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An AI coding agent’s plan is an intention, not evidence that it changed your code or solved the problem. Reliable execution depends on the whole setup: the model, repository context, agent scaffold, tools, execution environment, and checks that verify the result. A convincing final message—or a plausible-looking patch—is not a substitute for confirming that the change was applied and relevant tests pass.
What “execute the plan” actually requires
A coding agent has to do more than generate a sequence of sensible steps. It needs to understand the relevant parts of a repository, make changes through available tools, run commands in an environment where those changes take effect, and gather evidence that the result meets the task’s requirements.
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Those are separate stages, and a run can break down at any of them. A model may misunderstand an issue, lack access to the file it needs, issue a tool call that fails, modify the wrong code, or stop before testing. Its final response may still describe the intended fix confidently. Treat the response as a report to verify, not proof of completion.
- Plan: The agent proposes an approach. No repository change has necessarily happened.
- Action: The agent reads files, edits code, or runs commands using its tools. Tool availability and the surrounding scaffold affect what it can do.
- Verification: Tests and other task-specific checks establish whether the applied changes work and whether they caused regressions.
This distinction is central to SWE-bench, which gives agents a software issue and repository, then evaluates proposed changes against tests expected to pass for the issue and tests intended to catch unrelated breakage. Passing only a narrow check—or merely producing a patch—does not establish that the issue is fully resolved.
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Why a capable model can still fail on a real repository
The model is only one part of the agent
The same model can perform differently when paired with different scaffolds: the software that supplies repository context, manages the task, and connects model output to tools. OpenAI’s February 2025 SWE-bench update reported that GPT-4’s SWE-bench Lite score ranged from 2.7% with an early retrieval-augmented-generation scaffold to 28.3% with CodeR. Those are historical results for the cited model, benchmark, and setups—not a forecast for a current local model. They do illustrate why a model name alone is an incomplete description of an agent’s capabilities.
Repository work is harder than producing plausible code
A proposed change has to fit the actual codebase, satisfy the issue, and avoid breaking behavior elsewhere. OpenAI describes SWE-bench Verified as a curated subset of 500 human-verified samples, created in response to concerns about assessing generated code and simulating real development tasks. The benchmark is more informative than judging a patch by how convincing it sounds, but its results still apply to its particular tasks and evaluation procedure.
Tool activity and verification can fail independently
Even a sound approach can fail if a tool call does not run, a command produces an error the agent misses, or the final edits are never tested. Conversely, a test may pass without covering the requested behavior. Reliable completion therefore depends on observable actions and checks suited to the specific issue—not the agent’s assertion that it is done.
What benchmark results do—and do not—tell you
Published scores are evidence about particular combinations of models, scaffolds, task sets, and evaluation methods. They are not universal success rates for local agents or guarantees about your repository.
| Reported result | What it describes | How to interpret it |
|---|---|---|
| 33.2% of SWE-bench Verified samples resolved | GPT-4o on the best-performing tested scaffold in OpenAI’s 2024 results, discussed in its update published February 24, 2025 | A result for that model and benchmark setup, not a current general agent success rate. |
| 2.7% to 28.3% on SWE-bench Lite | GPT-4 across the early retrieval-augmented-generation scaffold and CodeR in OpenAI’s February 2025 update | A reported spread across scaffolds; it shows why the surrounding system matters. |
| 194 of 500 problems, or 38.80% | Agentless results reported by Xia, Deng, Dunn, and Zhang in their July 2025 paper | A result for that method and benchmark configuration. The paper also reports more than 50% for one Claude 3.5 Sonnet configuration; that is likewise configuration-specific. |
| 37.2% on SWE-bench Verified | OpenHands LM 32B, as reported in its 2025 launch article | Attribute narrowly to that launch report and setup; it is not a general estimate of local-agent reliability. |
| More than 1,400 tasks valued at $1 million in real-world payouts | The tasks described for OpenAI’s SWE-Lancer benchmark, announced February 18, 2025; the announcement notes a July 28, 2025 dataset/results update | This describes the benchmark’s task collection and payout value, not an agent’s success rate. |
There is no general failure-rate figure in these sources that answers how often local coding agents fail to execute plans. The percentages above measure specific benchmark outcomes. They should not be combined or presented as a population-wide rate.
