The spec as source of truth, not a side document.
Spec-Driven Development is one of those ideas that software engineers have reached for before and then set aside when the effort stopped paying.
Specs as source of truth, or slow ceremony?
Spec-Driven Development entered 2026 as the serious developer’s answer to vibe coding drift.
Keep intent close to the code.
Decision records are the missing memory layer in AI-assisted software development. They capture not just what was built, but why — and that distinction becomes critical when AI tools are writing your code.
Handle errors at the right boundary.
Go error handling is easy to complain about. Every Go developer has written this code hundreds of times:
Stop sleeping in concurrent Go tests.
Testing concurrent Go code has always required a bit of discipline. Goroutines are cheap, channels are simple, and context cancellation is idiomatic — background workers and timers are everywhere in real Go services.
A2A is not dead. It is just not universal.
Google’s Agent2Agent protocol, usually shortened to A2A, had a strange first year.
Reliable polling patterns for AI agents.
Polling agents are one of the least glamorous parts of AI assistant architecture, but they are also one of the most useful.
MCP gives agents tools. A2A gives agents peers.
AI agent architecture is starting to split into two layers.
A2A turns agents into network peers.
The A2A Protocol, short for Agent2Agent Protocol, is an open standard for communication between independent AI agent systems.
Build CQRS in Go without needless ceremony
CQRS is one of those patterns that gets oversold, overcomplicated, and occasionally misdiagnosed as a cure for plain old CRUD boredom.
Control the risk, not just the model.
LLMs are unpredictable. They hallucinate, leak data, generate harmful content, or refuse legitimate requests. Guardrails constrain model behavior without sacrificing capability.
Pick the simplest pattern that works.
Single-model systems are simple. Multi-model systems are powerful. The challenge isn’t choosing models — it’s designing the architecture that orchestrates them.
Working, structured, and retrieval memory for assistants.
Memory turns assistants from reactive to persistent, but it is also where many systems quietly rot. Surveys argue the short-term versus long-term split is no longer enough for modern agent memory; OpenAI and LangGraph SDKs point to a simpler stack — working memory, durable state, and retrieval.
How serious assistants are actually built.
A production AI assistant is not “an LLM with a prompt”. It is a system that accepts intent, keeps state, decides when to retrieve or act, and exposes enough runtime detail to debug failures.
Search is not knowledge structure
Most modern knowledge systems optimize retrieval, and that is understandable. Search is visible, easy to demo, and feels magical when it works. Type a question, get an answer.
Compiled knowledge for AI systems
The premise is simple: compiled knowledge is more reusable than retrieved fragments. RAG became the default answer to a straightforward question - how do I give an LLM access to external knowledge?
