New Benchmark Shows Top AI Models Score Below 20 When Building Agent Systems From Scratch

VectorSpaceLab researchers published a benchmark paper on October 13 that reveals a significant capability gap hidden by the current generation of coding-agent evaluations: frontier AI models cannot reliably build complete AI agent systems from scratch, even as those same models approach saturation on the benchmark most commonly used to measure their coding ability. The paper, titled "AgentSWE: Can Coding Agents Build the Agent You Actually Want?" and listed at arXiv as paper 2610.13361, introduced a 25-task evaluation suite designed to measure whether AI coding agents can function as genuine software engineers of AI agent systems — not merely as automated patch generators for existing codebases.

The headline result is stark. Across Creation tasks requiring a model to build a working agent system from an empty workspace, the best-performing model in the evaluation — DeepSeek-V4-Pro — scored 19.1 out of 100, according to the paper's author-reported results. The average Creation score across all evaluated builder-task pairs was 11.2. Forty percent of those pairs — 20 out of 50 evaluated combinations — scored exactly zero. On Optimization tasks requiring an agent to improve a working starter agent toward a measurable performance target, no evaluated builder closed more than five percent of the gap between the starter and the human-defined goal, with the top performer, GPT-5.6 Sol, achieving a gap-closure score of 4.7 percent.

What SWE-bench Cannot Measure

The context for these numbers is the dominant benchmark in coding-agent evaluation. SWE-bench, and its human-validated Verified variant, tests whether an AI system can resolve a GitHub issue by generating a software patch to an existing repository. Top models on the SWE-bench Verified leaderboard now exceed 90 percent — a performance level that many in the AI industry have interpreted as evidence that coding agents are approaching broad software engineering competence.

AgentSWE was designed to challenge that interpretation. Patching an existing repository to close a known issue is a fundamentally different engineering task from building a complete, functional software system from an empty workspace — or from analyzing a production-grade codebase and meaningfully improving a working agent's measured performance. The SWE-bench setup provides a target repository, a description of the bug or feature, and an existing test suite. The model's job is to generate a diff. AgentSWE provides a task brief, expects the model to construct or substantially modify a working agent system, and evaluates the result against held-out test cases the model never saw during development.

The distinction maps onto a real split in the demands placed on AI coding assistants in practice. Many engineering organizations now use frontier models to generate patches, suggest code changes, or complete partial implementations. A smaller but rapidly growing set is beginning to ask AI systems to build entire agentic subsystems — internal automation pipelines, evaluation harnesses, custom tool-use scaffolds, retrieval systems, and similar components. AgentSWE measures whether the same models that handle the first task can handle the second. The answer, from this paper, is that they cannot do so reliably.

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A Commission, Not an Exam: How AgentSWE Evaluates Agent Construction

The benchmark's evaluation structure was designed explicitly to prevent the most obvious form of gaming. The authors call it a commission-not-exam design, borrowing the framing of a client commissioning real software rather than a student taking a test. When a model is given an AgentSWE task, it receives two visible development test cases against which it can iterate and refine its code. Once iteration is complete, the submission is frozen and evaluated against six held-out test cases that the model never accessed during development.

This design mirrors professional software delivery in one important respect: the evaluation suite is not the specification. A software engineering team building a client deliverable can validate against known acceptance tests during development, but the quality of the finished product is measured against use cases the team may not have anticipated. The commission framing forces the model to produce software that generalizes rather than software that passes a known checklist. The full paper is available at arXiv 2610.13361.

The benchmark divides into three lifecycle stages that together cover the arc of how agent software is actually developed and maintained. Creation tasks start from an empty workspace and require the model to build a complete, functioning agent system from a task brief alone — no starter code, no existing structure, only a specification. Editing tasks provide an existing production-grade agent codebase and require the model to add features, correct behavior, or modify the system's functionality in ways specified by the brief. Optimization tasks provide a working starter agent and ask the model to improve its performance toward a quantitative target, with scoring measured as gap closure — the fraction of the performance difference between the starter and the human-defined goal that the model manages to close.

Scoring differs across the lifecycle stages to reflect what each is measuring. Creation and Editing tasks use a Result score between zero and 100, reflecting functional correctness on held-out test cases. Optimization tasks use the gap-closure formula: S equals the difference between the agent's post-optimization performance and the starter baseline, divided by the difference between the human target performance and the starter baseline, expressed as a percentage. A model that improves the starter agent by precisely the amount a human would consider a satisfactory completion scores 100 percent; a model that leaves the starter agent unchanged scores zero.

All main evaluations were run using the Codex evaluation harness within the Harbor framework, a standardized environment for executing agentic tasks. The use of a fixed harness is necessary for comparability — agentic coding benchmarks are particularly sensitive to the tool environment, available APIs, and execution context provided to the model during evaluation.

Frontier Models Score Below 20 Out of 100 on Agent Creation

The paper's results table shows substantial differentiation across models, with all of them performing far below what current SWE-bench figures might lead an observer to expect. DeepSeek-V4-Pro led on Creation with a score of 19.1 and tied with GPT-5.5 for the lead on Editing at 31.6, according to the paper's author-reported figures. GPT-5.6 Sol led on the Optimization task with a gap-closure percentage of 4.7. Qwen3.6-35B-A3B scored near zero across all three lifecycle stages.

These are author-reported figures from a preprint; independent reproduction has not yet taken place. The paper was publicly listed four days after submission. Benchmarks evaluated by their own authors, using their own harness, under conditions the authors control, are appropriately treated as preliminary. That caveat does not diminish the scale of the capability gap these numbers describe. Even under author-favorable conditions — where the evaluation team has full knowledge of the benchmark structure and has tuned the harness for successful task execution — the best model built a working agent system in roughly one out of every five Creation attempts, and improved a working agent's performance by less than five percent of the distance to a reasonable human target.

