Page 134
12 articlesThe Observability Tax: When Monitoring Your AI Costs More Than Running It
AI workloads generate 10–50x more telemetry than traditional services, pushing monitoring bills past inference costs. A practical guide to tiered sampling, retention policies, and tool consolidation that cuts observability spend by 50–90% without losing signal.
The Planning Tax: Why Your Agent Spends More Tokens Thinking Than Doing
LLM agents burn 40-70% of their token budget on planning before executing a single tool call. A breakdown of where reasoning tokens go, why more thinking doesn't always mean better outcomes, and the architectural patterns — ReWOO, plan caching, hierarchical decomposition — that reclaim your budget.
The Second System Effect in AI: Why Your Agent v2 Rewrite Will Probably Fail
Fred Brooks warned about the second system effect in 1975 — and it's now the leading cause of failed AI agent rewrites. 68% of multi-agent deployments would have performed equally well as single-agent systems, yet teams keep reaching for architectural complexity they don't need.
The Trust Calibration Curve: How Users Learn to (Mis)Trust AI
The over-trust → failure → over-correction lifecycle that kills AI product adoption. Why single high-salience errors collapse trust disproportionately, and the design patterns that build durable, calibrated user trust.
Vision Inputs in Production AI Pipelines: The Preprocessing Decisions Nobody Documents
How image resolution, compression artifacts, OCR preprocessing, and aspect-ratio handling silently degrade vision model accuracy in production — and the normalization pipeline that separates model failures from input failures.
The Warranty Problem: Who Pays When Your AI Feature Is Wrong?
Software warranties assumed deterministic behavior — AI features break that assumption. A practical guide to the liability, insurance, and contract gaps engineering teams face when shipping non-deterministic systems.
When Your Agents Disagree: Consensus and Arbitration in Multi-Agent Systems
How to resolve conflicting outputs from peer AI agents when there's no ground truth — covering majority voting, confidence weighting, judge models, and when to surface disagreement to users rather than hide it.
Write-Ahead Logging for AI Agents: Borrowing Database Recovery Patterns for Crash-Safe Execution
Database WAL patterns map directly to AI agent workflows — an execution journal that logs intent before action and outcome before advancing enables skip-replay recovery, exactly-once side effects, and deterministic resumption after mid-workflow crashes.
Capability Probing: How to Map Your Model's Limitations Before Users Do
Map your LLM's failure boundaries before deployment using probe suites, capability matrices, canary prompts, and a probe-to-regression pipeline that catches silent regressions across model upgrades.
CZ's 'Freedom of Money': from a Jiangsu Boy to a Crypto Empire - Chapter-by-Chapter Summary
A comprehensive chapter-by-chapter breakdown of Changpeng Zhao's autobiography, 'Freedom of Money.' From a rural Jiangsu village to a Canadian immigrant, from a Wall Street coder to founding the world's largest crypto exchange, and his journey through a guilty plea, prison, and newfound freedom—all 25 chapters detailed.
The Hidden Token Tax: How Overhead Silently Drains Your LLM Context Window
System prompts, tool schemas, and chat history silently consume 30-60% of your LLM context window before user content arrives — here's how to audit and cut the overhead.
The Infinity Machine: How Demis Hassabis Built DeepMind and Chased AGI
From chess prodigy to Nobel Prize co-winner, Demis Hassabis built DeepMind into the world's most ambitious AI research lab. Sebastian Mallaby's biography traces the scientific breakthroughs, corporate battles, and existential dilemmas behind the quest for artificial general intelligence.
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.
