To get high-quality, autonomous work from an AI agent, you must treat it like a new hire, not just give it a simple prompt. You must provide a clear goal, specific skills (pre-defined knowledge), the right tools (APIs, etc.), and rich context (company data).

Treat your first AI agent like a new employee. Avoid giving it zero context or overwhelming it with a data dump. Instead, provide a focused briefing on who you are, what the specific job is, and point it to key resources. This onboarding process yields far better results than either extreme.

With AI agents, the key to great results is not about crafting complex prompts. Instead, it's about 'context engineering'—loading your agent with rich information via files like 'agents.md'. This allows simple commands like 'write a cold email' to yield highly customized and effective outputs.

For advanced AI models, providing a high-level goal rather than a detailed, prescriptive list of instructions often produces better outcomes. Over-prompting can constrain the model's intelligence, while a simpler prompt allows it to leverage its own planning capabilities for a more effective execution.

Contrary to intuition, providing AI with excessive or irrelevant information confuses it and diminishes the quality of its output. This phenomenon, called 'context rot,' means users must provide clean, concise, and highly relevant data to get the best results, rather than simply dumping everything in.

Simply giving an AI agent thousands of tools is counterproductive. The real value lies in an 'agentic tool execution layer' that provides just-in-time discovery and managed execution to prevent the agent from getting overwhelmed by its options.

The best prompts strike a balance between providing enough specific information (e.g., "include code excerpts") and not over-constraining the model. Adding a phrase like "whatever is needed to give me maximum context" gives the AI an "out" to use its own judgment and provide additional, helpful information you didn't ask for.

Instead of loading large context files on every turn, use "skills." The agent only sees a skill's name and description initially, loading the full instructions only when needed. This method, called progressive disclosure, drastically saves tokens and improves performance.

Top-tier language models are becoming commoditized in their excellence. The real differentiator in agent performance is now the 'harness'—the specific context, tools, and skills you provide. A minimalist, well-crafted harness on a good model will outperform a bloated setup on a great one.