Video Summary
Current models are very capable; the real advantage comes from the context and harness you build around them.
agent.md/claude.md files load full text every turn and waste tokens; 95% of users can avoid them.
Skills use progressive disclosure (only name+description in context) so the agent loads full details only when needed.
Build skills by walking the workflow with the agent, produce a successful run, then have the agent write and iteratively refine the skill.
Scale by starting with one reliable agent and expanding to sub-agents after workflows and skills are stable.
Only when you have truly proprietary or company-specific information that must be present on every single turn. For most users (about 95%), that constant inclusion just wastes tokens and degrades performance.
Skills use progressive disclosure: only the skill name and short description are in context each turn. The agent fetches the full skill file only when needed, reducing typical per-turn token cost from thousands to a few dozen tokens.
Identify the workflow, walk the agent step-by-step through a successful run while correcting mistakes, then ask the agent to codify that successful run into a skill file and iteratively refine it when failures occur.
Start with a single agent and build robust skills and workflows first. Add sub-agents only after core workflows are reliable—avoid jumping to multi-agent setups for novelty.
"Context is the model assembling information that it needs to execute an action."
Understanding how context is utilized in AI agents is essential for effective use. Context refers to the information that the AI model needs in order to make informed decisions and perform actions. This involves a general system prompt that guides the model on how to operate.
Many people believe they need extensive context setup, but the speaker indicates that "95% of people don't need this," highlighting that the models are proficient enough to handle tasks without additional context in many cases.
"Skills are designed in a way that's called progressive disclosure."
Skills within AI systems are structured to optimize efficiency through progressive disclosure, meaning only the title and description of a skill are added to the context initially. This prevents unnecessary information overload and token waste.
The speaker argues against the common practice of adding extensive context with agent.mmd files, which could lead to large amounts of unnecessary data being processed each time the agent operates.
"A lot of people will identify they have a workflow and then they'll jump to create the skill right away—this is the worst thing you can do."
The speaker emphasizes that understanding a workflow is crucial before creating a skill. Rushing into skill creation without fully grasping the workflow can lead to inefficiencies and improperly structured skills.
Using a systematic approach to identify the necessary steps and guide the model appropriately will enhance the performance and decision-making ability of the AI agent, particularly through structured skills that provide context when required.
"You have to walk with it. I told it, 'Okay, this is how you research.' And it's like, 'Okay, it researches.'"
Experiential learning is crucial when training AI agents. Rather than simply giving commands and expecting them to perform correctly, it is vital to guide the AI step-by-step through the desired workflows.
In practical applications, such as analyzing a company for YouTube, actively involving the AI in research tasks can lead to improved outcomes. Specific inquiries regarding various aspects of the company and how to handle them can result in better decision-making by the AI.
Frustration often arises when AI fails to achieve desired results. However, this is often due to a lack of context and guidance, highlighting the necessity for a clear instructional framework.
"It feels like it understands. It feels like it thinks. Heck, it even feels like it has emotion."
AI models do not possess true understanding; rather, they map inputs to outputs based on patterns identified during training. They mimic human-like responses without actually "thinking" or comprehending the material.
For effective training, users need to treat AI as a new employee. This means elucidating the workflows, identifying right and wrong actions, and iterating the processes regularly.
It is essential to provide AI with the context of a successful operation. Without this background, AI skills developed by other users may not be functional, as they lack the necessary parameters tailored to a specific workflow.
"The best way to create a skill is to work with it in your specific workflow."
The process of skill creation in AI should be iterative. Initially, one must actively engage with the AI to establish workflows and teach it the characteristics of successful outcomes.
After achieving a successful run with the AI, users should instruct it to review its actions and codify these into a skill. This promotes a more accurate and reliable performance in future tasks.
Many users err by downloading skills from others without understanding their context, which can lead to ineffective use of AI capabilities. Instead, creating custom skills to fit one's unique workflow is encouraged.
"You have to sort of... put in the work and build it up."
