How can I make AI Agents more reliable and restrict the actions they can take?

Anthropic's research emphasizes that the most effective large language model (LLM) agents use simple and composable patterns rather than complex frameworks, but even simple agents can produce errors such as hallucinations, incorrect tool usage, or ignoring instructions. To mitigate these issues, the article discusses applying layered controls across the AI agent lifecycle, from model selection and prompt structuring to output schema validation and tool design. It highlights the importance of proactive controls and design choices to enhance agent reliability, making their runtime behavior more predictable and reducing subsequent evaluation and monitoring costs. The article also explores practical implementation using n8n, an AI-native workflow automation platform, which provides built-in nodes for each control type to streamline the process without extensive coding. Key considerations include configuring model parameters, structuring prompts with context and constraints, enforcing consistent output formats through JSON schemas, and applying guardrails to handle unsafe inputs and outputs. Additionally, it stresses the significance of logical routing and sub-workflows to ensure the agent's actions are controlled and reliable, advocating for a methodical approach to building AI agents with continuous testing and improvements.