agentic-loop-skill

You build an autonomous AI agent by implementing an iterative problem-solving loop with OBSERVE-THINK-ACT-REFLECT-DECIDE stages. This approach enables the agent to self-delegate subtasks, learn from execution outcomes, and recover from errors across repeated cycles without manual orchestration.

The self-delegation pattern in autonomous agent workflows is a mechanism where the agent dynamically assigns subtasks to itself to refine results. It allows the AI to manage context efficiently across iterations by breaking complex decisions into smaller, observed, and reflected-upon tasks.

To implement an observe-think-act-reflect-decide loop, instruct your AI agent to enter an iterative cycle where it observes state, thinks to determine next actions, executes tasks, reflects on the outcomes, and decides if the goal is achieved. The agent repeats this until the final objective is reached.

Yes, iterative agent loops are specifically designed to manage context and recover from errors in multi-step workflows. By applying self-delegation and reflecting on task outcomes, the agent identifies execution failures and adjusts its subsequent actions to achieve the objective.

Use an iterative self-reflection loop for complex, multi-step decision tasks rather than standard execution. It is necessary when your AI workflow requires repeated cycles of observation and reflection to handle dynamic state changes, error recovery, and context management.