Section
All published posts.
The generic agent looks simple until it becomes a giant context window with too many tools, too many permissions, and too much cost. Domain-specific agents trade that accumulation for composition, clear boundaries, and cheaper execution.
Your agent nailed the demo and everyone loved it. But how do you know it actually works? If the answer is 'we tested it and it seemed fine', you are operating in vibes mode. And vibes don't scale.
Autonomous agents can turn a trivial task into a financial black hole because token consumption is massive, stochastic, and difficult to predict.
The pace of software development has undergone unprecedented compression. What was once simply programming is now trad coding: line-by-line work shaped by human problem-solving time.
In Go, context.Context is the standard mechanism for propagating cancellation, deadlines, and values across Goroutines and function calls. But passing context forward has a subtle trap.
You have probably experienced this: one moment, GPT or Claude solves a complex coding problem in seconds; the next, the same AI forgets basic context or invents nonexistent information.
If you follow the GitHub Copilot ecosystem, you have probably heard of *.agent.md files. They are great for simple things, basically a boosted prompt that runs inside Copilot
The paradox of modern speed We live in the paradox of technical abundance: in the age of Artificial Intelligence, we generate code in minutes, but organizations still struggle to convert that volume into real value.
There is an efficiency gulf between biological architecture and silicon. While a 12-year-old child already masters human language, models like GPT-3 require far more data.
Engineering leaders and developers are constantly searching for more productivity. The pressure to deliver faster is relentless, but the path to that speed is rarely clear.