Mastering Context Engineering: 5 Proven Strategies to Boost AI Agent Performance

01 What Is Context Engineering?

Recently, "context engineering" has become a buzzword among agent developers. The term was introduced by Andrej Karpathy and resonated with many because it pinpoints a core pain point in agent development: while the workflow appears simple, the massive context generated by numerous tool calls and long‑horizon reasoning becomes a huge bottleneck for performance, cost, and even model capability.

Context engineering is the methodology of providing the right information to an agent at the right time. It goes beyond prompt engineering and Retrieval‑Augmented Generation (RAG) and is a decisive factor for agent success. If we liken an LLM to a CPU, the context window is the RAM; the signal‑to‑noise ratio of the context directly determines product effectiveness because the context comes not only from human instructions but also from tool calls and the agent's own reasoning chain, making compression of the memory space to the most critical information essential.

Why Do We Need Context Engineering?

Many believe 2025 will be the "agent year," yet developers find that while building agents seems straightforward, efficiently running the whole system is extremely challenging, with context management being the key bottleneck.

In June, Karpathy tweeted the definition of context engineering: “filling an LLM’s context window with just the right information for the next step,” which quickly sparked widespread interest.

02 Method One: Offload

Offload context means the agent does not have to pass the full context back to the model after every tool call. Instead, the information can be moved to external storage such as a file system. The model receives only a summary or a URL as a pointer and retrieves the full data only when needed, dramatically reducing token consumption and improving efficiency.

Key points from Lance Martin’s talk at Latent Space:

Record notes in a file system.

Use a file (e.g., todo.md) to plan and track progress.

Reading/writing files consumes a lot of tokens.

Store long‑term memory in files.

By summarizing or providing a URL instead of the entire context, the model can focus on the most relevant information, leading to lower latency and cost.

03 Method Two: Reduce

Reduce context involves summarizing or pruning the accumulated information. Typical actions include summarizing the agent’s message history, trimming irrelevant parts, summarizing tool outputs, and compressing information when handing off between agents. Care must be taken to avoid irreversible loss of critical data.

Manus notes that aggressive compression can cause the agent to forget earlier goals, especially in long‑context or complex tasks. Therefore, reductions should be reversible whenever possible, e.g., keeping URLs so the original content can be fetched later.

04 Method Three: Retrieve & Memory

Retrieve context means pulling relevant information from external resources—knowledge bases, previous dialogues, documents, tool outputs—and injecting it into the model’s context to improve accuracy. Retrieval has long existed (e.g., RAG) but is now a core component of context engineering.

Examples include combining vector search with traditional grep, using markdown files that list document URLs and descriptions, and letting the agent decide which documents to fetch based on the task.

Memory can be categorized into episodic, semantic, procedural, and background memory. Effective agents need to write and read memories appropriately, often treating large‑scale memory reads as a form of retrieval.

05 Method Four: Isolate

Isolate context means splitting the context among multiple agents or components to avoid interference. By partitioning responsibilities, each sub‑agent handles a specific type of information, reducing the overall burden on any single agent.

Care must be taken because multi‑agent systems can produce conflicting decisions. Isolating sub‑agents that do not participate directly in decision‑making can mitigate this risk.

06 Method Five: Cache

Caching stores repeated tokens (e.g., agent instructions, tool descriptions) in a prefix so that subsequent calls can reuse the cached content, dramatically reducing token cost. For example, Claude‑sonnet’s input token cost drops from $3 / M tokens to $0.30 / M tokens when caching is applied.

While caching improves latency and cost, it does not solve the fundamental problem of long context windows; once the context exceeds the model’s limit, performance decay still occurs.

The Bitter Lesson Inspiration

Hyung Won Chung of OpenAI highlighted in his talk that computing power grows roughly ten‑fold every five years, and algorithms with less inductive bias—those that rely on massive data and compute—tend to outperform hand‑crafted solutions. This mirrors the "Bitter Lesson" that letting machines learn from data is more effective than manually encoding knowledge.

He also noted that adding structure (inductive bias) can help when compute is limited, but as compute scales, such hand‑crafted structures become bottlenecks.

Practical Experience

Lance Martin’s Open Deep Research initially used a highly structured pipeline with few tool calls. As model capabilities improved, the system shifted to a more flexible agent‑centric architecture, allowing the model to decide its own research path and leverage tool calls.

Anthropic’s Claude Code follows the same principle: keep the system simple and general, providing broad model access rather than embedding heavy‑weight logic.

In enterprise settings, AI is often embedded into existing workflows, whereas AI‑native products rebuild processes from scratch to fully exploit advanced model capabilities.

Key takeaways:

Transparent, composable frameworks add real value.

Reducing unnecessary structure enables agents to adapt quickly as models improve.

Caching, offloading, and smart retrieval are essential for cost‑effective, high‑performing agents.