No-Code Agentist
A daily digest of practical AI agent workflows for non-developers. We isolate actionable no-code guides from viral hype. Scored against human-defined standards.
Daily Summary
2 curated | 6 evaluatedNo-code agent builders are confronting a fundamental challenge: AI systems lose valuable corrections between sessions and often fail silently, making it difficult to build reliable automation without proper and . Practitioners are discovering that context management, error detection, and iterative refinement matter more than clever prompts, as agents must retain learning across tasks and validate outcomes beyond simple exit codes.
We correct AI constantly. But most of those lessons disappear when the chat ends. A simple improvement loop: turn repeated corrections into reusable skills, test them, and make task N+1 better than task N. https://t.co/OQTX8cQb8S
Microsoft's ThinkingBox ran 12 models through 507 database workflows, 20 times each. Of the runs that failed, 67% looked fine: the agent wrote to the database and no tool threw an error. So I check the record after a job, not the exit code. https://t.co/4LsWLVYJGF