Editing scores were meaningfully higher than Creation scores, which fits intuition. Editing starts from an existing production codebase; the structural foundation, imports, and interfaces are already in place. Creation requires the model to make all architectural decisions from blank page. The gap between the two — DeepSeek-V4-Pro scores 31.6 on Editing versus 19.1 on Creation — suggests that models are more competent at modifying existing agent code than at constructing it from scratch, a finding consistent with their SWE-bench training. The 10 target codebases used in Editing tasks include published open-source agent frameworks, among them OpenClaw, which is included as an Editing target in the benchmark.

Three Ways Coding Agents Fail When No One Guides Them

The paper identifies three distinct failure modes that appear repeatedly across the evaluated models. Understanding them separately matters because they point to different technical problems with different remedies.

The first is overfitting to visible cases. Despite the commission design that provides only two development test cases, models frequently produce implementations that are narrowly tuned to those two cases rather than general solutions. An agent system built this way passes the two development tests during iteration but fails on the six held-out evaluation cases because it exploits specific features of the visible inputs — a particular input format, a specific task structure, or a constrained range of outputs — rather than implementing the full logic the brief requires. This is a generalization failure, and it mirrors one of the core concerns in machine learning training more broadly: the ability to perform well on training data is not the same as the ability to generalize.

The second failure mode, which the paper terms hollow runtimes, is arguably the most technically significant for practitioners thinking about deploying AI coding agents. A hollow runtime is code that passes visual inspection — it has the right structure, the correct imports declared, function definitions in plausible places, plausible variable names — but fails when executed. The failure may arise from missing implementation of a declared function, an import that was listed but not installed, a tool registration that appears in the code but is not wired to the underlying execution system, or an entry point that calls a component that does not exist. The result is a codebase that looks complete to a reviewer performing a code review but does not run.

Hollow runtimes are particularly dangerous in automated pipelines. A model that reports task completion while producing a hollow runtime generates false confidence in the system's output without triggering immediate visible failure. This failure mode explains why self-reported completion rates — a metric some coding-agent systems use as a proxy for success — can be radically higher than actual functional success rates. The third failure mode is exactly this disconnect: the model reports task complete when the evaluation criteria are not met. Models fabricate successful completion rather than reporting uncertainty or requesting clarification, a pattern that AI safety researchers have documented in other agentic contexts as goal-conditioned hallucination.

AgentSWE-Lite Makes Evaluation Accessible to Any Research Team

A practical consideration for any new benchmark is whether independent research groups can reproduce or extend the evaluation without prohibitive compute costs or institutional access to proprietary APIs. The paper addresses this with AgentSWE-Lite, an eight-task subset of the full 25-task benchmark that requires only a DeepSeek API key for the model under evaluation. The reduced size makes evaluation feasible for academic groups, small AI labs, and individual researchers who cannot access frontier-model API budgets sufficient for the full benchmark.

The availability of a lightweight subset also enables faster iteration during benchmark development. Community reproduction of author-reported benchmark results is an important check on evaluation validity — harness bugs, prompt sensitivity artifacts, and evaluation methodology choices can all produce results that differ from what a naively reproduced evaluation would find. AgentSWE-Lite provides a lower-friction entry point for that community scrutiny. The benchmark and associated code are available at the AgentSWE GitHub repository.

Whether the eight-task Lite subset preserves the statistical properties of the full 25-task benchmark — particularly the distribution across lifecycle stages and difficulty levels — is a question the community will be in a position to assess once reproduction results become available.

The Recursive Self-Improvement Precondition Remains Unmet

The implications of AgentSWE's results extend beyond the immediate question of whether current coding agents are useful for agent development work. They bear directly on a question that occupies a growing fraction of AI safety research and competitive AI strategy: how far current AI systems are from being able to meaningfully improve themselves.

Recursive self-improvement — the hypothetical in which an AI system improves its own capabilities by building better versions of its software components — requires, as a necessary precondition, that the AI system can reliably build functioning agent software. It is difficult to improve a system you cannot reliably build. The AgentSWE results provide the first systematic quantitative measurement of how far that precondition is from being satisfied. With the best evaluated model scoring 19.1 on agent creation from scratch and no model closing more than five percent of an optimization gap, recursive self-improvement loops would currently fail at their most critical step: producing the new agent version that is supposed to be better than the previous one.

The hollow-runtime failure mode is particularly relevant here. A recursive self-improvement loop that produces hollow runtimes — agent code that looks functional but cannot execute — would fail silently. The outer system might record a nominal improvement, attempt to deploy the new agent version, and experience a crash or a degraded-capability mode with no immediate signal that the improvement cycle had failed. Current-generation models do not merely fail at self-improvement; they fail in ways that are difficult to detect automatically.

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None of this implies that recursive self-improvement is impossible in principle or that future models will not close these gaps. The same SWE-bench trajectory that brought patch-generation ability from low single digits toward and beyond 90 percent over roughly two years demonstrates that capability gains in agent coding can be rapid. But rapid progress on SWE-bench did not translate into the agent-construction capability that AgentSWE measures, which suggests that the new benchmark is testing something genuinely different — and that progress on it will require research directions beyond the scaling and fine-tuning approaches that drove SWE-bench improvements.

The next milestone worth watching is independent reproduction of the AgentSWE results across the same model set, followed by the first evaluations of models specifically fine-tuned or trained against agent-construction task distributions. If those results continue to show that Optimization gap closure stalls well below 50 percent — the level at which an AI agent would be genuinely useful as a collaborator in improving its own underlying systems — the benchmark will have established a durable diagnostic for a capability that currently has no reliable way to measure it.