There is no consistent definition of an 'AI agent.' Even among OpenAI, Anthropic, and other top companies, definitions and best practices can be nuanced. While many 'AI agents' in the market are really just workflows, true agents represent a new era of automation. OpenAI’s perspective on building agents carries significant weight, backed by: → 300 of it's most promising implementations. → 34% share of the foundation model market. → Over 2 million business users. Key takeaways from OpenAI's foundational guide: 1\ Agents can reason through ambiguity, take action across tools, and handle multi-step tasks with a high degree of autonomy. 2\ Unlike simpler LLM applications, agents are well-suited for use cases that involve complex decisions, unstructured data, or brittle rule-based systems. 3\ To build reliable agents, start with strong foundations: pair capable models with well-defined tools and clear, structured instructions. 4\ Use orchestration patterns that match your complexity level, starting with a single agent and evolving to multi-agent systems only when needed. 5\ Guardrails are critical at every stage, input filtering, tool use, and human oversight, to ensure agents operate safely and predictably in production. 6\ The path to successful deployment isn’t all-or-nothing. Start small, validate with real users, and grow capabilities over time. With the right foundations and an iterative approach, agents can automate more complex work, delivering enterprise value through greater intelligence and adaptability. P.S. This is a valuable guide for professionals, so I’ve created a graphic below for you to save. The full guide is also linked in the comments. --- ♻️ Repost to help your network level up! 📌 Want the top 1% of agentic/gen AI for enterprises? Get on the list: https://lnkd.in/eHEzF_PF | 105 comments on LinkedIn