Building AI Agents in Rust — part 4

2026-06-21 · Source: Towards AI - Medium · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Advanced, quick

Summary

Part 4 of "Building AI Agents in Rust" introduces state machines as a robust solution for complex, multi-step AI agent tasks, contrasting them with the simpler "loop" approach from Part 1. The previous loop is inadequate for real-world scenarios involving phased operations like explore-plan-execute-verify, human approval gates, retryable failures due to transient issues, or the need to resume tasks after a process restart. State machines address these challenges by enabling typed state sharing, named transitions, durable checkpoints after each step, and the ability for nodes to pause and report reasons. This concept is exemplified by the Claude Code CLI's "EnterPlanMode" and "ExitPlanMode" tools and the LangGraph Python framework.

Key takeaway

For AI Engineers designing agents for production environments, recognizing the limitations of simple conversational loops is critical. You should adopt state machine architectures to manage complex, multi-step tasks, ensuring your agents can handle phased operations, integrate human approvals, gracefully recover from transient failures, and persist progress across restarts. This approach significantly enhances agent reliability and user experience for long-running or critical processes.

Key insights

State machines are crucial for AI agents to manage complex, multi-phase tasks, ensuring robustness and persistence.

Principles

• Complex agent tasks require structured state management.
• Durability and retries are essential for agent reliability.
• Explicit state transitions enhance agent control.

In practice

• Implement state machines for multi-phase agent workflows.
• Integrate human approval gates into agent processes.
• Design agents to resume tasks after system reboots.

Topics

• AI Agents
• Rust Programming
• State Machines
• Multi-step Tasks
• LangGraph
• Agent Orchestration

Best for: AI Engineer, Machine Learning Engineer, AI Architect

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Editorial summary, takeaway, and curation by AIssential. Original article published by Towards AI - Medium.