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12 articlesThe Difficulty Concentrator: AI Support Deflection Burns Out the Humans Left Behind
Deflection rates measure difficulty avoided, not difficulty removed. When AI handles the easy 80 percent of support tickets, the human queue becomes 100 percent edge cases — and the team feels it long before the dashboard does.
Browser Agent Session Bleed: When One Profile Serves Many Tenants
Long-running browser agents that reuse profiles to dodge cold-start cost can serve one tenant's session to another's request. The trace says success — and a different user's dashboard just got read.
Credentials Residue: The Agent You Retired Is Still Logged Into Production
Retiring an AI agent by deleting its code leaves OAuth tokens, service accounts, vector indices, and eval datasets rotting in production. The fix starts at launch, not sunset.
The Eval Ceiling: When Your Golden Test Cases Stop Discriminating
Once every candidate model scores 95+ on the same test cases, your eval suite has stopped measuring anything — the ruler outgrew the platform, not the other way around.
Eval Datasets Are Customer Data With a Right Answer Attached
Golden eval sets are real customer queries paired with labeled correct answers — and most teams handle them as engineering fixtures, bypassing every privacy control built for the underlying production data.
Eval Selection Bias: Why Your Test Set Goes Blind to the Failures That Drove Users Away
Eval sets refreshed from production traces inherit a survivor bias: the users who hit the worst failures left and stopped generating traces. Scores climb while retention slips. Here is how to break the loop.
The Fallback That Became the Default: Why Your Tier Mix Needs an SLO
Your fallback path was supposed to fire on 0.5% of requests. It is now serving 38%. The fix is to treat tier mix as a first-class SLO.
The Five Definitions of 'Now' Inside Your LLM Prompt
Five layers of implicit time inside every LLM prompt — and why the layers silently disagree the moment a request is replayed, batched, or evaluated against a pinned snapshot.
Hyrum's Law for Streamed Reasoning: Pacing, Pauses, and Intermediate Tokens Are an Undocumented Contract
When a model swap preserves your structured-output schema but changes token pacing, pause patterns, and intermediate phrasing, you ship a breaking change to a contract you never wrote down.
The Latency Budget Negotiation: How to Tell Product That 'Real-Time' Costs Capability
A structured way to negotiate latency targets with product before they become commitments — the conversion table, the pick-two frame, and why TTFT is usually the number that matters.
The MCP Cold Start Tax: How Tool-Server Overhead Compounds by Agent Step 7
Why a 200ms MCP tool call becomes a 4-second agent loop, where the cold-start tax actually lives, and the warm-pool discipline that turns multi-second penalties into sub-100ms ones.
The Mixed PR Queue: Reviewer Throughput Is Now the Binding Constraint
Coding agents removed the code-writing constraint and dropped the load on the review queue. The team that ships agents without redesigning review will ship a backlog generator.
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.
