Most of us are stuck on the prompt treadmill - manually tweaking instructions that break every time the task shifts. This post lays out an architecture where the AI agent grades its own work, rewrites its own prompts, builds its own tools, and rolls back when things get worse. Every idea is backed by published research. No jargon, just the blueprint.
A practical deep-dive into the algorithms powering modern AI agents - from Chain-of-Thought to automated workflow discovery. Each algorithm is explained with flow diagrams, simple examples, and Python pseudocode
How the rise of AI-assisted development is redefining what it means to be a good developer.
You're copying the same agents into every new workflow. There's a better way. A self-organizing architecture with RAG-based discovery, reputation scores, budget-aware planning, and dynamic composition that solves problems nobody anticipated.
You're copying the same agents into every new workflow. There's a better way. A self-organizing architecture with RAG-based discovery, reputation scores, budget-aware planning, and dynamic composition that solves problems nobody anticipated.