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12 articlesThe OOO Auto-Reply Your Agent Did Not Read
Most agent stacks page humans by name, not by role — so when somebody takes PTO, the agent loops with the auto-reply until on-call notices.
The Tool-Call Authorization Layer Nobody Wrote
Your gateway checks who. Your endpoint checks what. Nothing in your agent stack checks whether the model was allowed to make the call in the first place.
The Tool You Added For One Agent Is Now In Every Agent's Hand
A shared tool registry quietly hands every agent every capability. Per-agent allow-lists, not the catalog, are the unit of authorization that actually constrains what an agent can do.
What You Deleted Is Invisible to Your Coding Agent
Coding agents reintroduce code you deleted yesterday because absence leaves no trace in the repo. A field guide to recording the negative decisions agents need to respect.
The Nightly Batch Job That Quietly Became a Latency-Critical Service
A nightly batch job becomes a latency-critical service one reasonable request at a time. Why batch and online inference optimize for opposite goals, how the drift produces quiet failures, and how to re-architect on purpose.
The Feature Store Your Agent Reinvented Badly
AI agents re-derive the same facts every turn — churn risk, account age, plan tier — with no caching, no shared definitions, and no point-in-time correctness. Why that makes them a broken feature pipeline, and how to fix it.
Provider Rate Limits Are a Capacity Plan You Never Wrote
When your app hits a 429, the retry code that runs next quietly becomes your capacity policy. Treat rate-limit handling as deliberate load shedding — with priority tiers, jitter, and a scheduler — instead of a library default nobody reviewed.
Your Happy Path Is Your Expensive Path: The Agent That Costs More When It Wins
A failed agent run is cheap; a successful one can cost 50x more. Why raising your agent's success rate compresses margin, and the levers that fix it.
Your Agent Endpoint Is a Distributed System Pretending to Be a Function Call
An `await agent.run()` looks like a local function but hides a remote, partially-failing distributed system. Here is the timeout, retry, idempotency, and circuit-breaker discipline agent code needs.
Your Agent Has No Concept of Business Hours
AI agents act the instant they decide, with no sense of whether 3 a.m. is the wrong time to send it. How to separate anytime work from daylight work and build a timing layer that knows when to wait.
The Backfill Problem: Why Agent Memory Needs Migrations Like a Database
Agent memory is a production database that drifts every time you improve its format. Version your records, write real migrations, and backfill before old memories quietly rot.
The Bug You Can't Reproduce Because the Model Picked a Different Token
Replaying an LLM bug and watching it pass doesn't mean the bug is gone — it means you drew a different sample. How to debug a sampler when your tools assume determinism.
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.
