Beyond Prompt Engineering: Context Engineering for AI Agents

Most people start with prompt engineering. You write a system prompt, you tune it, you iterate until you get good outputs. It's satisfying because it's tight feedback—you change something, you see if it works, you adjust. It feels like coding. But once you move into chat loops and agent systems, especially when you're injecting context and orchestrating multiple agents, everything changes. You're not just tuning a prompt anymore. You're designing an entire information system.

The first thing you realize is that you can't just pack more capabilities into a single agent and expect it to work better. The opposite happens. The more responsibilities you give an agent, the worse it performs at each one. Models start calling the wrong tools, making confused decisions. So you have to be intentional about reducing scope. Each agent should have very specific, distinct responsibilities. Their tools should be singular and focused. This is where architecture decisions matter—whether you use hierarchical group chats, aggregated group chats, different selection strategies. Those patterns exist because the problem space demands it.

But then there's the context layer. It's not enough to just decide what an agent does. You have to decide what an agent sees. When multiple agents are interacting in a conversation, controlling which agent sees which parts of the context fundamentally changes how they behave. You might have agent A see the full history, agent B only see recent messages, agent C only see specific tool outputs. You build reducers for each agent. This visibility architecture is just as important as the prompt itself.

And then there's the volume and placement of context. Models have attention patterns—they're good at the beginning and end of context but struggle in the middle. Lost in the middle is real. So where you put information matters. How much you include matters. These are all variables that ripple through your entire system. Change one thing and it affects everything else. It's not like prompt engineering where you can isolate variables. It's systems thinking.

That's the jump from prompt engineering to context engineering. You're not writing better prompts. You're architecting information systems for agents. Tool specificity, agent scope, visibility controls, context volume and placement—these all compound into whether your agents actually work reliably across multiple turns or fall apart.