Agentic
Loops
Design patterns, system architecture, and production-grade implementations for software engineers building autonomous AI systems.
Our Beliefs
Building AI agents is engineering, not magic.
AI agents are changing how we build software, and the full impact is still hard to measure. But one thing is clear — this technology is not going away.
We should adapt instead of saying “it’s not ready yet.” If we wait until everything is stable, we may lose time and fall behind engineers who already learned how to use it well.
The good news is that building agents is still engineering. LLMs can help a lot, but they can also be wrong and hard to trust. To make an agent useful, we need clear contracts, error handling, logging/tracing, tests, and security.
This community is for engineers who want to learn by building. We are learning and sharing experiments, reference projects, and practical patterns that make agents more reliable and easier to maintain. We focus on simple, repeatable methods - not magic prompts or blind trust. If you want to understand how agents work and run them safely in real systems, you're in the right place.
{
"belief": "Engineering > hype",
"focus": [
"Production readiness",
"Predictable behavior",
"Traceability and auditability",
"Reusable patterns",
"Failure handling and recovery"
],
"anti_patterns": [
"Black-box decisions",
"Prompt-only reliability",
"Hidden state and surprises",
"Autonomy without control",
"Systems you can't debug"
]
}Who We Are
Featured Repos
We focus on internals, so engineers understand how agents work.
What We Cover
A comprehensive curriculum for building production-ready AI agents
Stay in the Loop
Get deep-dive engineering posts on AI agents - no hype, just engineering insights.
Join engineers learning to build production AI agents