Jamie Park 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
Search for “AI agent roadmap” and you’ll find dozens of structured learning paths. They all teach the same thing: how to build an agent. Call an LLM, parse the response, invoke a tool, loop. You can learn the basics in a weekend. What none of them teach is the part that actually determines whether your … Read more
You deploy an agent. It passes your manual tests. It handles the demo beautifully. Then a customer triggers an edge case where the agent calls the wrong tool, processes the malformed response without noticing, and confidently delivers a wrong answer. No error. No escalation. Just a silent failure that nobody catches until the customer complains. … Read more
A peer-reviewed study running 9,649 experiments concluded that the quality of context you feed a model matters more than the quality of your prompts. That finding is reshaping how production AI teams work. Prompt engineering optimizes what you ask. Context engineering optimizes what the model sees. And for AI agents running multi-step workflows in production, … Read more
OpenAI’s Codex team shipped a million-line codebase with seven engineers in roughly one-tenth the traditional timeline. They did not write the code. They built the harness: the environments, constraints, feedback loops, and verification systems that enabled AI agents to write reliable code. Their philosophy: “Humans steer. Agents execute.” This is harness engineering, and it is … Read more
A team spends three weeks refining their agent’s prompt. They tune the system message, adjust the temperature, add few-shot examples, and iterate on the instruction phrasing. Their task completion rate moves from 85% to 88%. Then a different team adds a structured verification step after each tool call, a pattern that takes two days to … Read more
You have read about ReAct, Plan-and-Execute, multi-agent orchestration, routing, reflection, and a dozen other agent design patterns. Every framework tutorial presents its pattern as the obvious choice. Every architecture blog makes the selected approach look inevitable. And you still do not know which pattern to use for the system you need to build next week. … Read more