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12 articlesThe Agentic Debugger's Trap: When Your Agent Patches Faster Than You Can Diagnose
Agent-generated patches close bugs faster than engineers can diagnose them. The cost is a codebase whose failure modes only the agent understands.
The AI Bill of Materials: What Your Dependency Tree Looks Like When Procurement Asks
Most AI teams answer 'show us your dependency tree' with a Slack thread. AIBOM turns that into a query — a continuously generated inventory of models, prompts, tools, and datasets that satisfies regulators and procurement before they ask.
The AI Bystander Effect: Why Five-Team Launches Ship Eval Suites Nobody Watches
AI features fail the way bystanders fail to call 911 — not because nobody noticed, but because everyone assumed someone else owned the call. Why a single named DRI for output quality is the only fix that scales.
Your AI Feature Needs a Kill Switch That Isn't a Deploy
A standard deploy-rollback takes thirty minutes; a misbehaving LLM ships bad outputs to customers in seconds. Here is the off-switch primitive your AI feature needs before its first incident — the four-flag family, the detection signals that fire it, and the test discipline that keeps it real.
Your AI Feature Has No DRI: Why It's Drifting Without a Quarterly Goal Owner
AI features straddle product, engineering, research, and FinOps — and end up owned by none of them. Here's the org pattern that stops them from drifting between quarterly reviews.
Your AI Feature Is Only As Reliable As The ETL Pipeline Nobody Owns
Most AI quality regressions are upstream data problems wearing an AI costume. Data contracts, lineage, and paired on-call turn the invisible ETL seam into a first-class artifact.
AI Feature Soak Windows: Why a Two-Week Canary Misses What Actually Matters
Two-week canaries catch crashes; AI features fail by trends. A practical case for soak windows, slow-failure metrics, and a rollback path that stays warm long enough to matter.
AI Procurement Clauses Your Lawyers Haven't Learned to Ask For Yet
Standard SaaS templates miss the AI-specific clauses — training data exclusions, model pinning, output indemnity, audit rights — that decide whether your vendor relationship survives the next model swap.
The Autonomy Toggle: When Agent Mode Should Be a User Setting, Not a Model Setting
Hardcoding a single autonomy level into your agent product alienates half the user base. Ship a per-task autonomy ladder, scaled undo affordances, and learned defaults instead.
Bug Bashes for AI Features: Sampling a Distribution, Not Hunting Defects
Why the inherited bug-bash ritual misfires on stochastic AI features, and how to redesign it as a sampling pass that produces evals, not anecdotes.
The Closed-Loop Escalation Bug: When Your Specialist Agents Route in Circles
Two specialist agents passing the same conversation back and forth can quietly burn fifty thousand dollars in inference before anyone notices. Treat handoffs as a routing protocol, not a domain abstraction.
Your Coding Agent Is a Junior Engineer Who Never Reads the Tests
Coding-agent productivity lives in the scaffolding around the model — the same scaffolding teams already needed for junior engineers. Here's what to write down, and why the agent finally forces you to.
About Tian Pan
I'm Tian Pan, an engineer-founder focused on agentic engineering — building autonomous AI systems and scaling engineering teams. I write practical guides on system design, technical leadership, and shipping with AI agents. Previously an early engineer at Uber, Brex, and IoTeX.
