Page 141
12 articlesText-to-SQL in Production: Why Correct SQL Is the Easy Part
LLMs score 86% on SQL benchmarks and 10% on your actual warehouse. The queries that fail don't error—they return wrong data. A taxonomy of silent failure modes and the layered architecture that catches them.
The Three Attack Surfaces in Multi-Agent Communication
82% of frontier LLMs comply with malicious commands from peer agents even when refusing them from users. Here are the three distinct attack surfaces — prompt injection, agent spoofing, and memory poisoning — and the protocol-level defenses each requires.
Why Your Thumbs-Down Data Is Lying to You: Selection Bias in Production AI Feedback Loops
Only 1–3% of users click rating buttons — and they are systematically different from everyone else. How selection bias distorts RLHF training data, amplifies preference collapse, and hides 80% of your quality problems, plus the five implicit behavioral signals that capture ground truth from every user.
The Token Economics of Chain-of-Thought: When Thinking Out Loud Costs More Than It's Worth
Chain-of-thought prompting inflates token costs 2–5x and adds seconds of latency — yet delivers no measurable accuracy gain for most production tasks. A decision framework for when CoT pays off, when it hurts, and cheaper alternatives like Chain-of-Draft that close the gap at a fraction of the cost.
The Tool Result Validation Gap: Why AI Agents Blindly Trust Every API Response
AI agent frameworks validate tool inputs but never check what comes back. Here are the three categories of tool result failures and a tiered validation architecture that catches schema violations, stale data, and semantically wrong results before they reach users.
The Unit Economics of AI Agents: When Does Autonomous Work Actually Save Money
Your API bill is 10–20% of the true cost of running AI agents in production. A breakdown of the hidden cost stack, the full cost-per-task formula, volume thresholds for positive ROI, and the metrics that actually predict whether autonomous work saves money.
When the Generalist Beats the Specialists: The Case for Unified Single-Agent Architectures
For most production AI tasks, a single capable agent with rich tool access outperforms multi-agent pipelines — and the research explains why coordination overhead, error amplification, and capability saturation make specialization a liability at scale.
Agentic Engineering: Build Your Own Software Pokémon Army
One person replaced a 15-person engineering team with autonomous AI agents. Here are the hard-won principles, spectacular failures, and practical setup behind running an AI-native software company.
The Principal Hierarchy Problem: Authorization in Multi-Agent Systems
When Agent A spawns Agent B, whose permissions apply? A deep dive into how trust propagates through delegation chains, why the confused deputy attack is devastating at agent scale, and the authorization patterns that prevent privilege escalation in production multi-agent deployments.
Agent Authorization in Production: Why Your AI Agent Shouldn't Be a Service Account
Giving AI agents service account credentials is the fastest path to discovering which of your systems they can reach when something goes wrong — how ambient authority, over-permissioning, and impersonation tokens create production incidents, and the four patterns that properly scope agent authority.
The Agent Planning Module: A Hidden Architectural Seam
Separating task decomposition from execution in LLM agents is the architectural decision most teams skip — until their agents start failing on anything beyond five steps.
Agent-to-Agent Communication Protocols: The Interface Contracts That Make Multi-Agent Systems Debuggable
How poorly designed inter-agent message contracts cause silent failures in production multi-agent systems — and the schema patterns, error signals, and versioning strategies that prevent them.
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.
