Methodik
·
3 MIN
Context engineering: why it matters and how to do it well
Context engineering designs everything a model sees, not just the prompt. It is what makes agents reliable.
LOCATION
Worldwide
SERIES
AI Tooling & Dev Harnesses
AUTHOR
Aashwin Shrivastava
PUBLISHED
The reliability of an AI agent depends less on the model and more on what you put in front of it. Context engineering is the practice of designing that whole information set: the system prompt, the memory, the tools, and the data you retrieve, not just the question. In our experience it is the single biggest lever on whether an agent works in production, and it is a different discipline from prompt engineering (Context Engineering 2.0).
01.
Beyond the prompt
Prompt engineering tunes the question. Context engineering designs the environment the model decides in. For a one-off task, a good prompt is enough. For an agent that runs over many steps, reads tools, and carries memory, the prompt is a small part of what the model sees. The rest, what you retrieve, what you remember, what tools you expose and how, is context, and it decides far more of the outcome. This is why extending an agent harness is mostly context work.
02.
The four failures to design against
Most agent failures trace back to one of four context problems:
Context overload. Too much in the window. The model loses the thread, and cost and latency climb. More context is not better context.
Context poisoning. A wrong or stale fact enters the window and the model treats it as truth. One bad retrieved chunk can derail an answer.
Token management. The window is finite. Without a plan for what to keep and what to drop, the important detail gets pushed out by noise.
Stale memory. Long-running agents accumulate context that no longer applies, and act on it.
Naming the failure is half the fix. Each one has a concrete counter-move.
03.
Practices that hold up
A few patterns recur in setups that stay reliable:
Retrieve, do not dump. Pull the few relevant passages with good retrieval rather than pasting everything. This is the whole point of RAG, and it is context engineering applied to data.
Prune as you go. Apply rule-based editing inside the scaffold to keep the window lean: drop what a step no longer needs.
Summarize long history. Replace a long transcript with a faithful summary before it crowds out the task.
Start simple, scale on need. Add memory and tools only when a real failure demands them, not by default.
The instinct to add more is the trap. The discipline is subtraction.
04.
Why this is the real engineering
A bigger model rarely fixes a context problem; it just fails more expensively. The work that moves an agent from demo to dependable is almost entirely context work: what it sees, when, and what it is made to forget. For very long inputs the model architecture matters too, which is where subquadratic approaches come in, but for most teams the gain is in the context, not the parameters.