Planning is not the only route to solving a software issue
The 2025 Agentless paper describes a three-stage approach: localization, repair, and patch validation. The method deliberately excludes autonomous tool use and planning, and reports results on SWE-bench Verified. This shows that autonomous planning is not the only possible design for issue resolution; it does not establish that planning is generally counterproductive or that a staged method will suit every task.
For readers, the useful point is that “agent” can describe meaningfully different systems. One setup may let a model plan and call tools repeatedly; another may constrain the workflow to specific stages. Evaluate the result and the evidence for it, rather than assuming that more autonomy—or a stronger-sounding plan—means more reliable work.
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When an agent says it completed a task but the code is unchanged, incomplete, or broken, inspect the run as a sequence of events rather than guessing which part failed.
- Check the repository state. Inspect the relevant files and version-control diff to see whether the intended edits were applied. A description of edits is not the same as an applied change.
- Inspect tool calls and command results. Look for failed commands, missing permissions, unavailable files, or output the agent may have overlooked. OpenHands describes trace views that expose model calls, tool activity, timing, and cost for evaluation runs.
- Compare the edits with the task. Confirm that the change addresses the stated behavior rather than only altering a nearby file or silencing a symptom.
- Run relevant checks. Use tests that exercise the requested behavior, then run suitable regression checks. Record what ran and whether it passed; an unrun test provides no validation.
- Classify the failure. Separate a misunderstanding of the issue from missing repository context, a failed tool action, an incomplete edit, or a test gap. The remedy depends on where the run stopped.
Ways to make agent runs more reliable
Give the agent a concrete task and usable context
State the problem in terms of observable behavior, identify relevant constraints, and provide access to the repository context it needs to inspect. SWE-bench’s issue-and-repository setup reflects why both the task description and code context matter. Avoid defining success as “make a plan” or “fix it”; define what should change and how you will check it.
Make actions auditable
Keep records of model requests, tool calls, command output, and file changes. A trace makes it easier to distinguish “the agent intended to run tests” from “the tests ran and passed,” and to locate a failure without relying on the final summary. OpenHands’ evaluation index, dated February 20, 2026, describes trace visualization covering model calls, tool activity, timing, and cost.
Validate both the requested behavior and regressions
Run checks tied to the issue, then check for breakage outside the immediate change where practical. SWE-bench’s evaluation design uses tests associated with the issue as well as tests intended to catch unrelated regressions. A test suite cannot prove that every possible behavior is correct, but a relevant, passing test gives stronger evidence than a plausible explanation alone.
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Record the model, scaffold, tools, repository context, and evaluation procedure when comparing agents. Choose tasks that resemble the work you need done. OpenHands’ index groups evaluations into issue resolution, greenfield development, frontend work, software testing, and information gathering; results in one area do not automatically establish performance in another.
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Treat hardware as a constraint, not a cure-all
OpenHands’ 2025 launch article recommends GPU-backed serving with SGLang or vLLM for its OpenHands LM 32B model. That recommendation is specific to that model and serving setup. It does not establish a universal hardware requirement for local agents, identify a particular GPU or memory capacity, or show that faster inference fixes weak repository context, tool integration, or missing validation. Check the requirements for the exact model and serving stack you intend to use.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What to check when evaluating an agent
- Task fit: Has it been evaluated on the kind of work you need—issue repair, application development, frontend changes, test generation, or information gathering?
- Completion evidence: Are changes judged by applied edits and relevant tests, rather than by a self-reported success message?
- Observability: Can you review model calls, tool actions, command results, timing, and costs when a run goes wrong?
- Evaluation quality: Are the tasks and tests curated well enough to represent the intended work and avoid overly narrow or invalid checks?
- Local serving fit: Are the model size and serving requirements compatible with your hardware and software stack?
Benchmark scores can help narrow comparisons, but they cannot replace a trial on representative tasks using the complete setup you plan to run. Treat a result as evidence about that configuration and test process—not as proof that every proposed plan will be carried out correctly.
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