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Coding agents can appear more capable because many coding tasks offer a concrete artifact to change and fast ways to check that change. Go-to-market (GTM) work—such as prospecting, campaign planning, and market decisions—often depends on shifting context, uncertain signals, and outcomes that take time to judge. That is a useful explanation for the perceived gap, not a measured finding: the available studies do not directly compare coding agents with GTM agents, and coding agents themselves remain unreliable in important settings.
Why task structure can favor coding agents
An agent has to do more than produce a plausible answer. It must gather information from an environment, take a sequence of actions, observe what happens, and adjust its strategy. Google Research’s January 2026 report describes this as sustained interaction under partial observability, with performance shaped by task decomposition, dependencies, and feedback.
Some coding tasks have a relatively legible loop: inspect a repository, edit files, run tests, and review the result. Tests do not prove that a change is correct in every respect, but they can provide quick, repeatable evidence about defined requirements. A task with a clear specification and a bounded change may therefore give an agent more useful feedback than work whose success depends on interpreting people, markets, or delayed business outcomes.
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More agents help only when the work can be divided
In its controlled evaluation of 180 agent configurations, Google Research found that coordination strategy mattered and that adding agents was not automatically beneficial. On one parallelizable finance-reasoning task, centralized coordination improved performance by 80.9% over a single-agent baseline. On the sequential PlanCraft planning task, tested multi-agent variants performed 39–70% worse. In the evaluated setup, independent multi-agent systems amplified errors 17.2 times, compared with 4.4 times for centralized systems. The study’s predictive model selected the optimal coordination strategy for 87% of unseen task configurations.
Those figures belong to the specific evaluation and tasks; they are not general performance forecasts for software development or GTM. They do illustrate a practical risk: splitting work is useful when subtasks can proceed independently, but harmful when later decisions depend on earlier ones or agents pass flawed assumptions along. GTM workflows can include both patterns. Researching separate market segments may be parallelizable; deciding whom to contact, what to say, and when to follow up can involve dependencies and noisy evidence. That application is analysis, not a finding from Google’s task set.
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How to judge whether a workflow is agent-friendly
Before comparing agents, examine the work they are expected to do. The following is a decision framework drawn from the task-structure, production, and coding-agent findings; it is not a measured coding-versus-GTM scorecard.
| Question | What to examine | Why it matters |
|---|---|---|
| Is the goal specific? | Can success and an acceptable output be described in advance? | Vague goals make it harder to distinguish a sound result from a plausible one. |
| Can the work be split? | Are subtasks independent, or does each step depend on the previous one? | Coordination can help parallel work and create overhead or error propagation in sequential work. |
| How quickly does feedback arrive? | Can the agent check an action’s result promptly, or must it wait for a delayed outcome? | Fast, informative feedback supports iteration; delayed or noisy signals make course correction harder. |
| How is correctness judged? | Can defined checks test the output, or does it require contextual human judgment? | Mechanical checks can catch some failures quickly, while judgment-heavy work needs review and clear evaluation criteria. |
| How stable is the context? | Does relevant information remain current and consistent, or change across sources and over time? | Partial, shifting context can make a once-reasonable action wrong or poorly grounded. |
| What if an error propagates? | Can a mistake be contained, or will downstream steps compound it? | Higher propagation risk calls for checkpoints, tighter coordination, or human approval. |
| Where does human review fit? | Which actions can proceed automatically, and which require a person to approve or evaluate them? | Supervision can catch errors, but it also defines the real operating model and workload. |
What deployed agents look like in practice
“Agents work” can suggest long-running autonomy, but production evidence points to a more supervised reality. Pan and colleagues’ 2026 paper, Measuring Agents in Production, combines 20 in-depth case studies with a survey of 86 practitioners working on deployed systems across 26 domains. In that surveyed sample:
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- 68% of agents executed at most 10 steps before human intervention.
- 70% relied on prompting off-the-shelf models rather than weight tuning.
- 74% depended primarily on human evaluation.
The authors identify reliability—consistent correct behavior over time—as the leading development challenge and describe systems-level design as a way practitioners address it. These are findings from the paper’s practitioner sample, not a census of deployed agents, and the study does not isolate GTM systems. They nevertheless help explain why a useful agent deployment is often a supervised workflow, not an unattended digital employee.
Coding-agent results are real but limited
Coding benchmarks and demonstrations can make coding agents look dependable, but success on a subset of tasks does not establish broad reliability. Mehtiyev and Assunção’s April 2, 2026 arXiv preprint analyzes 9,374 trajectories from 19 agents, eight coding-agent frameworks, 14 language models, and 500 tasks. The authors report that even top-ranked agents fail on more than 20% of benchmark problems.
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The paper also cautions against treating patch size or apparent task simplicity as a complete explanation for difficulty. Human annotators judged 12 never-solved tasks easy and requiring simple patches, yet every agent in the study failed on them. The authors point to architectural reasoning and domain knowledge as possible gaps. Successful trajectories more often gathered context before editing and spent effort validating results. These are patterns observed in that analysis, not universal causal rules or guarantees that following the same sequence will make an agent succeed.
Results also change with the coding context. A Montana Research Foundation review dated August 2026 summarizes two trials with substantially different conditions: a 2023 controlled Copilot trial in which developers completed a scoped, greenfield HTTP-server task 55.8% faster, and a 2025 METR randomized trial involving 16 experienced open-source developers and 246 real issues in their own mostly mature repositories, in which developers took 19% longer when AI tools were allowed. The percentages are the review’s summaries of those studies, not a direct comparison between agents or a prediction for all coding work. Bounded, well-specified work and issues embedded in mature systems are different tasks.
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Benchmarks and adoption metrics need context
A benchmark score is only as informative as its tasks, tests, and setup. The Montana Research Foundation review reports that OpenAI stopped evaluating models on SWE-bench Verified in February 2026 after auditing a selected set of failures. Material test or task-description problems were found in 59.4% of that audited set. That is the share of the selected failures audited—not the share of all benchmark tasks that were invalid.
The same review summarizes a 2024 DORA survey finding: a 25% rise in AI adoption was associated with a 1.5% fall in delivery throughput and a 7.2% fall in delivery stability. These are associations in survey data; they do not show that AI adoption caused the changes. Together with the varied coding trials, the figures argue for checking the conditions behind a result rather than treating any single benchmark, study, or adoption metric as a verdict on agent capability.
What the apparent GTM gap does—and does not—mean
The available evidence supports a cautious explanation: tasks with clear goals, useful feedback, manageable dependencies, and effective validation are easier to operationalize as agent workflows. Some coding tasks have these properties, while many GTM activities involve changing external context, judgment, and delayed outcomes. But neither category is uniform. Coding agents can fail on apparently simple benchmark tasks and slow experienced developers in mature repositories; GTM work can also be narrowed into bounded tasks with reviewable outputs.
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