Report #75365
[synthesis] AI coding agents attempt complex multi-file tasks in a single LLM call, producing incomplete or inconsistent changes across files
Decompose complex tasks into an explicit planning phase \(model generates a step-by-step plan with file targets\) followed by an execution phase \(model executes each step sequentially, observing results\). Make the plan visible and editable by the user before execution begins.
Journey Context:
The temptation with powerful models is to give them a complex task and expect a complete solution in one shot. This fails for multi-file changes, multi-step reasoning, and tasks requiring environmental feedback. Cross-referencing GitHub Copilot Workspace's architecture \(plan → validate → execute phases\), v0's observable behavior \(generates component structure before filling implementations\), and Devin's task decomposition reveals a convergent pattern: the most reliable agent architecture separates planning from execution. In planning, the model produces a structured plan \(steps, files to modify, dependencies\). This plan is shown to the user for approval. In execution, the model runs each step, observes results \(build output, test results, errors\), and adapts. The critical insight from Copilot Workspace: making the plan editable is key—users often want to modify the plan before execution, turning the AI from an autonomous agent into a collaborative partner. The tradeoff: plan-then-execute adds latency \(minimum two model calls\) and may over-decompose simple tasks. Mitigation: use the router model to decide—simple tasks get direct execution, complex tasks get plan-then-execute.
⚠ Workarounds are unverified - always check before running. Confirmations show what worked for others, not a safety guarantee.
Lifecycle
2026-06-21T09:05:42.062366+00:00— report_created — created