium: more tokens and more calls per mission, but dramatically higher odds that the resulting trajectory actually satisfies operational constraints. When we factor in the cost of failed missions—wasted flight time, re‑runs, delayed detection—the agentic system’s 93% success rate looks less expensive than it first appears.

None of this means that agentic frameworks are always cheaper in a narrow cloud‑bill sense. A pure vision‑LLM approach keeps our architecture simple and our per‑call overhead low. We can batch images, run them through a single VLM, and get scene descriptions or coarse analytics with predictable latency. If our benchmark only cares about perception‑level accuracy on static tasks, that simplicity is compelling. But once we move toward workload‑level benchmarking—chains of queries, mission‑like sequences, or “LLM‑as‑a‑judge” roles—errors propagate. A cheap VLM judgment that nudges a pipeline in the wrong direction can incur downstream costs far larger than the initial savings. UAV‑CodeAgents’ design, where agents iteratively reflect on observations and revise mission goals, is essentially an explicit acknowledgement that paying for more reasoning steps up front can reduce expensive mistakes later.

For ezbenchmark, which inherits TPC‑H’s focus on whole workloads rather than micro‑tasks, this suggests a specific way to think about cost‑effectiveness studies. Instead of trying to price each VLM token or GPU second in isolation, we treat the combination of “analytics accuracy + mission success + human oversight time” as our cost metric, and then compare three regimes: vision‑LLM alone, vision‑LLM embedded as a component in an agentic judge, and a full multi‑agent ReAct‑style framework like UAV‑CodeAgents wrapped around our catalog and tools. The existing literature gives us at least one anchor point on the agentic side—around 97 seconds of planning with 93% success for complex missions—while the vision‑LLM‑only side gives us per‑task accuracy but typically omits mission‑level reliability. A genuine cost‑effectiveness study in our setting would fill that gap, measuring not only GPU minutes but also re‑runs, operator interventions, and time to trustworthy insight over a suite of benchmark workloads

What’s missing in current research, and where ezbenchmark could be genuinely novel, is a systematic, TPC‑H‑style analysis that treats agentic frameworks and vision‑LLMs as first‑class design choices and quantifies their end‑to‑end economic impact on drone image analytics. UAV‑CodeAgents proves that multi‑agent ReAct with vision‑language reasoning can deliver high mission success with bounded planning time; our benchmark can extend that logic to analytics and judging: how many agentic reasoning steps, how many tool calls, and how many vision‑LLM passes are worth spending to get one unit of “better decision” from a drone scene. Framed that way, cost‑effectiveness stops being an abstract question about model sizes and becomes something our framework can actually measure and optimize.

 The emerging survey literature on agentic AI for UAVs makes it clear that “AI agents for drone image analytics” is no longer a single pattern but a family of architectures, each carving up perception, reasoning, and control in different ways. Sapkota et al. introduce the term “Agentic UAVs” to describe systems that integrate perception, cognition, control, and communication into layered, goal-driven agents that operate with contextual reasoning and memory, rather than fixed scripts or reactive control loops. arXiv.org arXiv.org alphaxiv.org In their framework, aerial image understanding is only one layer in a broader cognitive stack: perception agents extract structure from imagery and other sensors; cognitive agents plan and replan missions; control agents execute trajectories; and communication agents coordinate with humans and other UAVs. This layered view is useful when we start thinking about agentic frameworks as “judges” for benchmarking: the judging capability can itself be an agent, sitting in the cognition layer, consuming outputs from perception agents and workload metadata rather than raw pixels alone. arXiv.org alphaxiv.org 

Within this broader landscape, vision–language–driven agents are a distinct subclass. Sapkota et al. explicitly highlight vision–language models and multimodal sensing as key enabling technologies for Agentic UAVs, noting that they allow agents to parse complex scenes, follow natural-language instructions, and ground symbolic goals in visual context. arXiv.org alphaxiv.org These agents differ from traditional planners in that they can reason over image and text jointly, which makes them natural candidates for roles like “mission explainer,” “anomaly triager,” or, in our case, “benchmark judge” for aerial analytics workloads. Instead of judging purely from numeric metrics, a vision–language agent can look at a drone scene, read a workload description, inspect candidate outputs, and form a qualitative judgment about which pipeline better captures the intended analytic semantics. 

UAVCodeAgents by Sautenkov et al. provides a concrete multi-agent realization of this vision–language–centric approach for UAV mission planning. arXiv.org arXiv.org arXiv.org Their system uses a ReAct-style architecture where multiple agents, powered by large language and vision–language models, interpret satellite imagery and high-level natural language instructions, then collaboratively generate UAV trajectories. arXiv.org arXiv.org arXiv.org A core feature is a vision-grounded pixel-pointing mechanism that lets agents refer to precise locations on aerial maps, and a reactive thinking loop that enables iterative reflection, goal revision, and coordination as new observations arrive. arXiv.org arXiv.org In evaluation on large-scale fire detection missions, UAVCodeAgents reaches a 93% mission success rate with an average mission creation time of about 97 seconds when operated at a lower decoding temperature, illustrating that a team of reasoning-and-acting agents, anchored in visual context, can deliver robust, end-to-end behavior. arXiv.org arXiv.org arXiv.org While their agents are designed to plan rather than judge, the architecture is the same kind we would co-opt for an evaluative role: a vision–language agent that can “look,” “think,” and “act” by querying tools or recomputing metrics before rendering a verdict. 

