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2 posts tagged with "agentic-engineering"

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The AI Standup Where Yesterday's Status Is a Lie

· 9 min read
Tian Pan
Software Engineer

The team meets at 10am. The first engineer reports what their agents finished overnight. Except the eval suite that kicked off at 7am hasn't returned, the PR the agent opened at 3am is waiting on a review from another agent whose queue depth is unknown, and the long-running refactor agent is on hour eleven of an estimated four-hour run with no signal that it's stuck and no signal that it's healthy. Yesterday's status is not "done" and not "in progress." Yesterday's status is unknowable from inside the room.

The standup was a synchronous ritual built for synchronous human work. Each person did a thing, finished it, slept on it, and reported it the next morning. The unit of work was a workday. The unit of reporting was a person. The cadence matched the substrate. None of that holds anymore. The unit of work is now an agent run that started before you went to bed and may finish during the meeting or three hours after. The unit of reporting is a fleet, not a person. And the cadence — a 9- to 15-minute round-robin at 10am sharp — is a frequency the substrate doesn't produce events on.

The PR-Bot That Never Sleeps: When Your Reviewers Become the Rate Limiter

· 11 min read
Tian Pan
Software Engineer

For two decades the bottleneck in software engineering was writing code. We optimized IDEs, autocompletion, refactoring tools, and frameworks to make typing cheaper. We won. Now the bottleneck moved one step downstream: writing is cheap, and reading is expensive. The PR-bot can spin up ten implementation attempts in parallel and open ten pull requests against your repo before you finish your morning coffee. Your reviewers cannot.

The rate limiter for AI-assisted software delivery is no longer the model's tokens per second. It is the number of human eyes you can put on a diff per day. And when those eyes get overwhelmed, you do not get a graceful degradation — you get rubber stamps. Code merges with LGTM 🚀 on top of code that nobody actually read. A senior engineer approves an AI-written patch that another AI tool already reviewed, and three weeks later a data-inconsistency bug eats forty hours of someone's life. Surface correctness is not systemic correctness, and a green pipeline is not understanding.