Review agent work plans and track user intent logs
I think it is a good approach for this year, but it misses something important - how do you know the code still reflects your intention after steering for weeks and months, across many sessions? Task: The author addresses intent drift in long-running AI coding sessions by logging every user message to a persistent chat log. Periodically, they review this log to distill the current user intent. Additionally, they generate work plans saved as files and have one or more agents review these plans against project constraints before execution. This workflow relies on custom or unnamed agents for the review steps.; tools: custom-built or unnamed agent. Human role: The original post does not say how a person stayed involved.; limitations: The method is described as project-specific with no general solution for testing or trust. The source does not detail the specific tools, costs, or integration requirements for the agents used. Human involvement is implied in reviewing logs and approving plans, but the exact approval workflow is not defined..
Tools
custom-built or unnamed agent
Published on Agent Work Atlas:
Overview
I think it is a good approach for this year, but it misses something important - how do you know the code still reflects your intention after steering for weeks and months, across many sessions?
Workflow
The author addresses intent drift in long-running AI coding sessions by logging every user message to a persistent chat log. Periodically, they review this log to distill the current user intent. Additionally, they generate work plans saved as files and have one or more agents review these plans against project constraints before execution. This workflow relies on custom or unnamed agents for the review steps.
Reported result
The source does not report measured outcomes, success rates, or specific failures. It presents the approach as a conceptual solution to trust and intent tracking issues in automated coding. No quantitative data is provided to validate the effectiveness of the chat log or plan review processes.
Human role
The original post does not say how a person stayed involved.
Limitations
The method is described as project-specific with no general solution for testing or trust. The source does not detail the specific tools, costs, or integration requirements for the agents used. Human involvement is implied in reviewing logs and approving plans, but the exact approval workflow is not defined.
Try it
Act as a project manager for an AI-assisted coding workflow. I need to implement a system to track user intent and review work plans. 1. Create a persistent log file named `intent_log.md`. Append every user instruction or message to this file with a timestamp. 2. Generate a work plan for the current task and save it as `plan.md`. 3. Simulate an agent review: Analyze `plan.md` against the latest distilled intent from `intent_log.md` and project constraints. Output a review report in `review_report.md` highlighting any misalignments. 4. Present the `review_report.md` to me for approval before proceeding with any code changes. Use [replaceable inputs] for project constraints and current task details. If no constraints are provided, ask for them. Do not execute code changes until I approve the review report.
Source reports are not independently verified. Publication date is the catalog publication date, not the original article date.