Across these works, we can roughly distinguish three archetypes of agents relevant to drone image analytics. First are perception-centric agents, effectively wrappers around detection, segmentation, or classification models that expose their capabilities as callable tools within an agentic framework. arXiv.org alphaxiv.org Second are cognitive planning agents, like those in UAVCodeAgents, which translate goals and visual context into action sequences, refine them through ReAct loops, and manage uncertainty through deliberation. arXiv.org arXiv.org arXiv.org Third—more implicitly in the surveys—are oversight or monitoring agents that track mission state, constraints, and human guidance, and intervene or escalate when anomalies arise. arXiv.org arXiv.org For ezbenchmark, the “judge” fits best in this third category: an oversight agent that does not control drones directly, but evaluates analytic pipelines and their outputs against goals, constraints, and visual evidence, possibly calling perception tools or re-running queries to validate its own judgment before scoring. 

Agentic surveys also emphasize the role of multi-agent systems and collaboration, which is directly relevant to how we might structure an evaluative framework. arXiv.org alphaxiv.org Instead of a single monolithic judge, we can imagine a committee of agents: one agent specialized in geospatial consistency (checking object counts, extents, and spatial relations); another focused on temporal coherence across flights; another on narrative quality and interpretability of generated reports; and a final arbiter that aggregates their recommendations into a final ranking of pipelines. Sapkota et al. note that multi-agent coordination enables UAV swarms to share partial observations, negotiate tasks, and adapt to dynamic environments more effectively than single-agent systems. arXiv.org alphaxiv.org Translated into benchmarking, multi-agent evaluation would let different judges stress-test different aspects of a pipeline, with the ensemble acting as a richer, more discriminative “LLM-as-a-judge” than any single model pass. 

What makes this particularly attractive for an ezbenchmark-style adaptation of TPCH is that the agentic literature already leans heavily into reproducibility and benchmarking. UAVCodeAgents, for example, is explicitly released with plans for an open benchmark dataset for vision–language-based UAV planning, making their evaluation setup a template for standardized mission-level tasks and metrics in an agentic setting. arXiv.org arXiv.org Sapkota et al. argue for a “foundational framework” for Agentic UAVs that spans multiple domains—precision agriculture, construction, disaster response, inspection—and call out the need for system-level benchmarks that assess not only perception accuracy but also decision quality, mission flexibility, and human–AI interaction quality. arXiv.org arXiv.org This is very close in spirit to a TPCH-style workload benchmark, except operating at the level of missions and workflows rather than isolated queries. If we treat each ezbenchmark workload as a “mission” over a drone scenes catalog, an agentic judge can be evaluated on how consistently its preferences align with human experts when comparing alternative pipeline implementations for the same mission. 

In practice, using these agent types as judges means giving them access to more than just model outputs. An evaluative agent would see raw or tiled imagery, structured detections from classical or neural perception models, SQL outputs over our catalog, and the natural-language description of the analytic intent. It could then behave much like a planning agent, but in reverse: instead of generating a mission, it generates probes—additional queries, spot checks on specific tiles, sanity checks on object distributions—that help it decide which pipeline better fulfills the workload semantics. This is exactly the kind of “Reason + Act” loop that UAVCodeAgents demonstrates, only the action space is benchmark tooling instead of flight waypoints. arXiv.org arXiv.org The survey of Agentic UAVs suggests such introspective, tool-using behavior is central to robust autonomy in the field; using it in a judging capacity extends the same philosophy to benchmarking, pushing ezbenchmark beyond static metrics toward a living, agent-mediated evaluation process. arXiv.org arXiv.org alphaxiv.org 

Seen through this lens, enhancing ezbenchmark with an agentic judge is less about bolting on a new feature and more about aligning with where UAV autonomy research is already heading. Agentic UAV surveys formalize the components we need—perception tools, cognitive controllers, communication layers—and UAVCodeAgents shows how multi-agent ReAct with vision–language reasoning can reach high reliability on complex aerial tasks. arXiv.org arXiv.org arXiv.org arXiv.org Our benchmark can exploit those same design patterns: treat specialized detectors and SQL workloads as tools, wrap them in agents that can look, think, and act over drone imagery and metrics, and then measure how well those agents serve in an evaluative role. In doing so, ezbenchmark evolves from a TPCH adaptation into a testbed for agentic judgment itself, letting us benchmark not only pipelines, but also the very agents that will increasingly mediate how humans and UAVs reason about aerial imagery. 

Our References besides citations above: