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
Action Steps
- Implement a state machine in Rust to handle multi-step tasks
- Use the state machine to compose phases of agent execution
- Add human approval gates to the state machine for more complex interactions
- Test the state machine with different scenarios to ensure correct functionality
- Integrate the state machine with the initial loop from Part 1 for seamless conversation handling
Who Needs to Know This
Software engineers and AI researchers working on building AI agents can benefit from this tutorial to improve their agent's capabilities, while product managers can use this to inform their product strategy
Key Insight
💡 State machines are essential for handling complex, multi-step tasks in AI agents, allowing for more sophisticated interactions and human approval gates
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🤖 Build AI agents in Rust with state machines for multi-step tasks! 🚀
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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