Start simple, then add memory and loops as your agent problem becomes clear
bendee983 · x · 2026-07-27
A practical agent-building take: start simple, then add complexity only when the problem shape is clearer.
- Begin with the most accessible levers first: prompts, context, and documents.
- Add memory, skills, and loops gradually as edge cases become obvious.
- Replace fragile prompt logic with deterministic constraints and rules where repeatability matters.
- As you scale, use model routing to cut cost and latency.
- The core message is to avoid overengineering early agent setups and to let the system structure emerge from the problem.
Related event: Developer Shares 100 Practical Tips for Building Personal AI Agents(3 posts)→
More from coding & agent
- theo builds his own visualizer for today's agent models, showing how cheap Luna really is — ivan_bezdomny · 2026-09-23
- Vite+ Hits RC: One Rust-Powered CLI to Replace Your Entire Web Toolchain — cnakazawa · 2026-09-23
- Tesla's in-car Grok agent books trips across Gmail, Calendar and Notion in one command — xiaohu · 2026-09-23
- Tesla's In-Car Grok Assistant Now Executes Cross-App Tasks in One Sentence — xiaohu · 2026-09-23
- Garry Tan says Capy lets him ship PRs much faster than Codex or Claude Code — garrytan · 2026-09-23
- DeskPilot: open-source native Python desktop client for local LLMs with MCP and sandboxed tools — poofph · 2026-09-23