Page 136
12 articlesThe Batch LLM Pipeline Blind Spot: Offline Processing and the Queue Design Nobody Talks About
Most LLM API spend goes to batch workloads — nightly classification, data enrichment, embedding generation — yet teams design them like slow chat APIs. A practical guide to queue architecture, checkpoint-resume, failure taxonomy, and per-pipeline cost attribution for offline LLM pipelines.
The Batch LLM Pipeline Blind Spot: Queue Design, Checkpointing, and Cost Attribution for Offline AI
Production LLM batch pipelines fail when built with real-time serving patterns. Job sizing, checkpoint-resume, dead letter queues, cost attribution, and queue backpressure all need rethinking for offline workloads.
Beam Search for Code Agents: Why Greedy Generation Is a Reliability Trap
Greedy single-pass generation caps code agent reliability at 20–30% on hard tasks. Tree exploration strategies — beam search, MCTS, and structured tree search with execution feedback — deliver 30–130% pass rate improvements on the same problems without changing the underlying model.
Cognitive Tool Scaffolding: Near-Reasoning-Model Performance Without the Price Tag
Four structured cognitive operations applied as tool calls can lift a standard 70B model from 13% to 30% on competition-level math benchmarks — nearly matching o1-preview at base-model prices. A practical decision framework for when cognitive scaffolding beats buying a reasoning model.
Cold Cache, Hot Cache: Why Your LLM Latency Numbers Lie in Staging
Prompt caching makes staging latency look 80% better than production reality. A four-phase load testing methodology that accounts for cold cache, traffic diversity, and per-node routing reveals the honest p95 and p99 numbers before your users do.
The Cold Start Problem in AI Personalization
When a new user sends their first message, your AI system has one data point and must make dozens of implicit decisions. Here's the architectural playbook for navigating cold start without building a filter bubble yourself.
The Composition Testing Gap: Why Your Agents Pass Every Test but Fail Together
67% of multi-agent system failures stem from inter-agent interactions, not individual defects. A practical guide to property-based invariants, trajectory replay, seam injection, and contract testing for composed agent pipelines.
Computer Use Agents in Production: When Pixels Replace API Calls
A production guide to computer use agents — covering the see-think-act loop, coordinate scaling pitfalls, five failure modes that kill deployments, sandboxing requirements, and a decision framework for when pixels beat API calls.
Cross-Tenant Data Leakage in Shared LLM Infrastructure: The Isolation Failures Nobody Tests For
How prompt caches, vector indexes, fine-tuned model weights, and agent memory stores can silently bleed data between tenants in shared LLM products — which isolation primitives actually enforce boundaries, and the audit methodology for finding contamination before a customer does.
DAG-First Agent Orchestration: Why Linear Chains Break at Scale
Linear agent pipelines serialize work that should run in parallel, propagate failures that could be isolated, and make partial recovery structurally impossible. Here is what switching to a DAG-first execution model actually changes.
The Debug Tax: Why Debugging AI Systems Takes 10x Longer Than Building Them
Production AI debugging demands 3–8x more engineering time than initial development — driven by non-reproducible failures, semantic errors invisible to traditional monitoring, and prompt regressions that break silently. A practical methodology covering retrieval triage, evaluation hierarchies, statistical pass/fail criteria, and trace-based replay.
Domain-Specialized Agent Architectures: Why Generic Agents Underperform in High-Stakes Verticals
Generic AI agents consistently underperform in medical, legal, and scientific domains. Here are the three architectural patterns — tiered specialist sub-agents, domain-specific tool servers, and curated knowledge injection — that close the gap, plus a decision framework for when specialization overhead is worth it.
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.