Users aiming for productivity should focus on building AI capabilities from the ground up rather than relying on pre-built systems that may not suit their needs.
The notion of scaling productivity emphasizes starting with one agent, developing it, and then gradually incorporating additional capabilities, such as sub-agents, to enhance efficiency.
This approach is akin to managing a new team—starting simply and expanding as familiarity and proficiency grow, ensuring better control and understanding of the processes involved.
"If you don't know how to build skills or use AI, people say you're joining the permanent underclass."
The fear of job loss due to AI advances is valid, especially with the rise of AGI. Individuals lacking knowledge of AI tools and their applications may find themselves at a significant disadvantage.
The term "permanent underclass" emphasizes the urgency for non-technical individuals to acquire skills in working with AI. The makeup of the workforce is changing, and adapting to these tools will be crucial for maintaining relevance.
It is crucial to understand that while AI can replace certain tasks, those who develop the ability to utilize these technologies effectively can thrive in the evolving landscape.
"There needs to be this level of delusion where you're like, 'This is just going to work out. We're just going to launch the product, and if it doesn't, on to the next one.'"
The conversation highlights a transition in mindset regarding product development and skill acquisition. It emphasizes the importance of not overthinking and the value of releasing products to the market, recognizing that failure is a part of the process.
Building one's own skills is encouraged, similar to the idea of preparing homemade meals rather than relying on fast food. This analogy stresses the significance of self-sufficiency in skill development.
Skills are highlighted as increasingly valuable, particularly in coding areas where AI agents excel at generating code across various frameworks.
"Code itself has become context now."
The video discusses how the relevance of templates has resurfaced in the coding landscape, where having a good foundational template can serve as a context for agents to build upon.
It points out that excessive specifications, such as detailed MD files for specific tech stacks, are often unnecessary. Instead, a minimal contextual approach combined with solid skill foundations is preferable.
The speaker suggests that iterative skills development, through systematic refinement of workflows, fosters successful project completion and reduces the likelihood of errors.
"When it messes up, thank God. You don't complain... This is the moment where you identify the error."
The dialogue introduces the concept of recursively building skills, which involves continuously refining and updating skills based on previous failures and feedback from AI agents.
It encourages users to actively engage with the AI when errors occur by identifying issues and instructing the agent to correct them, thereby updating skill files to prevent future mistakes.
The speaker shares a personal example of developing a report generator, illustrating the importance of patience and iterative improvement in crafting effective tools and skills.
"There's going to be two, three, five, six hiccups. Over time it should be good."
It is underlined that initial expectations regarding the functionality of AI tools should be adjusted, as users may encounter multiple challenges before achieving effective performance.
The speaker elaborates on the initial uncomfortable stage of working with AI, recalling personal experiences of frustration with new tools.
The overarching message is to embrace the learning curve associated with AI integration, as context and familiarity with workflows significantly enhance the efficiency and reliability of AI as a supportive tool.
"The models are really good, and the context matters even more."
The current models have reached a high level of effectiveness, and while they will likely continue to improve, the context in which they are used is crucial for optimizing their output.
It is essential to surround these models with the right tools and frameworks to enhance their capabilities.
"Building gradually and making it productive for you first is key."
Incremental development of skills and tools is advised over rushing to adopt the latest flashy technologies.
By taking the time to create customized tools that meet specific needs, individuals can significantly enhance their productivity.
"You want to keep your context window clear to save money and improve performance."
Maintaining a clear context window is important for both cost-effectiveness and the performance of AI models.
As the context window fills up, model performance deteriorates, similar to how human cognition struggles under information overload.
"Focus on what is unique and special about you, your workflow, and your business."
Instead of providing general knowledge, the models should be informed by specific individuals' workflows and preferences.
Skills tailored to unique needs allow for more effective communication with AI agents, leading to improved task execution.
"Skills are what it's all about."
Developing and implementing personalized skills is crucial for maximizing the effectiveness of AI agents in achieving specific tasks.
The investment in creating tailored skills pays off in terms of efficiency and productivity, making them a central component of successful AI applications.
