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A coding agent’s plan is only an intention. To count as completed work, it must inspect the right repository context, make changes through working tools, and pass checks that show the fix works without breaking unrelated behavior. A fluent explanation or plausible patch is not proof that the agent executed its plan.
Why can an agent describe a fix but fail to deliver it?
Planning and software execution are different capabilities. A model can propose sensible steps, but an agent also needs access to the relevant files, tools that can read and change them, a usable execution environment, and a way to check the result. A failure anywhere in that chain can leave the plan intact while the code remains unchanged—or changed in the wrong way.
That is why reliability is a system property, not a model trait alone. The model, agent scaffold, repository context, tools, and evaluation procedure all affect the outcome. OpenAI’s February 2025 SWE-bench update illustrates the scaffold effect: it reported GPT-4 SWE-bench Lite results ranging from 2.7% with an early retrieval-augmented scaffold to 28.3% with CodeR. Those are historical results for the reported benchmark setups, not a forecast for a current local model or a measure of every coding task.
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What counts as successful execution?
Judge the result by observable changes and relevant checks, not by the agent’s final message. SWE-bench gives an agent a repository and an issue description, then evaluates the proposed change against tests expected to pass after the fix and tests expected to remain passing. The second group matters: a patch that fixes the reported symptom but breaks existing behavior is not a successful resolution.
- Plan: the agent describes intended actions. This demonstrates neither that a tool ran nor that a file changed.
- Patch: an edit exists. It may still be incomplete, misplaced, or incompatible with the project.
- Verified result: the applied change addresses the task and passes the relevant checks, including regression tests where available.
OpenAI’s Introducing SWE-bench Verified, published August 13, 2024 and updated February 24, 2025, describes the evaluation challenge: “Evaluating these capabilities is challenging due to the complexity of software engineering tasks, the difficulty of accurately assessing generated code, and the challenge of simulating real-world development scenarios.” A benchmark is useful evidence, but it is not a guarantee about a different repository or test suite.
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What benchmark results say—and what they do not
These figures show outcomes in named evaluations, not universal odds that an agent will finish your task. The setups and task sets differ, so the values should not be treated as a direct ranking.
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| Evaluation and reported result | What the result describes | How to interpret it |
|---|---|---|
| SWE-bench Verified: 500 human-verified samples | OpenAI’s description of the Verified subset, on a page updated February 24, 2025. | The subset is curated; it is not a random sample of all software work. |
| SWE-bench Verified: 33.2% resolved | OpenAI reported this result for GPT-4o on the best-performing tested scaffold in its 2024/2025 update. | It applies to that model, scaffold, benchmark, and evaluation setup—not to local models in general. |
| SWE-bench Lite: 2.7%–28.3% | OpenAI reported this spread for GPT-4 across the cited scaffolds in 2025. | It demonstrates how much the surrounding scaffold can matter in that evaluation. |
| SWE-Lancer: more than 1,400 tasks valued at $1 million in real-world payouts | OpenAI’s benchmark announcement, published February 18, 2025; its page notes a dataset/results update on July 28, 2025. | Task count and payout value describe the benchmark’s scope, not a model success rate. |
| SWE-bench Verified: 37.2% resolved | OpenHands reported this for OpenHands LM 32B in a 2025 launch article. | This is a source-attributed launch result for a particular model and setup, not a general local-agent rate. |
| SWE-bench Verified: 194 of 500 problems (38.80%) | Xia, Deng, Dunn, and Zhang reported this Agentless result in their July 2025 paper. The paper also reports more than 50% for one Claude 3.5 Sonnet configuration. | Results depend on the model and configuration; the figures are not interchangeable with other benchmark rows. |
OpenAI’s February 18, 2025 SWE-Lancer announcement summarized its findings this way: “We evaluate model performance and find that frontier models are still unable to solve the majority of tasks.” That is a statement about the evaluated frontier models and SWE-Lancer tasks, not a measured failure rate for local coding agents.
Does reliable issue fixing require an autonomous plan?
No single architecture is established as the only route. The 2025 Agentless paper describes a bounded three-stage approach: localization, repair, and patch validation. It deliberately disallows autonomous tool use or planning, yet reports 194 resolved problems out of 500 on SWE-bench Verified. The result shows that an autonomous planning loop is not necessary in every issue-resolution approach; it does not show that planning is generally harmful or that Agentless is best for every kind of coding work.
How to make a coding-agent run easier to trust
- Define a concrete task and completion condition. Provide an issue description and let the agent inspect the relevant repository. State what observable change is required and which checks should establish completion. Avoid defining success as “explain the fix” or “say when done.”
- Inspect the actual work product. Check whether files changed, whether the changes address the requested behavior, and whether the agent’s summary matches the diff. Treat a proposed patch and an applied patch as different states.
- Run task-specific checks and regression tests. Use the project’s relevant test commands or validation process. A successful run should establish both that the requested behavior works and that unrelated behavior remains intact where tests cover it.
- Keep a trace of the run. Record model requests, tool calls, command results, file changes, timing, and cost where the agent environment supports it. OpenHands describes trace visualization for model calls and tool activity, along with timing and cost. A trace makes it possible to locate whether a failure came from the model’s decision, a tool action, or a command result instead of guessing from the final response.
- Compare complete configurations. When testing agents, record the model, scaffold, available tools, repository context, and evaluation procedure. Changing only the model name does not make a fair comparison if the surrounding systems differ.
- Choose evaluations that match your work. Issue repair, greenfield application development, frontend work, testing, and information gathering are distinct capabilities. OpenHands’ evaluation index, dated February 20, 2026, organizes evaluations across those areas; a result in one area does not establish performance in another.
When is local hardware the problem?
Hardware can constrain whether a particular model can be served at useful speed, but it is only one part of the execution chain. OpenHands’ 2025 launch article recommends GPU-backed serving for its 32B model using SGLang or vLLM. That recommendation is specific to that model and serving setup; it does not establish a universal GPU requirement for local agents or show that a GPU resolves poor task framing, missing repository access, broken tool integration, or absent validation. Check requirements for the exact model and serving stack you intend to run.
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If an agent produces good plans but no code changes, inspect whether it had access to the repository and whether its tools actually ran. If code changed but the task still fails, inspect the diff and test output. If tests pass but regressions appear, the checks may not cover the affected behavior. These are different failure points and call for different fixes.
How should you compare coding agents?
- Task fit: Does the evaluation resemble your work—repository issue repair, greenfield development, frontend changes, test generation, or information gathering?
- Completion evidence: Are results judged by applied changes and tests, rather than by a convincing explanation?
- Observability: Can you inspect model calls, tool actions, timing, and costs after a run fails?
- Evaluation quality: Are tasks and tests curated and representative, with checks against invalid or overly narrow test cases? OpenAI describes curation concerns and the construction of the SWE-bench Verified subset.
- Local serving fit: Are the model size and hardware requirements appropriate for the exact serving stack? Do not infer a universal hardware recommendation from one model-specific example.
Benchmark results are best used to narrow questions, not settle them: what task was tested, which complete setup was used, and what evidence counted as success? The practical test is still whether the agent can make and verify the right change in the repository you care about.
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