Loop Engineering
Over the past year, AI has been constantly bringing new terms and practices into our work. Yet another XX Engineering probably doesn't already surprise anyone.
Let's take a look at Loop Engineering, which is hyped right now.
The main idea is that an engineer no longer prompts an agent directly. Instead, they design a system that does it on its own. A loop here can be thought of as a recursive goal: you define the purpose, and the AI iterates until complete.
A typical loop consists of the following steps:
🔸 Discovery: Define what should be done during the iteration. The key is letting the agent find its own work rather than providing it manually.
🔸 Handoff. Move the task from the scheduling system into the hands of the agent that does the work.
🔸 Verification. Check whether the result satisfies the goal. This is what prevents the loop from blindly moving forward.
🔸 Persistence. Save the result somewhere it can survive between sessions and agents, e.g., PR, issue tracker, database, etc.
🔸 Scheduling. Define when the loop should run. For example, an automated morning CI triage.
Building blocks for the loop:
🔸 Automations. Automated procedures or triggers that start the loop.
🔸 Worktrees. Built-in git mechanism for multiple independent working directories in one repo to allow agents to work independently.
🔸 Skills. Project knowledge and instructions.
🔸 Plugins\Connectors. Tools (usually MCPs) that give agents access to the outside world: issue trackers, databases, APIs, and other systems.
🔸 Subagents. Separation of tasks and responsibilities. For example, one agent does the work while another verifies it.
🔸 Memory. Long-term storage for task state, execution results, and lessons learned.
The overall concept is pretty cool, but it requires very strong engineering discipline.
The cost of an incorrectly designed loop is much higher than the cost of a bad prompt. In a loop, an error can accumulate with every iteration, the result can gradually drift away from the goal, and a lot of tokens can be just wasted.
That's why verification becomes one of the most critical parts of Loop Engineering. If we want autonomous loops to produce stable results, they need a reliable way to verify their own work.
For a deeper dive:
- Getting Started with Loops Claude blog
- Loop Engineering: The Anthropic Playbook for Designing Systems That Prompt Your Agents
- Loop Engineering by Addy Osmani
- Practical Loop Engineering by Addy Osmani
#engineering #ai
Over the past year, AI has been constantly bringing new terms and practices into our work. Yet another XX Engineering probably doesn't already surprise anyone.
Let's take a look at Loop Engineering, which is hyped right now.
The main idea is that an engineer no longer prompts an agent directly. Instead, they design a system that does it on its own. A loop here can be thought of as a recursive goal: you define the purpose, and the AI iterates until complete.
A typical loop consists of the following steps:
🔸 Discovery: Define what should be done during the iteration. The key is letting the agent find its own work rather than providing it manually.
🔸 Handoff. Move the task from the scheduling system into the hands of the agent that does the work.
🔸 Verification. Check whether the result satisfies the goal. This is what prevents the loop from blindly moving forward.
🔸 Persistence. Save the result somewhere it can survive between sessions and agents, e.g., PR, issue tracker, database, etc.
🔸 Scheduling. Define when the loop should run. For example, an automated morning CI triage.
Building blocks for the loop:
🔸 Automations. Automated procedures or triggers that start the loop.
🔸 Worktrees. Built-in git mechanism for multiple independent working directories in one repo to allow agents to work independently.
🔸 Skills. Project knowledge and instructions.
🔸 Plugins\Connectors. Tools (usually MCPs) that give agents access to the outside world: issue trackers, databases, APIs, and other systems.
🔸 Subagents. Separation of tasks and responsibilities. For example, one agent does the work while another verifies it.
🔸 Memory. Long-term storage for task state, execution results, and lessons learned.
The overall concept is pretty cool, but it requires very strong engineering discipline.
The cost of an incorrectly designed loop is much higher than the cost of a bad prompt. In a loop, an error can accumulate with every iteration, the result can gradually drift away from the goal, and a lot of tokens can be just wasted.
That's why verification becomes one of the most critical parts of Loop Engineering. If we want autonomous loops to produce stable results, they need a reliable way to verify their own work.
For a deeper dive:
- Getting Started with Loops Claude blog
- Loop Engineering: The Anthropic Playbook for Designing Systems That Prompt Your Agents
- Loop Engineering by Addy Osmani
- Practical Loop Engineering by Addy Osmani
#engineering #ai