Building AI Agents in Rust — part 4

📰 Towards AI

Learn to build AI agents in Rust using state machines for multi-step tasks, enabling more complex interactions and human approval gates

intermediate Published 22 Jun 2026

Action Steps

Implement a state machine in Rust to handle multi-step tasks
Define states and transitions for the agent's conversation flow
Use a loop to handle single-turn conversations and integrate with the state machine for multi-turn conversations
Add human approval gates to the state machine for more complex interactions
Test and refine the agent's state machine to ensure correct functionality

Who Needs to Know This

AI engineers and Rust developers can benefit from this article to improve their skills in building more sophisticated AI agents, while product managers can understand the capabilities and limitations of such agents

Key Insight

💡 State machines are essential for building AI agents that can handle complex, multi-step tasks and interactions

Key Takeaways

Learn to build AI agents in Rust using state machines for multi-step tasks, enabling more complex interactions and human approval gates

Full Article

Author(s): Enzo Lombardi Originally published on Towards AI. State machines for multi-step tasks The loop in Part 1 handles a class of question that fits in one breath: read this file, list that directory, answer the user. Two turns, three turns, done. As long as the model can plan and execute inside one conversation, the loop is enough. Beyond the initial loop, the article argues that real multi-step agent work needs a state-machine structure to handle composition of phases, human approval gate

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