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12 articlesParallel Tool Calls in LLM Agents: The Coupling Test You Didn't Know You Were Running
Enabling parallel tool execution in LLM agents exposes hidden coupling in your tool design — the three silent failure modes, how to classify tools for safe parallelism, and when to consolidate instead of parallelize.
Production AI Incident Response: When Your Agent Goes Wrong at 3am
AI incidents don't look like software incidents — no stack traces, no 500 errors, just confident wrong answers and runaway loops. A practical guide to detection, triage, containment, and post-mortems for production LLM systems.
The Prompt Ownership Problem: What Happens When Every Team Treats Prompts as Configuration
A $340K production incident exposed what happens when prompts have no owner, no version history, and no review gate — and the lightweight governance model that prevents it.
Prompt Sprawl: When System Prompts Grow Into Unmaintainable Legacy Code
System prompts that start at 200 tokens and balloon to 4,000 silently degrade LLM performance. How to audit, decompose, and architect modular prompts that stay maintainable — applying DRY, separation of concerns, and version control to prompt management.
Why the Chunking Problem Isn't Solved: How Naive RAG Pipelines Hallucinate on Long Documents
Fixed-size and semantic chunking both fail in predictable ways on production documents. Here's what the research shows about RAG chunking failures, and the evaluation and architecture patterns that close the accuracy gap.
RAG's Dirty Secret: Your Retrieval Succeeds but Your Answers Are Still Wrong
Retrieval success doesn't guarantee correct answers. A third failure mode lurks between retrieval and generation—context sufficiency—where retrieved documents rank correctly but lack the specific information needed. Here's how to detect it and what to do about it.
The RAG Freshness Problem: How Stale Embeddings Silently Wreck Retrieval Quality
Semantic similarity has no temporal dimension — stale embeddings score just as high as fresh ones. The CDC pipelines, decay-weighted scoring, and monitoring stack that keep production RAG systems from silently serving outdated answers.
The Reasoning Model Premium in Agent Loops: When Thinking Pays and When It Doesn't
Reasoning models cost up to 86x more per query than standard models — and inside agent loops, that cost compounds with every iteration. A practical decision framework for when to route to reasoning models and when fast models are the better call.
The Reasoning Trace Privacy Problem: How Chain-of-Thought Leaks Sensitive Data in Production
Reasoning models correctly identify sensitive data 98% of the time yet leak it in their chain-of-thought 33% of the time. Here's why the scratchpad is a distinct attack surface and what production teams need to do about it.
The Reasoning Trace Privacy Problem: What Your CoT Logs Are Leaking
Chain-of-thought traces leak more PII than final model outputs, create a readable attack surface for prompt injection, and turn your observability stack into a GDPR liability. Here's what to do about it.
The Retry Storm Problem in Agentic Systems: Why Naive Retries Burn 200x the Tokens
Naive retry logic in AI agent systems creates exponential token-cost amplification across chained tool calls and multi-agent delegation. A layered defense architecture — circuit breakers, conversation-level budgets, deadline propagation, and honest degradation — prevents a single flaky API from cascading into a full agent meltdown.
The Retry Storm Problem in Agentic Systems: Why Every Failed Tool Call Burns Your Token Budget
Naive retry logic across chained agent tool calls creates exponential cost amplification — a $0.01 task becomes a $2 meltdown. A four-layer defense stack with tool budgets, agent budgets, orchestration backpressure, and error classification prevents cascading failures in production AI agents.
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.
