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12 articlesThe 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.
Agentic Coding in Production: What SWE-bench Scores Don't Tell You
SWE-bench Verified hit 80%—yet the same models score 23% on harder benchmarks, and a controlled study found AI tools made experienced developers 19% slower. Here's where agentic coding agents actually deliver value and where they silently fail.
CI/CD for LLM Applications: Why Deploying a Prompt Is Nothing Like Deploying Code
Deploying a new prompt version silently breaks production in ways no dashboard catches. Here's how to build a proper CI/CD pipeline for LLM applications — from prompt versioning and shadow testing to canary rollouts and behavioral drift detection.
The Context Stuffing Antipattern: Why More Context Makes LLMs Worse
Dumping full documents, raw tool outputs, and long chat histories into the LLM context window is a reliability trap. Here's how to detect when context is hurting your system — and the budget-aware curation patterns that fix it.
Continuous Batching: The Single Biggest GPU Utilization Unlock for LLM Serving
How iteration-level scheduling replaces static batching to deliver 4–8x GPU throughput gains in production LLM serving—and the failure modes that appear at high concurrency.
Your Database Schema Is Your Agent's Mental Model
Poorly normalized schemas cause AI agents to hallucinate joins, misread relationships, and chain unnecessary tool calls. Here's how to design a schema layer that your agent can actually reason about.
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.
