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12 articlesPre-Deployment Autonomy Red Lines: The Safety Exercise Teams Skip Until an Incident Forces the Conversation
A concrete framework for defining what AI agents are never permitted to do before production—and why encoding those limits in system prompts is insufficient.
Prompt Contract Testing: How Teams Building Different Agents Coordinate Without Breaking Each Other
Multi-agent AI systems fail at rates of 41–87% in production, and over a third of those failures are coordination breakdowns between agents. Prompt contract testing—adapting consumer-driven contracts to LLM prompts—is how teams ship without breaking each other.
Prompt Credit Assignment: Finding the Dead Weight in Your System Prompt
A practical engineering guide to identifying which instructions in your system prompt actually drive model behavior — and which are burning tokens for nothing.
The Prompt Engineering Career Trap: Which AI Skills Compound and Which Decay
Most prompt engineering skills have a half-life. As models improve, few-shot examples and CoT templates erode in value — while evaluation design, behavioral specification, and system architecture compound. Here's how to tell which side of the line your skills are on.
When RAG Makes Your AI Worse: The Creativity-Grounding Tradeoff
Retrieval augmentation improves factual accuracy but systematically degrades creative and generative tasks. Here's how to detect the problem and apply selective grounding strategies.
The Read-Only Ratchet: Why Your Production Agent Shouldn't Start with Full Permissions
Most teams grant AI agents full permissions upfront, then scramble to restrict them after incidents. The safer pattern starts read-only and escalates trust incrementally — proven by UNIX, OAuth, and a growing list of production failures.
Reranking Is the Real Work: Why Your Retrieval System's Bottleneck Is Never the Index
Most teams over-invest in vector index tuning and under-invest in the reranking layer. The ranking step — not the index — determines whether your RAG system delivers or hallucinates.
The Shadow AI Problem: Why Engineers Bypass Your Official AI Platform and What to Do About It
Nearly half of engineers use AI tools their employers haven't sanctioned. Blocking endpoints makes the problem worse. Here's why shadow AI is a platform design failure — and how to fix it.
The Stakeholder Explanation Layer: Building AI Transparency That Regulators and Executives Actually Accept
Most AI systems can explain themselves to engineers. Almost none can explain themselves to regulators, executives, or legal teams. Here's the architectural layer that bridges that gap — and why it's fundamentally an observability problem, not an interpretability one.
The System Prompt Is a Software Interface, Not a Config String
Most teams treat system prompts like config strings — unversioned, untested, and one bad edit away from silent failure. Applying software interface design principles to prompts is what makes LLM systems maintainable at scale.
Thinking Budgets: When Extended Reasoning Models Actually Make Economic Sense
Extended reasoning models can inflate inference costs 5–30x — or deliver genuine quality jumps on hard tasks. The difference comes down to routing: which queries actually warrant thinking tokens, how to set budget ceilings, and how to catch over-thinking before it hits your invoice.
Timeout-Aware Agent Design: How to Deliver Partial Results Instead of Silent Failure
Most AI agents fail completely when they hit a deadline. Here's how to design agents that surface the best available result instead of returning nothing.
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.
