Agent Loops: Verification Over Architecture
Nate Herk | AI Automationgo watch the original →
the gist
Agent loops are not about complex multi-agent swarms, but about designing a system where an AI agent iterates on a task using a clear, objective 'done' criteria and a verification step to improve output quality.
The Core Mechanism of Agent Loops
An agent loop is a recursive workflow defined by three components: a trigger, an action, and a stop condition. Rather than relying on a single prompt to generate a perfect result, the loop allows an agent to reason, act, observe the outcome, and iterate until a predefined goal is met. The primary value of this approach is moving the quality of the output closer to the desired result on the first attempt by outsourcing the feedback and iteration process to the agent itself.
Designing Effective Loops
Success in loop engineering depends on the quality of the 'done' criteria and the verification method. A loop is only as effective as the agent's ability to objectively check its own work against a target metric.
- Define Objective Metrics: Replace subjective goals like "until satisfied" with concrete metrics, such as "keep iterating until X metric equals Y result."
- Implement Verification Steps: Ensure the agent has the necessary tools to verify its output, such as running code tests, taking screenshots for visual inspection, or validating data against a reference.
- Use Hard Constraints: Prevent infinite loops and excessive costs by setting hard caps on the number of iterations or execution time.
- Select the Right Architecture: Most tasks do not require complex swarms or manager-helper hierarchies. A simple solo loop, where one agent reasons, acts, and observes, is often sufficient for knowledge work and coding tasks.
Practical Application
When building a loop, the agent should follow a structured cycle: plan the implementation, execute the task, observe the result, and compare the result against the "done" criteria. If the criteria are not met, the agent must refine its approach and repeat the cycle. This method is particularly effective for tasks like video editing, where an agent can cut transcripts and sync beats, or for generating code where the agent can test functionality in a browser or terminal before finalizing the output.