The IDE-agent products that have stuck for me through a year of daily use share one thing: a strong plan-then-execute workflow. The tools without it produce flashier demos and worse outcomes. Worth being explicit about why this is the actual product.
Six months in on Operator, plus Mariner, plus Computer Use. The browser-agent category is real and the durable use cases are smaller than the demos suggested. Worth being concrete about what the category does and doesn't do well.
Tekton, Argo CD, GitHub Actions, Jenkins X, four answers to model deploys. You can't unit-test a model, so eval suites become the test substitute. Versioning, rollback, blue-green serving. Pipeline config as the Decisions as Code surface, projected per environment.
Halfway through the year. The frontier moved, the workhorse tier got crowded, the price floor dropped. The honest leaderboard isn't a single ranking, it's a routing decision per workload, and the menu in July looks meaningfully different from the menu in May.
Two years ago, every other LinkedIn post promised six-figure salaries for prompt engineers. Today the title's mostly gone from job boards. The work didn't go away. The framing did, and it's worth being clear about why.
An M4 Max with 64 GB of unified memory comfortably runs open-weights models that needed a serious GPU rig a year ago. The price-per-capability curve crossed something quietly, and the consequence for who can run frontier-adjacent inference at home is bigger than it looks.
Twelve months of running agentic coding tools daily, and the failure mode that keeps repeating isn't the dramatic one. It's the quiet one, the agent doing the thing it was asked, exactly, to a result nobody wanted.
GPT-5 dropped this week. The benchmark deltas are real, the marketing layer is loud, and the question for shops with already-working AI stacks is which routing decisions actually need to change. Honest answer: fewer than the launch suggests.
Eleven seconds, very thorough, immaculate audit trail. A walk through what one Tuesday afternoon in my homelab teaches you about working with agentic coding tools, and the workflow changes that follow.
AWS shipped its own agent SDK two weeks ago, joining LangGraph, Semantic Kernel, the OpenAI Agents SDK, the Anthropic Agents SDK, and a half-dozen others. The question isn't which one's best. It's whether the framework layer is still where the differentiation happens at all.
Agents that run for ten minutes mostly work. Agents that run for ten hours mostly don't. The middle is where the actual production work lives, and the patterns we have for it are weaker than the conversation pretends.
Two years into agents-everywhere, a small set of design patterns has separated from the noise. Worth being explicit about which shapes are actually paying back in production and which are still mostly aspirational.
