Jamie Park An insurance company deployed a claims processing agent that could handle 80% of routine claims without human intervention. The remaining 20% involved ambiguous situations: overlapping policy clauses, disputed damage assessments, claims touching multiple coverage types. The team’s first instinct was to keep improving the agent until it could handle 100%. That was the wrong approach. … Read more
A fintech startup deployed a customer service agent on a Friday afternoon. By Monday morning, it had confidently told 47 customers they were eligible for loan products they didn’t qualify for. The agent passed every functional test during development. What it didn’t pass was a systematic verification of safety constraints, output validation, and failure handling … Read more
Hiring managers reviewing harness engineering candidates see dozens of chatbot demos every week. A chatbot that calls an API and returns a response proves you can follow a tutorial. It doesn’t prove you can build systems that work reliably in production. What separates hired candidates from rejected ones isn’t the sophistication of their agent logic. … Read more
The ReAct pattern is the most widely used reasoning pattern for AI agents. It stands for Reasoning and Acting. Instead of generating an answer in a single pass, the agent thinks about what to do (Reason), takes an action (Act), observes the result (Observe), then repeats until it has enough information to answer. This three-step … Read more
Static context is the beginner approach to agent design. You write a system prompt, maybe add a few retrieved documents, and send everything to the model on every turn. It works for simple agents. It fails for complex ones. The problem with static context is that it doesn’t adapt. A customer support agent needs different … Read more
The difference between a mediocre agent and a great one is usually context, not the model. Feed the same model poor context and it hallucinates. Feed it well-engineered context and it produces reliable, grounded outputs. Context engineering is the skill of curating what the model sees to maximize output quality while minimizing cost. This tutorial … Read more
Eighteen months ago, the job title “harness engineer” didn’t exist. Today, hundreds of companies are hiring for it, even if they call it different things: AI reliability engineer, agent infrastructure engineer, LLM operations engineer, or simply “senior AI engineer with production agent experience.” The role exists because every company deploying AI agents in production has … Read more
The job title “harness engineer” appeared in fewer than 50 LinkedIn postings a year ago. Today it shows up in hundreds. Companies deploying AI agents in production have learned that building the agent is the easy part. Making it reliable is the hard part. Harness engineers are the people who solve the hard part. But … Read more
The job title “harness engineer” appeared in fewer than 50 LinkedIn postings a year ago. Today it shows up in hundreds. Companies deploying AI agents in production have learned that building the agent is the easy part. Making it reliable is the hard part. Harness engineers are the people who solve the hard part. But … Read more
Your agent works in the demo. It handles the five examples you tested during development. Then it hits production and a user asks something slightly different, and the whole thing falls apart. The gap between “works in demos” and “works in production” is almost always a dataset problem. Not a model problem. Not an architecture … Read more