The Tool Desk
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This is a risk-managed workflow recommendation, not a universal division of labor established by a controlled head-to-head trial. Task fit depends on the repository, its checks and conventions, the agent, and the quality of the specification.
Which pull request tasks suit an AI coding agent?
Start with work that is limited in scope, easy to describe, and straightforward to check. Treat the following as triage defaults—not guarantees that an agent will succeed or that every task in a category should be delegated.
| PR task | Default allocation | Conditions and review |
|---|---|---|
| Documentation, comments, release notes, and straightforward examples | An agent can draft or implement the change. | Specify the intended audience and source of truth. Check technical accuracy, links, and project terminology. |
| Routine chores, formatting, and mechanical build or CI updates | An agent can prepare a patch. | Keep the change small, state what must remain unchanged, and run the project’s checks. Inspect dependency and workflow changes closely. |
| A narrow bug fix with a reproducer and tests | An agent can investigate and propose a fix; a human confirms expected behavior. | Require a failing test or clear reproduction, inspect edge cases and the diff, and run relevant CI. Do not assume one agent is best at every fix. |
| New features, user-facing behavior, or ambiguous requirements | A human owns definition and design; an agent may prototype a bounded piece. | Resolve product intent and compatibility questions before implementation. Keep a person responsible for decisions about behavior. |
| Architecture, security-sensitive, data-handling, licensing, or policy-sensitive changes | Human-led; an agent may assist with analysis or a constrained patch. | Use an accountable reviewer with repository context. Check applicable security, licensing, contribution, and policy requirements. |
| Performance optimization, large refactors, or broad multi-file changes | Human-led investigation and decomposition; an agent can assist within a narrow unit. | Require profiling or other evidence for performance claims, stage the work, and examine scope and regression risk. |
What should remain a human responsibility?
Delegating implementation does not delegate ownership. A developer who understands the repository should make or confirm the decisions that determine whether a patch is appropriate:
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- Intent: define the user or maintenance problem, desired behavior, and what is explicitly out of scope.
- Constraints: identify compatibility needs, project conventions, architectural boundaries, and security or privacy requirements.
- Context: determine whether the change fits the codebase, its contribution rules, licensing obligations, and existing design.
- Acceptance: decide whether the proposed behavior is correct and whether the available tests meaningfully verify it.
- Merge decision: review the actual diff and validation results, request revisions where needed, and accept or reject the change.
For ambiguous work, have a person settle the requirement before asking an agent to implement it. That makes the agent’s task checkable and avoids treating a plausible-looking patch as proof that the underlying product decision was right.
How to delegate and review an agent-authored PR
- Write the acceptance criteria. State the intended result, relevant constraints, and what must remain unchanged. Link or provide the repository context the agent needs.
- Make the task independently checkable. Provide a reproducer, test, expected output, or other concrete validation path. If the requirement cannot yet be checked, resolve it with a human first.
- Limit the scope. Ask for a small patch or a defined subtask, particularly for refactors, performance work, and changes spanning multiple files.
- Inspect the diff, not just the summary. Check changed files and lines for unrelated edits, missing edge cases, and consistency with project conventions. A patch that is difficult to review creates risk even if it was quick to generate.
- Run relevant validation. Check tests, builds, static checks, and CI as appropriate, then decide whether those checks actually cover the requirement. Passing checks do not by themselves establish product fit, security, or policy compliance.
- Review the interaction as well as the code. Confirm that reviewer instructions were followed and revisions address the requested issue before approving or merging.
What does the evidence show—and what does it not show?
Published findings provide useful signals about task differences and workflow risks, but they measure different things. PR acceptance or merge rates, a controlled coding-assistance exercise, enterprise telemetry, and benchmark completion are not interchangeable measures of autonomous-agent performance.
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| Evidence | Reported result | How to interpret it |
|---|---|---|
| Task-stratified analysis of agent-authored PRs, 2026 | Among 7,156 PRs in the AIDev dataset, documentation PRs had 82.1% acceptance and new-feature PRs had 66.1% acceptance. | The authors report task type as an important factor and no agent leading across all task types. These are results for that dataset and its acceptance measure, not forecasts for a particular team. |
| Study of failed agentic PRs, MSR 2026 | The study examines 33,596 PRs across five agents; 24,014, or 71.48%, were merged. | The observed merge rate reflects the study’s sample and project selection. Rejection patterns include reviewer abandonment, unsuitable or duplicate PRs, incorrect or incomplete code, CI or test failures, licensing or contribution-policy violations, and failure to follow reviewer instructions. The study also examines changed lines and files, CI status, and review interactions, making review burden part of task fit. |
| GitHub Copilot Chat code-authoring and review exercise, 2023 | GitHub reports 36 participants with five to ten years of development experience; reviews were 15% faster and almost 70% of participants accepted comments from reviewers using Copilot Chat. | Participants worked on API endpoints in a controlled exercise. This concerns assistance with authoring and reviewing code, not agents independently completing production PRs. |
| GitHub report on its Accenture study, 2024 | GitHub reports an 8.69% increase in PRs per developer, a 15% increase in PR merge rate, and an 84% increase in successful builds for the observed Copilot setting. | The report describes an RCT and enterprise telemetry. These vendor-reported results concern that enterprise Copilot context; they are not a direct comparison of autonomous-agent-authored PRs with human-authored PRs. |
| GitHub discussion of agentic harness evaluation, 2026 | GitHub describes SWE-bench Verified as 500 human-validated bug-fix tasks from open-source Python repositories, and SWE-bench Pro as harder, multi-step work intended to reflect broader engineering tasks. | Benchmark results are conditional on the benchmark, model, agent, harness, and run. GitHub notes stochastic variation between runs; benchmark completion does not replace review and validation in the target repository. |
| Anthropic analysis of Claude Code usage, 2026 | The observational report describes approximately 400,000 Claude Code sessions from approximately 235,000 people, spanning October 2025 to April 2026. It reports that people commonly make planning decisions while Claude makes many execution decisions. | This is vendor-specific observational usage data, not a controlled comparison of PR outcomes or a universal prescription for how teams should divide work. |
How should a team compare agent-assisted and human-led work?
Use the same issue and repository context where possible, and evaluate the resulting work rather than relying on draft speed alone. Track:
- Correctness: whether the change meets the written requirement and handles relevant edge cases.
- Validation: whether tests, builds, static checks, and CI pass—and whether they cover the requirement.
- Scope: how many files and lines changed and whether unrelated edits appeared.
- Review effort: reviewer time, required revisions, and whether instructions were followed.
- Maintainability and fit: whether the patch follows project design and conventions and can be understood by the next maintainer.
- Outcome over time: whether the PR was accepted and merged, and whether it led to regressions or rework.
There is no established, controlled, representative head-to-head comparison across current agents, languages, repository types, and task categories in the cited evidence. Observed merge rates do not isolate the causal effect of using an agent. Teams should therefore treat task allocation as a policy to revisit using their own PR, CI, review-time, and regression data.
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