Section
All published posts.
When an agent is allowed to act too broadly, the risk is not only leakage or abuse. It also becomes hard to operate: it works outside scope, repeats actions, burns budget, and fails without a clean way back.
Code volume is growing faster than human review capacity. The problem is not using AI, it is pretending review still costs the same.
The agent is no longer the whole product. The next jump is the layer above it: memory across sessions, coordination across agents, context across repos, and automatic optimization of the harness itself.
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