In the past year, agent architectures have gone from niche experiments to front-page product strategies. From coding copilots and data analysts to browser navigators and virtual assistants, everyone seems to be building agents. But while most of the public attention centers on planning strategies, tool execution, or multimodal extensions, one area remains dramatically under-discussed: context engineering.

Context engineering — the art and science of shaping what the model sees — has become the keystone for reliable, performant, and scalable agents. It's not glamorous. It doesn't involve state-of-the-art benchmarks or flashy demos. But if your agent is slow, forgetful, costly, or hallucinatory, odds are the root cause lives in the context window.

This post outlines what context engineering really is, why it matters, and how it's evolving as agents move from prototypes to production.

Why Context Is the Real Runtime

Most agents today rely on in-context learning. The model sees a long prompt — a system message, a few-shot task, perhaps a tool schema — and must generate the next action based on that input. This stands in stark contrast to traditional fine-tuned models, which internalize behavior during training.

With agents, there is no hard-coded policy network or learned memory. Instead, behavior is emergent from context. The model reasons about what to do next based on what it has seen. That means context is not just a hint; it's the operating system.

This design choice brings speed and flexibility, but also introduces brittleness. Slight variations in context — like tool order, inconsistent serialization, or timestamp drift — can derail performance. Context becomes a dynamic and fragile artifact, shaped by engineering choices rather than model weights.

Cache-Aware Contexts Are Fast Contexts

At scale, latency and cost dominate. This is where the Key-Value (KV) cache becomes crucial. LLMs, especially transformer-based ones, prefill the context and cache intermediate representations (keys and values) for reuse in future decoding steps. If your context remains unchanged between steps, you can reuse prior computation — dramatically speeding up response time and reducing cost.

But the cache is picky. Even a single-token change can invalidate it. We've seen agents that naively include high-entropy values — timestamps, random UUIDs, session hashes — that nuke cache effectiveness without realizing it. A few key principles help:

• Use deterministic serialization.
• Avoid including volatile values unless necessary.
• Maintain stable system prompts and tool schemas.

In some systems, enabling cache-awareness yields a 5x to 10x cost reduction and reduces latency to sub-second levels. But it's not just a performance hack. The more stable your context, the more reliable your agent becomes.

Don't Truncate, Externalize

The naïve way to deal with long contexts is truncation. Drop old observations, trim verbose responses, compress history. But this loses information — and worse, loses it unpredictably. Agents need to reason over long-horizon dependencies. That random stack trace from 20 steps ago might be the missing clue for recovery.

The more robust approach is externalization. Instead of forcing everything into the context window, treat the agent's memory as structured storage. Log events to a file. Index documents in a vector store. Record partial plans in a scratchpad.

Some of the best-performing agents today don't rely on a single context at all — they simulate memory via file systems, notebooks, or explicit planning artifacts (like todo lists or call trees). This kind of externalization preserves retrieval power without bloating token counts. It also aligns well with emerging memory architectures that separate short-term and long-term context.

Shape Model Attention Through Language

One of the most elegant techniques we've seen is what some teams call "recitation." Rather than trying to tune attention weights or redesign memory architectures, agents simply rewrite key goals or summaries into the end of the context.

It's deceptively simple. Updating a todo.md file with the next step. Repeating the task objective. Logging an explicit goal reminder.

This keeps the agent anchored. It avoids mid-task drift, helps with "lost in the middle" issues, and reinforces global coherence. And it works because models attend more strongly to recent tokens. By placing the summary at the end, you nudge attention forward — without touching the model weights.

Don't Clean Up the Mess

It's tempting to hide failure. When the agent makes a mistake — bad tool call, invalid input, runtime error — the impulse is to clean the trace. Retry the call. Replace the faulty output. Present a clean slate.

But erasing failure erases signal. One of the clearest signs of agentic behavior is recovery: seeing a problem, adapting behavior, changing course. If the model can't see what went wrong, it can't improve.

Some of the best error-handling agents lean into this. They preserve the full trace, including mistakes. They show the stack trace. They note what failed and why. Over time, this builds an internal prior in the model: "I tried this, it didn't work, so maybe try something else."

This isn't just a logging best practice. It's part of the learning loop. And in production, it's often the fastest way to squash edge cases.

Fight Contextual Homogeneity

Few-shot prompting is powerful — but in agents, it can backfire. If the model sees the same pattern over and over, it starts to overfit. That works for static completions, but not dynamic tasks.

Imagine reviewing 20 resumes. If the first five decisions look similar, the model may blindly repeat them — even if the sixth case is different. This is the mimicry trap: LLMs are excellent imitators. Repetition becomes bias.

The fix is surprisingly low-tech: add controlled variation. Use different phrasing. Mix ordering. Vary serialization format slightly. Break uniformity just enough to keep the model awake.

The Agent Is in the Context

The biggest myth in agentic systems is that the agent lives in the code. It doesn't. The orchestration framework, the tool registry, the loop logic — all of that is scaffolding. The real agent is the behavior that emerges from the context passed to the model.

This is why context engineering matters so deeply. It is the interface between human intent and model behavior. It governs latency, cost, robustness, coherence, and adaptability.

You can't debug an agent by looking only at the code. You have to read the context. Study it like a compiler log. Understand what the model saw, and how it was shaped.

As LLMs evolve — longer context windows, improved memory architectures, better function calling — the importance of context will only grow. But the principle remains: shape the input, shape the behavior.

Context engineering isn't just a hack. It's a discipline. The best agent teams treat it like one.