Context Engineering for Multi-Step Agents
Learn how to manage context across the many steps of an autonomous agent so early mistakes don't compound.
Every agent step is a context-assembly decision
An agent running ten steps makes ten separate decisions about what to include: the original task, prior actions and their results, relevant files or search results, and any error messages. Each step's context should be assembled deliberately for that step's decision, not just appended to with everything from every previous step.
Avoiding unbounded context growth
Left unmanaged, an agent's context grows every step as tool outputs, file contents, and command results accumulate, eventually crowding out the original task or forcing expensive truncation mid-session. Design an explicit pruning or summarization policy — drop full file contents once they're no longer needed, summarize completed sub-tasks — before a long session runs into this wall.
Compounding errors from bad early context
If an agent misreads a file or misunderstands a requirement in an early step, that mistake becomes part of the context for every later step and can compound rather than self-correct, since the agent has no way to know its earlier read was wrong unless something contradicts it. Build in checkpoints — running tests, re-reading the actual requirement — that give the agent a chance to catch and correct an early error rather than build on it.
Sub-agent and hand-off context
When a task is delegated to a specialized sub-agent, decide explicitly what context it needs (not everything the parent agent has seen) and what it should return (a focused result, not its entire working transcript). Passing full context between agents is simple but expensive and can leak irrelevant or sensitive information across a boundary that should be narrow.
Instrumentation: know what the agent saw
For any non-trivial agent, log the exact context sent at each step, not just the final output. When an agent produces a surprising or wrong result, the fastest diagnosis is usually 'what did it actually have in front of it at that step,' which is impossible to answer without that instrumentation in place from the start.
Practical exercise
Run a multi-step agent task and capture the context at each step (most agentic tools or SDKs expose this via logs or verbose mode). Find the step where the agent's behavior first diverges from what you'd expect, and trace whether the cause was missing context, stale context, or context that was present but poorly formatted.
Sources and further reading
These primary or specialist references informed the concepts in this guide. Product details can change, so verify current documentation before implementation.