AI Agent Workflow
Understanding the complete workflow of an AI agent from goal input to result output gives you a clear mental model for building and using agent-based systems.
10 min•By Priygop Team•Updated 2026
Complete Agent Workflow
- 1. Goal input: the user provides a high-level goal ('Research competitors and write a report')
- 2. Goal understanding: the agent parses and clarifies the goal if needed
- 3. Planning: the agent creates a step-by-step plan using available tools
- 4. Step execution: for each step, the agent selects a tool and executes an action
- 5. Observation: the agent reads the result of the action
- 6. Memory update: the agent stores key information from the observation
- 7. Goal check: the agent evaluates whether the goal has been achieved
- 8. Re-planning if needed: if a step failed or the situation changed, the agent adjusts the plan
- 9. Result delivery: when the goal is complete, the agent delivers the result to the user
Diagram
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Deep Learning ⊂ Machine Learning ⊂ Artificial Intelligence
Key Takeaways
- Understanding the complete workflow of an AI agent from goal input to result output gives you a clear mental model for building and using agent-based systems.
- 1. Goal input: the user provides a high-level goal ('Research competitors and write a report')
- 2. Goal understanding: the agent parses and clarifies the goal if needed
- 3. Planning: the agent creates a step-by-step plan using available tools