A key capability is creating skills that continuously search the web, Reddit, and X for the latest techniques on a topic. The agent then incorporates this new knowledge to improve its future outputs and stay current.
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A cutting-edge pattern involves AI agents using a CLI to pull their own runtime failure traces from monitoring tools like Langsmith. The agent can then analyze these traces to diagnose errors and modify its own codebase or instructions to prevent future failures, creating a powerful, human-supervised self-improvement loop.
When everyone can generate content with AI, the basic version becomes table stakes. The new competitive edge comes from creating advanced agent workflows, such as a "critic agent" that constantly evaluates and improves output against specific quality metrics.
Knowledge workers are using AI agents like Claude Code to create multi-layered research. The AI first generates several deep-dive reports on individual topics, then creates a meta-analysis by synthesizing those initial AI-generated reports, enabling a powerful, iterative research cycle managed locally.
The popular conception of AGI as a pre-trained system that knows everything is flawed. A more realistic and powerful goal is an AI with a human-like ability for continual learning. This system wouldn't be deployed as a finished product, but as a 'super-intelligent 15-year-old' that learns and adapts to specific roles.
To improve an agent's performance on a specific task like prompting the VO3 video model, create a dedicated 'onboarding document'. Use a tool like Perplexity to gather best practices from experts, compile them into a doc, and instruct the agent to reference it. This shortcuts the learning curve and embeds expertise.
Daniel Miessler's PAI includes an 'upgrade skill' that allows the system to improve itself. It can ingest new information from engineering blogs or platform changelogs, then recommend and implement upgrades to its own skills and hooks to incorporate new features and knowledge.
Instead of codebases becoming harder to manage over time, use an AI agent to create a "compounding engineering" system. Codify learnings from each feature build—successful plans, bug fixes, tests—back into the agent's prompts and tools, making future development faster and easier.
A truly effective skill isn't created in one shot. The best practice is to treat the first version as a draft, then iteratively refine it through research, self-critique, and testing to make the AI "think like an expert, not just follow steps."
Unlike chatbots that rely solely on their training data, Google's AI acts as a live researcher. For a single user query, the model executes a 'query fanout'—running multiple, targeted background searches to gather, synthesize, and cite fresh information from across the web in real-time.
A major flaw in current AI is that models are frozen after training and don't learn from new interactions. "Nested Learning," a new technique from Google, offers a path for models to continually update, mimicking a key aspect of human intelligence and overcoming this static limitation.
