Page 107
12 articlesThe Caching Hierarchy for Agentic Workloads: Five Layers Most Teams Stop at Two
Production AI agents need five caching layers — prompt, semantic, tool result, plan, and session state — each with distinct TTLs and invalidation strategies. Most teams stop at two and leave half their savings on the table.
Capability Elicitation: Getting Models to Use What They Already Know
Most prompt optimization focuses on instruction clarity, but the real bottleneck is often the model's failure to activate knowledge it already has. A practical guide to elicitation techniques — structured decomposition, analogical priming, expertise framing — that unlock latent LLM capability without fine-tuning.
Capability Elicitation vs. Prompt Engineering: Your Model Already Knows the Answer
Most teams iterate on prompt clarity when the real bottleneck is activating knowledge the model already has. A practical guide to five elicitation techniques — from analogical priming to combinatorial prompting — that unlock latent LLM capabilities without fine-tuning.
The Centralized AI Platform Trap: Why Shared ML Teams Kill Product Velocity
Building a shared ML infrastructure team sounds like the right move. In practice, it becomes the biggest bottleneck to shipping AI features. Here's what goes wrong and what to do instead.
Chaos Engineering for AI Agents: Injecting the Failures Your Agents Will Actually Face
LLM API calls fail 1–5% of the time in production. For multi-step agents making dozens of tool calls per task, untested failure modes become customer-facing bugs. A practical guide to fault injection categories, framework design, and benchmark results for building resilient AI agents.
Consensus Protocols for Multi-Agent Decisions: What Happens When Your Agents Disagree
Majority vote among LLM agents fails nearly 24% of the time on disputed questions. Distributed systems primitives — leader election, quorum voting, and CRDTs — offer battle-tested alternatives for coordinating multi-agent decisions.
The Context Window as IDE: Why AI Coding Agents Succeed or Fail Based on What They Can See
AI coding agents fail not because models lack capability, but because retrieval pipelines load the wrong files. How context utilization, project memory files, and codebase structure determine whether your agent writes correct code or plausible nonsense.
Conway's Law for AI Systems: Your Org Chart Is Already Your Agent Architecture
Why multi-agent AI systems mirror org charts — not architecture diagrams — and the organizational patterns (embedded AI engineers, shared eval infrastructure, prompt review practices) that prevent agent boundaries from inheriting team dysfunction.
Deep 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.
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.
