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What context does an AI agent actually see?
For context engineering, “context” means everything made visible to the model at a particular step: instructions, the current user request, prior conversation, retrieved text, tool descriptions, and outputs from earlier tool calls. The model reasons from this material; information stored elsewhere in an application is not automatically visible.
That distinction matters because “context” can also mean application-side state. In the OpenAI Agents SDK, local context is data and services available to application code or tools, while the LLM-visible context is the conversation history sent to the model. A callback or tool may be able to access a customer record without the model seeing it. If the model needs a value to make a decision, the application must include it in a message or provide a tool that can retrieve it.
Context engineering therefore involves more than writing a better prompt. It is the design of the instructions, inputs, tools, external data, memory, and history available across an agent’s turns. Salesforce’s official Agentforce guide defines it as “the art and science of giving your AI agent the right information, tools, and instructions to achieve its goals.”
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Start with the task, then add only the information needed to complete it safely and correctly. This keeps context selection tied to the desired outcome rather than to whatever material happens to be available.
- Define the outcome and boundaries. Say what the agent should produce or do, what constraints apply, and what it must not do. For example: “Summarize the attached incident report for the on-call engineer. Include the affected service, customer impact, current mitigation, and unresolved questions. Do not infer a root cause that the report does not establish.”
- Separate enduring rules from case details. Keep instructions that should apply on every run—such as output format or safety boundaries—in the instruction layer. Pass the user’s current request, relevant facts, and task-specific constraints with that request. Instructions that are always present consume tokens every time, so avoid putting occasional details there.
- Attach known, relevant references explicitly. If the agent needs a particular file, record, code symbol, or document, identify that item instead of supplying an entire repository or corpus “just in case.” All material included in the context takes up space, and unrelated sources can distract from the task.
- Retrieve conditional or changing information when needed. Use a function tool, search, or retrieval system for information that is large, current, or relevant only to some requests. Filter returned material for relevance and length before adding it to the model-visible context.
- Keep only useful tools available. Tool descriptions are part of the context too. Expose the tools that fit the current intent rather than every tool in the application, and avoid returning verbose tool output when a concise result will do.
- Preserve continuity for work that spans turns. Bound the conversation history with summaries or compaction. Save durable progress outside the active context in structured notes, then load those notes when work resumes.
- Measure the result. Record prompt size and test context changes on representative tasks. Compare answer quality and failure rate as well as token use, latency, and cost.
Anthropic’s Applied AI team captures the selection principle this way: “The guiding principle remains the same: find the smallest set of high-signal tokens that maximize the likelihood of your desired outcome.” The key word is “complete”: removing information that the task depends on is not optimization.
Which context strategy fits which information?
Choose where information lives according to how often it is needed, how quickly it changes, and what it costs to retrieve and maintain. The trade-offs below are architectural patterns, not a universally best design.
| Method | Best suited to | Main trade-off |
|---|---|---|
| Stable instructions | Rules and behavior that matter on every run | Repeated token cost; stale instructions affect every request. |
| Task input or explicit references | Known request details and files needed for this task | Must be selected for each task; all supplied material consumes context. |
| Tools and retrieval | Large, changing, or conditionally needed information | Adds retrieval or tool work; irrelevant results need filtering. |
| Summary or compaction | Long conversations approaching context limits | Compression can lose detail if it is too aggressive. |
| Structured notes or memory | Durable decisions, progress, dependencies, and open work | Requires a policy for what to save and when to refresh it. |
| Subagents | Focused research or analysis whose intermediate exploration can be isolated | Coordination and synthesis add overhead; use when complexity justifies it. |
For a short task, clear instructions and a few relevant inputs may be enough. Dynamic domain facts often fit retrieval or tools better than a large static prompt. Long-running work benefits from bounded history and durable notes. Subagents can isolate intermediate work on complex research or analysis, but they are not a requirement for every agent.
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Raw history grows with each turn, and not every old message remains important. Summarize or compact when the history starts to crowd out information needed for the next step. A useful summary retains the state that changes what the agent should do next, rather than attempting to preserve every exchange.
- Decisions: what was decided and any rationale that matters to future choices.
- Dependencies: facts, files, approvals, or external actions the next step relies on.
- Progress: what has been completed and what remains.
- Open issues: unanswered questions, risks, and unresolved choices.
Keep durable notes outside the active conversation if they must survive context resets; reload the relevant notes when the agent resumes. Summaries are lossy, so review them on demanding tasks for omitted constraints, subtle decisions, or unresolved items. Anthropic describes a particular subagent workflow in which a condensed summary may “often [be] 1,000-2,000 tokens”; that is an example from its workflow, not a universal summary size or benchmark.
How should you filter retrieval and tool output?
Retrieval can improve access to large or changing information, but retrieved text is not automatically useful context. AWS warns that unfiltered top-K passages can let low-relevance material displace stronger evidence. Filter for the current question, prefer authoritative and current sources when appropriate, and trim irrelevant material before passing results to the model.
Apply the same discipline to tools. A tool call can add a description, arguments, and a result to the model-visible conversation. Too many irrelevant tool schemas or unnecessarily large raw outputs consume space without helping the task. Select tools based on the agent’s current intent, and shape results so the model receives the facts it needs rather than an indiscriminate data dump.
How can you tell whether context is helping?
Do not optimize for a smaller prompt in isolation. A shorter prompt that omits a key constraint can lower quality or increase failure rates; a larger one may be justified if it improves outcomes enough to warrant its latency and cost. Track token use by component where possible, and compare prompt versions on representative tasks using the same success criteria.
Assess context with practical questions drawn from Google Research’s CAFE(S) vocabulary: is it clear, actionable, faithful to the source, efficient, and secure? Google presents these as dimensions for describing context quality, not as an empirically validated scoring system. Use them to guide review, not to claim a numeric quality score.
- Clarity: can the agent distinguish the task, constraints, and relevant evidence?
- Actionability: does it have the information and tools needed for the next step?
- Fidelity: are facts current and accurately represented, without unsupported inferences?
- Efficiency: does each instruction, source, tool, and history segment earn its place?
- Security: is the supplied content appropriate to expose, and are conflicting or malicious instructions handled safely?
Also check for stale knowledge, conflicting instructions, excessive tools, and irrelevant retrieved passages. AWS recommends budgeting and monitoring context use, versioning prompt changes, and evaluating outcomes. There is no universal token budget, ideal retrieval count, or percentage-full threshold established by the cited guidance; measure against the model, task, and workload you actually use.
What evidence supports these practices?
The implementation guidance comes from product documentation and vendor engineering guidance, which are useful for patterns but reflect their authors’ products and ecosystems. A broader 2025 survey by Lingrui Mei and coauthors describes reviewing over 1,400 research papers on context engineering; that figure is the authors’ stated scope, not a count of papers proving one performance result. Test architecture choices against your own tasks rather than treating any one vendor’s recommendation as universal.
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