Translation

Building Reliable Agentic AI Systems

The article discusses how Bayer built reliable agentic AI systems by combining large language models with traditional software engineering practices, using a multi-agent architecture with guardrails, observability, and testing to ensure safety and consistency in drug development workflows.

Background

Martin Fowler is a highly influential software engineering thought leader, known for his work on software architecture, design patterns, and refactoring. This article discusses "agentic AI" — AI systems (like LLM-based agents) that can autonomously plan and execute multi-step tasks rather than just answering questions. The key challenge: LLMs are inherently unreliable (they hallucinate, make errors, and can get stuck in loops), so building production-grade systems around them requires robust engineering patterns: validation, logging, fallback logic, and careful prompt engineering. The article draws on lessons from Google and other large-scale deployments. This matters as companies increasingly rush to deploy AI agents for tasks like customer support, code generation, or data processing, often underestimating the reliability engineering needed.

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Building Reliable Agentic AI Systems

Background

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