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
A 200K token context window sounds like infinite space until you’re paying for it. Every token you send costs money, adds latency, and competes for the model’s attention. Most agent systems waste 40-60% of their context window on information that doesn’t contribute to the current task. Context window optimization isn’t about fitting more information in. … Read more
The most common mistake in multi-agent system design is building a multi-agent system in the first place. Most agent tasks don’t need multiple agents. A single agent with well-chosen tools handles the majority of use cases more simply, more cheaply, and with fewer failure modes. But there are tasks where a single agent genuinely isn’t … Read more
An agent without memory is an agent that starts fresh every time. It can’t learn from past interactions, can’t build on previous work, and can’t avoid mistakes it has already made. Each conversation, each task, each decision happens in isolation. Memory transforms agents from stateless functions into systems that accumulate knowledge, learn from experience, and … Read more