Posts tagged ai.
Seeing what agents do is a necessary first layer. The product is the layer that tells an organization whether that activity is correct, effective, and improving.
The scarce resource in an agent-enabled engineering day is synchronous, high-bandwidth thinking. Everything downstream of that conversation is execution that can be delegated live or overnight.
Someone asked me recently: where do you get so much work to kick off? I did not understand the question in the moment, and it kept bothering me the next day. Engineering has to be developing the work. That does not mean making up work. There are tiers of work engineering should already own.
Agents run autonomously for hours only where verification is deterministic. That constraint, more than model intelligence, defines what agentic work actually functions today. It also explains a split that keeps repeating, where "agent ships your code" compounds and "agent builds your business plan" disappoints.
The hard part of putting an AI agent into a team's chat is not the chat. It is encoding a complex workflow into the agent and then proving the agent does that workflow correctly.
Every team is about to learn the three categories of AI agents. That's table stakes. The moat is deciding which events get which category.
Managing AI coding agents, and agents that direct other agents, is structurally the same job as being an engineering manager, a director, a VP, or a CEO. You give high-level direction to autonomous workers, and you operate through systems and outcomes instead of inspecting every artifact.
Horizontal code agents are running on VC-subsidized tokens. Vertical agents are paying sticker price. If you're building one of the latter, you have a problem to solve, and "we have a better prompt" isn't it.
Companies picking an AI plan almost always frame it as a budget question. Which tier gives us the most usage for the money? It's the wrong question.
Every agent company hires from the same labor pool. You and your competitor employ literally the same workers: the same frontier models, refreshed quarterly by the same vendors. Raw capability is identical by construction.
We still assign a human reviewer to every pull request. The human opens the diff, scrolls, approves. That ritual is already dead. Most teams are just propping up the corpse.
The most valuable thing I do as an engineer right now isn't writing code. It isn't even reviewing code. It's building the harness that lets the agent verify its own work before it asks me to look at it.
The thing nobody talks about with AI-assisted development isn't the models. It's the cycle time. The agent's iteration loop got longer, the right way to work changed, and most people are still working as if the loop is two seconds long.
My laptop is a four-year-old Dell XPS 15 with 16 gigs of RAM. Fine for normal work. Not fine for running Windows, WSL, a real codebase, a Claude session, and a browser at the same time.
I haven't written a Linear ticket by hand in six months. I don’t write the majority of my Claude prompts. The two stopped being separate things. The ticket is the prompt.
AI did not make the whole software delivery system faster.
I've been rebuilding one of my Claude Code workflows because the old version was too linear.
Your engineering team adopted AI coding tools six months ago. Deployment frequency is up. Lead time is down. PRs are flying through the pipeline. Everyone feels faster.
You're four steps into an AI agent workflow. Steps one through three went perfectly. Step four goes sideways. So you start correcting.
Three AI agents. Three different problem contexts. Each time, the solution emerged with the same architecture.
AI is forcing software development back to first principles. The practices most teams abandoned as overhead, specs, formal verification, architectural review gates, are becoming essential again the moment humans stop reading every line of code.
The distance between what you meant and what the AI produced, and why no refactoring pass can reverse it.
AI-generated tests pass. That's the problem.
The software development lifecycle isn't dead. It just lost its center of gravity.
Not every repetitive task is worth automating. Some tasks feel tedious but resist automation because they require subtle judgment at every step. Others feel complex but are actually just long sequences of mechanical steps.
I've written before about personalized software as the hidden iceberg of the AI era. Software that was never economical to build, but that people genuinely want. I keep coming back to this idea because I keep building more of it.
People are building incredible things with AI coding tools. Custom applications, full-stack prototypes, automation pipelines. But there's a quieter, equally powerful use case: using Claude Code as an operational partner.