Context Engineering for AI Agents: What to Include, Exclude, and Persist
A practical guide to context engineering for AI agents: instructions, tools, retrieval, memory, compaction, security boundaries, and…
Pan Yang6 min read
Pan Yang
1 article by Pan Yang.
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Questions Explored in Pan Yang’s Articles
Explore questions covered in the articles listed here. Each answer links to its original explanation and publication context.
What is context engineering for AI agents?
It is the design of the instructions, tools, retrieved evidence, task state, environment data, and memory available when an agent makes a decision.
Source: Context Engineering for AI Agents: What to Include, Exclude, and Persist
How is context engineering different from prompt engineering?
Prompt engineering focuses on how a request is expressed. Context engineering also decides which evidence and tools are available, how permissions apply, what persists, and how state is compacted across a multi-step run.
Source: Context Engineering for AI Agents: What to Include, Exclude, and Persist
Does a larger context window always improve an agent?
No. Stale plans, similar records, overlapping tools, and untrusted instructions can crowd out relevant evidence. The goal is decision-relevant precision, not maximum token volume.
Source: Context Engineering for AI Agents: What to Include, Exclude, and Persist
What belongs in an agent's context?
Include stable policy, current task state, clear tool contracts, minimal authoritative evidence, temporary working memory, and only the durable memories whose provenance and lifecycle are defined.
Source: Context Engineering for AI Agents: What to Include, Exclude, and Persist
How should an agent handle retrieved documents?
Treat retrieved text as data, preserve source and timestamp, filter by identity and permissions, rank authoritative records first, expose conflicts, and prevent document instructions from overriding system policy.
Source: Context Engineering for AI Agents: What to Include, Exclude, and Persist
What is context compaction?
It is the replacement of long histories with smaller structured state. Good compaction preserves decisions, commitments, owners, deadlines, evidence links, attempted actions, and unresolved conflicts.
Source: Context Engineering for AI Agents: What to Include, Exclude, and Persist