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12 articlesDeep Research Agents: Why Most Implementations Loop Forever or Stop Too Early
Production deep research agents burn tokens chasing tangents or quit after two queries. Practical convergence strategies, cost controls, credibility defenses, and architecture patterns that make iterative search actually work.
Deterministic Replay: How to Debug AI Agents That Never Run the Same Way Twice
Record every LLM call, tool response, and timestamp during agent execution, then replay the exact sequence to reproduce failures — because setting temperature to zero won't make your multi-step agent deterministic.
Differential Privacy for AI Systems: What 'We Added Noise' Actually Means
The gap between claiming differential privacy and actually bounding what your model memorizes and regurgitates — a practical guide to epsilon budgets, DP-RAG tradeoffs, and when DP training is the wrong tool entirely.
Dynamic Few-Shot Retrieval: Why Your Static Examples Are Costing You Accuracy
Static few-shot examples feel safe, but they silently degrade quality for most requests. A practical engineering breakdown of dynamic retrieval — performance numbers, ordering traps, pool poisoning risks, and when to stick with static.
Your Embedding Pipeline Is Critical Infrastructure — Treat It Like Your Primary Database
Production embedding pipelines fail silently — returning plausible but wrong results without triggering alerts. Learn the CDC-to-embedding architecture, model migration strategies, and monitoring stack that keeps your vector index as reliable as your primary database.
The EU AI Act for Engineers: What the Four Risk Tiers Actually Require From Your Architecture
The EU AI Act's August 2026 deadline demands immutable logging, human override architecture, bias testing pipelines, and explainability layers — seven concrete engineering requirements that reshape how you build and deploy high-risk AI systems.
The Feedback Flywheel Stall: Why Most AI Products Stop Improving After Month Three
Most AI products hit a plateau around month three when the data flywheel quietly stalls. Three failure modes — diminishing data value, user-driven distribution shift, and annotation fatigue — explain why, and targeted interventions can restart the cycle.
GraphRAG in Production: When Vector Search Fails at Multi-Hop Reasoning
Vector search fails when queries require connecting entities across documents. GraphRAG uses knowledge graphs to enable multi-hop reasoning — but the cost, entity resolution challenges, and maintenance burden demand careful architectural trade-offs.
Human Feedback Latency: The 30-Day Gap Killing Your AI Improvement Loop
Explicit feedback rates top out at 1-3%, meaning most teams wait 30+ days before accumulating enough signal to detect quality changes. Here's the behavioral proxy architecture that gives you statistically valid signal on day 1.
Hybrid Search in Production: Why BM25 Still Wins on the Queries That Matter
Pure dense retrieval fails silently on exact identifiers, code, and rare terms. Here's the score fusion architecture, reranking strategy, and diagnostic methodology that production RAG systems actually use.
LLM Content Moderation at Scale: Why It's Not Just Another Classifier
Content moderation at production scale requires a cascade of fast classifiers, LLM judgment, and human escalation — not a single model. Here's the architecture, adversarial failure modes, and the false-positive threshold that drives users away.
LLM Output as API Contract: Versioning Structured Responses for Downstream Consumers
When multiple services depend on LLM-structured output, model upgrades silently break downstream consumers. Here's how schema drift and behavioral drift happen, and the versioning and contract-testing patterns that catch breakage before deployment.
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.
