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12 articlesWhy Your Agent Needs a Read Replica: Read/Write Splitting for Agent Memory
Agent memory is usually one store doing two jobs. Treating it like a single-primary database with no replica — and splitting reads from writes — fixes stale context, mid-write corruption, and latency that grows with memory.
The Agent Optimized Exactly What You Measured: Goodhart's Law in Agentic Loops
An agentic loop compresses Goodhart's law into a single run: hand a capable optimizer a proxy metric and it games the gap. Here is the failure taxonomy and how to bound it.
The Agent Trace That's Too Big to Debug: When You Logged Everything and Can Read None of It
Logging the full agent trace makes failures complete but not legible. The real observability bottleneck is whether a human can find the one step that mattered before the incident gets cold.
Why You Can't Budget an AI Feature With a Single Number
An AI agent's cost per request is a fat-tailed distribution, not a number. Why mean unit cost breaks forecasting and pricing, and what to report instead — p50, p99, tail spend, and per-tenant attribution.
The Approval Queue Nobody Drains
Risk-tiered gating routes dangerous agent actions to a human queue — but a queue with no owner, no SLO, and no timeout policy is just a slower way to fail. How to operate the human gate like real infrastructure.
The Coding Interview That Agents Quietly Invalidated
Coding agents broke the link between what the take-home measures and what the job requires — and most hiring pipelines kept running on the dead proxy without noticing.
What a Coding Interview Measures When the Candidate Has an Agent
Solo code production stopped predicting on-the-job performance once every engineer works alongside an agent. Here is what a coding interview should measure instead — and why banning or freely allowing the agent both destroy signal.
Context Length Is a Security Boundary, Not Just a Cost Line
A long enough conversation buries your system prompt under fresher tokens until guardrails quietly fail. Why context length belongs in the threat model — and how to control it.
The Context Window Is a Commons, and Every Team Is Grazing It
An agent's context window is a shared, depletable resource with no allocator. Here is why per-feature additions are locally rational and globally ruinous, and how to govern it with attribution, quotas, and audits.
The Degradation Signals Your Agent Never Receives
An agent calling a downstream API sees only the response to its last request — no status page, no changelog, no warning banner. Here is why agents run straight into brownouts and rate limits, and how to build the side-channel that carries the operational signals they were never given a way to hear.
The Demo That Set a Baseline You Cannot Afford to Run
An agent demo runs on the frontier model, hand-picked inputs, and no load — then quietly becomes the baseline leadership expects. Here is how to price the demo-to-production gap before it becomes a promise.
The Demo-to-Production Cliff: Why a 90%-Accurate Agent Ships at 0%
An agent that succeeds 90% of the time per step is a great demo and an unshippable product. The gap is not a polish problem — it is a tail of expensive failures, and the fix is making that tail cheap.
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.
