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12 articlesRBAC Is Not Enough for AI Agents: A Practical Authorization Model
Static role-based access control breaks when agents shift permissions mid-task. Here is how to build an authorization model that actually holds: narrow tool scopes, short-lived credentials, ABAC runtime policies, and audit trails anchored to agent identity.
Reasoning Model Economics: When Chain-of-Thought Earns Its Cost
Extended thinking models cost 10–50x more per query. Here's the task taxonomy that tells you when that premium pays off — and the routing architecture that applies it automatically.
The Reranker Gap: Why Most RAG Pipelines Skip the Most Important Layer
Most RAG pipelines stop at vector similarity search and wonder why accuracy plateaus. The reranker is the missing layer — here's what it costs to skip it and how to decide when the tradeoff is worth it.
Sequential Tool Call Waterfalls: The Hidden Latency Tax in Agent Loops
Agent frameworks default to sequential tool execution even when calls are logically independent, creating latency cascades identical to the N+1 query problem. Here's how to identify and fix them.
Shadow to Autopilot: A Readiness Framework for AI Feature Autonomy
Moving AI from shadow mode through advisory, co-pilot, and autopilot stages requires explicit quality gates and monitoring, not just organizational courage. Here's the engineering framework.
The Share-Nothing Agent: Designing AI Agents for Horizontal Scalability
Most AI agents can't scale horizontally because they accumulate implicit state that ties them to a single machine. Here's the architectural discipline that fixes it.
The Six-Month Cliff: Why Production AI Systems Degrade Without a Single Code Change
Your AI feature shipped green and performed well at launch. Six months later it's quietly 20–40% worse — and your dashboards never flagged it. Here's why this happens and how to stop it.
What 99.9% Uptime Means When Your Model Is Occasionally Wrong
Traditional SLAs are meaningless for AI features where success is probabilistic. Here's the contract language and internal SLO design that lets engineering teams ship AI without open-ended liability.
Structured Output Reliability in Production: Why JSON Mode Is Not a Contract
JSON mode guarantees valid syntax — not correct answers. A breakdown of the three failure modes that kill production AI pipelines and the three-layer validation architecture that actually catches them.
Subgroup Fairness Testing in Production AI: Why Aggregate Accuracy Lies
Aggregate accuracy hides systematic failures for specific demographic and linguistic subgroups. The subgroup eval methodology, disparity SLOs, and production monitoring patterns that catch bias before it reaches users at scale.
The Sycophancy Trap: Why AI Validation Tools Agree When They Should Push Back
RLHF-trained models have a systematic agreement bias that makes them dangerous for code review, fact-checking, and decision support. How to measure it and restore appropriate pushback.
Synthetic Eval Bootstrapping: How to Build Ground-Truth Datasets When You Have No Labeled Data
How to build a working LLM evaluation pipeline from zero labeled data using synthetic test generation, human-validated anchors, cross-model disagreement, and behavioral invariants — plus the failure modes that synthetic evals share with the models they test.
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.
