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Building an agent loop is only the starting point. A useful agent also needs a deliberate way to decide what information the model sees at each step, where that information comes from, and what should be kept, updated, or discarded as work continues. That ongoing design is context engineering.
What is context engineering for AI agents?
Context engineering is the design and maintenance of the information available to a model during inference. It covers more than the prompt: an agent’s usable context can include instructions, the current request, conversation history, tools and their results, retrieved documents, workspace data, and state saved outside the live interaction. Anthropic describes the work as iterative curation of the information that reaches the model (Anthropic’s engineering guide).
A practical way to think about it is to distinguish an agent’s entire information environment from the slice actually provided to the model for a particular step. The model cannot act on data merely because the application has it somewhere; the harness must expose it through input, instructions, a tool, or retrieval.
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- User request and preferences: the immediate goal, constraints, and relevant preferences.
- Conversation or task history: prior decisions, results, and unresolved work.
- Tools and tool results: available functions or APIs and the information they return.
- Retrieved knowledge: relevant documents, records, or code fetched from a corpus or service.
- Application and workspace state: files, selections, errors, or other runtime information the harness chooses to attach.
- Persistent state: information stored externally and retrieved when useful in a later step or session.
These categories overlap in practice, and there is no single universally accepted taxonomy. They are useful because each has different freshness, visibility, and retention needs.
How is context engineering different from prompt engineering?
Prompt engineering usually focuses on the instructions and wording given to a model. Context engineering includes that work but also addresses the changing information environment around an agent: which tools it can use, which information to retrieve, how history is maintained, and how application state becomes model-visible.
The distinction matters when debugging. If an agent ignores a constraint, the cause may be an unclear instruction, but it may also be that the constraint was trimmed, omitted from a summary, not retrieved, or never passed to the model. OpenAI’s Agents SDK documentation distinguishes context available to application code from information available to the model; runtime-local data is not automatically model-visible (OpenAI Agents SDK context management).
Why is building the loop easier than managing its context?
A minimal agent can call a model, handle a tool request, return the tool result, and repeat. That loop describes control flow. It does not decide which of the many possible inputs matter now, whether information has gone stale, or what a future step will need to know.
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As a run grows, the available information pool grows too. The system must select a relevant subset for each inference step while respecting the task, permissions, and practical context limits. A larger context window can accommodate more material, but it does not guarantee that the model will use the right details. Anthropic cautions that recall can decline as token counts rise in needle-in-a-haystack evaluations and discusses context pollution as a risk; these are engineering cautions, not a quantitative rule that applies identically to every model and task (Anthropic’s engineering guide; Anthropic’s context-window documentation).
Microsoft’s VS Code documentation offers a product-specific example of context assembly: system instructions, customizations, the user’s message, history, implicit workspace context, explicit references, and tool output can all contribute. Explicitly referencing a file does not make its contents free of context-window use (VS Code context documentation). The exact assembly varies across products and agent frameworks.
How should you design an agent’s context?
Start from the work the agent must complete, then map the information needed at each step to a source and a retention rule. Microsoft’s learning material similarly recommends defining clear results, identifying needed information, and building context pipelines such as retrieval-augmented generation (RAG), MCP servers, or tools (Microsoft Learn: Context Engineering for AI Agents).
- Define the result. State what the agent should deliver or change when it is done. Include constraints that determine whether the result is acceptable.
- Map required information by step. Identify facts, prior decisions, permissions, and current data needed to plan, act, and verify. Do not assume every step needs the same information.
- Assign each item a source. Put small, stable rules in instructions; take the immediate task from user input; use conversation state for relevant recent work; use tools or retrieval for external and changing data; and use external storage for state that must survive beyond the live context.
- Choose when information enters the model’s context. Supply information consistently when it is small and needed on most runs. Fetch larger or conditional material on demand when the agent reaches a step that needs it. OpenAI documents both direct context and tool or retrieval approaches in its SDK guidance (OpenAI Agents SDK context management).
- Set retention and cleanup rules. Decide what stays verbatim, what becomes a summary, what is discarded, and what is saved externally. Specify how a saved item can be updated or invalidated when it becomes stale.
- Evaluate the workflow. Check whether the agent retrieves relevant, fresh information; retains important constraints; uses context efficiently; and completes the intended task. Test the actual workflow rather than assuming a particular architecture will work because it is common.
Should an agent use trimming, summaries, RAG, or external memory?
These patterns solve different problems and can be combined. The right choice depends on whether information is small and stable, large or changing, needed only occasionally, or expected to persist across sessions.
| Pattern | Useful when | Main tradeoff |
|---|---|---|
| Include information directly in instructions or input | The information is small, stable, and needed on most runs | Repeated or excessive material consumes context even when it is not useful |
| Fetch on demand through tools or retrieval | The information is large, changing, or needed for only some steps | The agent must choose and use the retrieval path well; relevance and freshness matter |
| Trim older conversation turns | Recent work matters most and keeping it verbatim is valuable | Older constraints, decisions, and preferences can disappear abruptly |
| Summarize prior history | Long-range goals and decisions need to persist compactly | Compression can omit or misweight details, and an inaccurate summary can carry forward |
| Store persistent state externally | State must survive sessions or exceed a practical prompt budget | Storage, retrieval, and rules for selecting relevant memories are required |
| Isolate work into focused contexts | Separate subtasks benefit from less competing information | The system must pass the necessary findings and state between contexts |
Trimming versus summarizing
Trimming is straightforward: preserve selected recent turns exactly and remove older material. That makes the retained portion reproducible, but an older requirement can vanish without being represented anywhere else. Summarization compresses prior history so that distant goals and decisions remain available, but the summary may omit details, introduce bias, or compound an earlier mistake. OpenAI’s September 9, 2025 cookbook discusses these short-term memory tradeoffs (OpenAI Cookbook: Short-Term Memory Management with Sessions).
Anthropic also describes compaction and structured note-taking for extended agent work. These approaches can preserve useful state without keeping every earlier message in the live context, but they still require deciding what information merits retention (Anthropic’s engineering guide).
RAG and tools versus persistent memory
RAG or a tool call fetches information for a current need; it does not by itself decide what a system should remember about previous work. Persistent memory is a separate information-architecture decision: store selected state outside the active context, then retrieve relevant items when needed. AWS describes external agent stores, including vector, object, and document stores, as ways to retain state and retrieve relevant memories at runtime (AWS guidance on generative AI agents and memory). An external store is not automatically useful memory: retrieval quality, freshness, permissions, and update rules still matter.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How should you compare context strategies?
Do not judge an approach only by how much it can store. Compare it against the agent’s actual workflow using criteria that expose different failure modes:
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- Retrieval relevance and freshness: Does it fetch information that answers the current question, and is that information current enough?
- State fidelity: Are retained decisions and facts preserved accurately through trimming, summaries, and handoffs?
- Context use: How much material reaches the model, and how much of it is useful for the current step?
- Latency and cost: How do retrieval calls, repeated instructions, summarization, and extra model steps affect the workflow?
- Traceability and debugging: Can the team determine what the model saw and why a fact or constraint was missing?
- Operational fit: Do the storage and retrieval design meet the team’s data access, permission, and maintenance requirements?
These are evaluation dimensions for an implementation, not a published benchmark or a guarantee that one pattern will outperform another.
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