Known Agent Issues

Known integration caveats for ai code agents used with aitasks

This page tracks current workflow issues by code agent. Issues are grouped by agent below.

Claude Code

Medium-effort models can miss workflow steps

In strict multi-step aitask-* workflows, medium-effort models may skip required checkpoints or finalization steps.

Use stronger reasoning/model settings when you need reliable workflow compliance. If the agent stops mid-workflow before a final step (for example, the satisfaction-rating prompt), nudge it to “continue the workflow” or “finish the workflow” to complete the remaining steps.

Codex CLI

Interactive checkpoints

ait setup enables the default_mode_request_user_input feature in the generated Codex config (.codex/config.toml), so request_user_input is available in Codex’s default mode. This makes the interactive workflow checkpoints — task confirmation, plan approval, and commit review — available during the aitask-* workflow. Availability is necessary but not sufficient for reliable compliance: see Reasoning effort and workflow compliance below.

ait codeagent invoke launches the planning skills (pick, explore) through plan mode — it reliably surfaces their commit and merge approval prompts and suits the planning phase. The analysis skills (qa, explain) run in Codex’s default mode.

Reasoning effort and workflow compliance

Set Codex’s reasoning effort to at least high when running the aitask-* workflow. At lower effort, Codex may silently skip required non-skippable workflow steps and treat the archive as the end of the workflow even when the interactive prompts are available. Raising the effort to high resolves most of these compliance problems.

When you change the effort setting, Codex also asks whether to override the current plan-mode effort setting — accept it so the planning phase runs at high effort too.

Some skipped prompts are not compliance failures: execution profiles such as fast deliberately pre-answer prompts like task confirmation, email, and worktree creation. Those skips are expected — the caveat here is only about steps no profile pre-answers.

Workaround: even at high effort, Codex may occasionally stop mid-workflow before a final step (for example, the satisfaction-rating prompt). If it does, prompt it to “continue the workflow” or “finish the workflow” so it completes the remaining steps. This is not unique to Codex (see Medium-effort models can miss workflow steps).

Model self-identification is unreliable

Codex CLI models cannot reliably self-report their model ID when prompted. The framework falls back to reading the configured model from ~/.codex/config.toml, but this may not reflect the actual model if it was overridden at invocation time via CLI flags.

Workaround: Launch Codex CLI via ait codeagent invoke instead of calling codex directly. The wrapper sets the AITASK_AGENT_STRING environment variable with the correct agent string, ensuring accurate implemented_with metadata.

OpenCode

Plan mode may skip task locking

When OpenCode runs in plan mode, interactive skills (aitask-pick, aitask-explore, aitask-review, aitask-fold) may skip the task locking step because plan mode restricts the agent to read-only tools.

Recommendation: Use OpenCode in regular mode (not plan mode) for interactive skills that acquire task locks. These skills have their own internal planning phases.

Shallow implementation plans

OpenCode may produce high-level overviews instead of detailed step-by-step implementation plans during the task-workflow planning phase. The opencode_planmode_prereqs.md file contains explicit instructions to mitigate this, but results may vary by model.

Workaround: If the agent produces a shallow plan, prompt it directly: “Please make a detailed plan of which files will be edited with which changes.” This usually triggers the agent to expand the plan with specific file paths, exact modifications, and code snippets.

References


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