Personal experiences using feedback-driven LLMs to write programs.
Designing agentic loops
Coding agents like Anthropic’s Claude Code and OpenAI’s Codex CLI represent a genuine step change in how useful LLMs can be for producing working code. These agents can now directly …
AI Agents: Key Concepts and How They Overcome LLM Limitations
An AI agent is an autonomous software entity that is often used to augment a large language model. Here's what developers need to know.
Agent Recipes
Explore common agent recipes with ready to copy code to improve your LLM applications.
If an AI agent can’t figure out how your API works, neither can your users
Discover how LLM‑powered agents act as tireless API testers—exposing friction in docs, errors, and design—and learn six essential strategies (consistency, comprehensive docs, rich errors, example‑driven tutorials, simplicity, and feedback loops) to elevate your API’s developer experience for both humans and machines alike.
Build agents, not pipelines
There are only two ways to use LLMs in a computer program: as part of a pipeline, or as an agent. In other words, either you express the control flow of the program in code, or you give a LLM tools and allow it to manage the control flow itself1.
LLM Agents are simply Graph — Tutorial For Dummies
Ever wondered how AI agents actually work behind the scenes?
LLM Agents are simply Graph — Tutorial For Dummies
Ever wondered how AI agents actually work behind the scenes?
truefoundry/trueforge
The open-source agent harness - the runtime layer that turns an LLM into a working agent.
What I learned building an opinionated and minimal coding agent
Lessons I learned while building my own coding agent from scratch.
The Agent Harness
A specification for agent behavior that LLM frameworks leave undefined: error handling, context management, tool execution, and state transitions.
Multi-agentic Software Development is a Distributed Systems Problem (AGI can't save you from it)
Recently, I've been thinking a lot about scaffolding and languages for managing systems of LLMs coordinating with each other — new programming languages might be the ideal solution for this area.
What I've learned about writing AI apps so far | Seldo.com
I scaled back my ambitions from "how to write AI apps" to what I've actually learned. Key lessons: LLMs excel at condensing text, not creating it. Give them the data they need rather than relying on training. Let them self-correct. Use regular code wherever possible. And stop trying to replace humans, especially doctors and lawyers.
Stevens: a hackable AI assistant using a single SQLite table and a handful of cron jobs
There’s a lot of hype these days around patterns for building with AI. Agents, memory, RAG, assistants—so many buzzwords! But the reality is, you don’t need fancy techniques or libraries to build useful personal tools with LLMs.
Agent experience: How to design products that agents can actually use — WorkOS
What engineers and founders need to know about designing APIs, tools, and interfaces for agent-driven workflows
The AI Operating System: Stateful Agents with Letta | Cameron Pfiffer, AI By the Bay25