The intuitive way to help a coding agent is to give it more. More context in the prompt, more documents in the window, and the whole project history pasted in before the real request. The more the agent knows, the better it should do.
The catch is that an agent will use everything you give it and weigh details that have little to do with the task at hand. Without a way to judge what’s relevant to your teams, it’s the agent’s first day on the job every time, and the output gets less reliable and more costly as a result.
Why relevance beats volume
An agent given too much context doesn’t ignore the noise. A stale decision from a project that was abandoned, a requirement from a different part of the system, a comment thread that was later reversed — all of it becomes input, and the agent has no reliable way to know which parts still matter. This results in context drift: work that’s confidently built on the wrong basis and token inefficient.
This is why giving an agent access to everything tends to disappoint. The knowledge an agent needs for a given task is usually a small, specific slice: the requirements for this feature, the decisions that still hold, the constraints on this part of the system. The value is in delivering that slice and leaving the rest out, so the agent reasons over what’s relevant instead of everything the team has ever written down.
Ground agents in trusted knowledge
The raw inputs your agents need already exist: the requirements, the acceptance criteria, the decisions, and the reasons the work is shaped the way it is. What most teams lack is a knowledge graph that can make sense of it, distill the relevant slice, and feed only that to the agent. More specifically, it needs to:
Scope context to the work at hand. An agent picking up a piece of work should draw on the requirements, acceptance criteria, and decisions attached to that specific work — not a generic dump of everything. When the context is scoped to the task, the agent builds against the real target instead of reasoning over things that don’t apply.
Bring the history that still holds. Most work connects to work that came before it. An agent that can see the related decisions and the reasons behind them, filtered to what’s still current and relevant, avoids resolving old problems and contradicting choices the team already made. The filtering matters as much as the access.
Write results back so the record stays current. When an agent completes work, what it did and why should flow back into the system of record. That keeps the record an accurate account of the work, so the next task, and the next agent, inherits context that’s current rather than stale.
Make scoping automatic
Relying on developers to handpick the right context for every task doesn’t scale, and it makes the quality of an agent’s work reliant on what someone remembered to include. Connecting the knowledge graph to your agents lets the relevant context flow automatically, scoped to each task. Then an agent starts from what applies without anyone assembling it by hand.
Done this way, the system of record becomes the live source agents draw from, and it earns its keep twice: once when people plan and track work in it, and again when it feeds agents exactly what each task needs.
What this means for leaders
The instinct is to invest in more capable agents, but the higher return (namely, more accurate outputs and fewer tokens spent) is often in the context you feed the agents you already have. An agent working from the relevant slice of your system of record produces work that reflects what your team decided and can be traced back to a real requirement.
See how leading engineering organizations give agents clarity before they write a single line of code.
Read More from This Article: Why more context can make your coding agents worse
Source: News