AI Prompts for Full Loop Engineering Workflow
Free tested AI prompts for Full Loop Engineering Workflow. Built for real results you can use right away.
Free AI prompts for Full Loop Engineering Workflow, tested and ready to use right now.
AI Prompts for Full Loop Engineering Workflow
Free tested AI prompts for Full Loop Engineering Workflow. Built for real results you can use right away.
Browse top AI prompts for Full Loop Engineering Workflow across plan the full loop, and more. Every prompt in this guide is free to copy and built for real results. No prompt engineering experience needed.
Stage 1
Plan the Full Loop
A production loop needs to be designed as a system, not assembled from parts. These prompts help you create the complete blueprint before you configure anything.
Write the full loop design document
Write a complete loop design document for [YOUR_PROJECT]. The document should cover: (1) the loop goal and success metric; (2) the trigger and schedule; (3) the agent roles and their responsibilities; (4) the tool connections and MCP servers required; (5) the state files and their formats; (6) the verification conditions; (7) the escalation and failure protocols; (8) the rollout plan (manual first run, then scheduled). Format this as a structured markdown document I can commit to the repo as LOOP.md.
Identify and scope the first sprint
I want to run my first production loop sprint for [YOUR_PROJECT]. Help me scope it. The sprint should: be completable in under one week of loop time, have a clear verifiable end state, not touch the deployment pipeline, and produce work I can review in under 30 minutes. Based on the following current state of the codebase [DESCRIBE YOUR CODEBASE STATE], propose the top 3 candidate sprints and recommend the best one to start with, with justification.
Write the loop kick-off prompt
Write the loop kick-off prompt for [YOUR_PROJECT]. This is the prompt I run once to initialize the loop for a new sprint. It should: read LOOP.md and AGENTS.md to understand the architecture, initialize the state files for this sprint, verify the MCP connections are working, run a health check on the test suite, write the first task queue to .agent/tasks.md, and output a plain-language summary of the sprint plan and the first three tasks. Write the full kick-off prompt.
Design the human review checkpoints
My loop for [YOUR_PROJECT] runs autonomously but I want to review its work at specific points. Design the human checkpoint protocol: when does the loop pause for my review (after each task, after each batch, only when escalation is triggered), what summary does it present when pausing, how do I signal it to continue versus revert the last batch, and how does it pick up from a human-approved checkpoint. Write the checkpoint protocol and the resume prompt.
Write the loop retrospective prompt
At the end of each week, I want my loop to generate a retrospective for [YOUR_PROJECT]. Write the retrospective prompt that reads the done log, the failure log, and the state file history, and outputs: total tasks completed, tasks that required multiple attempts and why, any patterns in what the loop got wrong, the average iterations per task, and three recommendations for improving the loop's AGENTS.md or task queue format. Write the retrospective prompt and the report format.
Frequently asked questions
How long does it take to set up a production agent loop?+
A minimal working loop, one that runs a scheduled task, does something useful, and updates a state file, can be set up in a day once you have your AGENTS.md, CLAUDE.md, and state files in place. A production loop with sub-agents, MCP connectors, and verification conditions typically takes a week of iteration. The prompts in this guide are designed to get you to a working first sprint quickly, then let you add complexity only where you need it.
What is the right first sprint for a new agent loop?+
The best first sprint is the smallest thing that proves the loop works end to end: a scheduled task that reads one file, makes one kind of change, runs the test suite, commits the result, and updates the state file. Resist the urge to build sub-agents and MCP connectors in the first sprint. Prove the basics run reliably first, then layer in complexity.
How do I know when to intervene in a running loop?+
Design explicit checkpoints rather than monitoring continuously. A well-designed loop should post a summary to a Slack channel or write to a file you can tail at the end of each run. If a run fails verification more than twice in a row, that is the signal to step in. The review prompts in Stage 4 of this guide help you build the checkpoint protocol before you need it.
What does a good loop retrospective include?+
A useful retrospective covers four things: tasks completed versus attempted, the average number of iterations per task, patterns in what the loop got wrong repeatedly, and specific edits to AGENTS.md or the task queue format that would help. Anything shorter is a summary, not a retrospective. The Stage 1 prompt in this guide generates the full format automatically from the done log and failure log.
How do I know if my loop is making real progress or running in circles?+
The done log is the most reliable signal. If .agent/done.md grows with verified, committed tasks each run, the loop is working. If the state file shows the same task in "in-progress" status across multiple runs without moving to done, the task is either too large, the stop condition is wrong, or the verification agent is blocking on something fixable. The loop retrospective prompt in Stage 1 surfaces this pattern automatically.
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