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Give an AI agent the smallest complete set of high-signal information it needs for the current step. Make the goal and constraints explicit, keep always-relevant instructions separate from task details, and fetch large or changing information only when it is needed. For long-running work, preserve decisions and open questions in concise, structured notes. Then test the setup on representative tasks: fewer tokens are useful only if the agent still gets the job right.
What “context” means for an AI agent
Context is the information visible to the model at a particular step: instructions, the current request, conversation history, retrieved material, tool descriptions, and outputs from earlier tool calls. Context engineering is therefore broader than prompt wording; it also includes which tools and external data an agent can use, and what information is carried between turns.
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Do not confuse model-visible context with application-side state. Your code may hold variables, records, or callback data that the model cannot see unless your application puts relevant information into the conversation or makes it available through a tool. The OpenAI Agents SDK’s context management guide distinguishes local application context from the LLM-visible conversation history. When an agent appears to “forget” a value, first check whether that value was ever exposed to the model.
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Assemble context around the current task
1. State the goal and boundaries
Tell the agent what outcome to produce and what constraints govern the work. Specify the relevant format, scope, permissions, or exclusions when they affect success. A broad instruction such as “review this” gives the agent less direction than a concrete outcome such as “identify the three most consequential regressions in this diff; do not modify files.” Clear objectives give every other context decision a purpose. Both Salesforce’s Agentforce guide and Microsoft’s guide to context in AI agents emphasize goals and constraints.
2. Separate standing rules from request-specific details
Put stable behavior that matters on every run in the agent’s instructions. Supply the current task, case facts, and one-off constraints with the request. This separation makes it easier to update a particular case without accidentally changing the agent’s general behavior—and easier to notice when a standing instruction has become stale.
3. Include known, relevant sources explicitly
If the agent must work from a particular file, record, symbol, or document, identify it. Do not attach a whole repository or corpus “just in case”: every supplied passage uses context-window space, and unrelated material can distract from the evidence the task actually requires. Microsoft’s context guidance recommends using relevant references rather than flooding an agent with large or unrelated sources.
4. Retrieve conditional or changing information when needed
Use retrieval or callable tools for information that is large, changes frequently, or is relevant only to some requests. A tool makes that information available without placing all of it in every initial prompt. But retrieval is not a free accuracy upgrade: filter results for relevance and length before adding them to the model’s context. AWS warns that unfiltered top-K passages can let weak matches displace better evidence.
5. Keep tools focused
Expose tools that fit the current task rather than making every available tool schema part of every run. Tool descriptions themselves consume context, and a broad tool menu can make it harder to select the useful action. Context engineering includes deciding what the agent can call—not just what text it receives.
Choose a context strategy for the information
Different information belongs in different places. The right choice depends on how often it is needed, how quickly it changes, and the costs of loading or maintaining it.
| Method | Best use | Main trade-off |
|---|---|---|
| Stable instructions | Rules and behavior that matter on nearly every run | Repeated token cost; stale guidance affects every request |
| Task input or explicit references | Known request details and specific files or records | Must be selected for each task; all included material consumes context |
| Tools and retrieval | Large, changing, or conditionally needed information | Adds tool or retrieval work; irrelevant results need filtering |
| Summary or compaction | Long conversations approaching context limits | Compression can discard details that matter later |
| Structured notes or memory | Durable decisions, progress, dependencies, and unresolved work | Requires a policy for what to save and when to refresh it |
| Subagents | Focused research or analysis whose intermediate work can be isolated | Coordination and synthesis overhead; not necessary for every task |
For a short task, a clear instruction and a few relevant inputs may be enough. For dynamic facts, retrieval or tools may fit better. For work that spans many turns, summaries and durable notes help maintain continuity. Complex analysis may benefit from subagents when isolating intermediate exploration is worth the extra coordination; it is not a default requirement.
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Preserve continuity without replaying the whole conversation
As history grows, repeatedly sending every prior turn can spend context on details the current step no longer needs. Bound the history with a summary or compaction, and keep durable progress outside the live conversation in a form the agent can reload.
A useful handoff note records the current objective, decisions already made, dependencies, important evidence or references, unresolved questions, and the next action. Keep facts that could change separate from durable decisions so they can be refreshed rather than repeated as if still current. Before relying on a compressed summary for demanding work, check that it preserved subtle constraints and open issues. Anthropic cautions that aggressive compaction can lose important detail; its context-engineering guidance also describes subagent workflows that may return a condensed summary “often 1,000-2,000 tokens.” That figure describes an example workflow, not a universal target or benchmark.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Measure context quality, not just prompt size
There is no evidence-backed universal token budget or ideal percentage of a context window to fill. A suitable budget depends on the model, task, available tools, and the cost of a mistake. Track token use by component—such as instructions, history, retrieval, and tool descriptions—so you can see what is consuming space and what can be changed.
Evaluate prompt or retrieval changes on representative tasks. Compare correctness and failure rate alongside token use, latency, and cost. A smaller prompt that omits a critical constraint is not an improvement; a larger one that adds no useful signal is not either. AWS’s Agentic AI Lens guidance recommends managing context use, filtering retrieval, and evaluating prompt changes rather than assuming that more context is better.
For a quality review, Google Research’s CAFE(S) framework offers five useful questions: is the context clear, actionable, faithful to its sources, efficient, and secure? Treat those as discussion prompts, not a scorecard: the CAFE(S) paper page describes a vocabulary for context quality, not a measurement system or universally validated predictor.
- Clarity: Can the agent distinguish the goal, constraints, and evidence?
- Actionability: Does it have the information and tools needed for the next step?
- Fidelity: Is the supplied information accurate and current, without summaries changing its meaning?
- Efficiency: Does each included passage or tool help with this task?
- Security: Could sensitive or untrusted content mislead the agent or be exposed improperly?
Common context failures and how to correct them
- The agent misses information stored in your application. Make the relevant value part of the model-visible input or provide a tool that can return it.
- A large prompt buries the useful evidence. Remove unrelated sources, narrow retrieval, or fetch details only when the task calls for them.
- Retrieved text is present but misleading. Filter passages for relevance and inspect whether the selected material actually supports the requested decision.
- Tools crowd the prompt or confuse tool selection. Offer only tools relevant to the current intent, with descriptions that make their purpose clear.
- A summary drops a crucial constraint. Preserve decisions and unresolved work explicitly, then review the handoff on tasks where a lost detail would matter.
- Instructions conflict or knowledge is stale. Check the source and freshness of each standing rule and retrieved fact; resolve contradictions instead of adding more text around them.
The academic survey by Lingrui Mei and coauthors, “A Survey of Context Engineering for Large Language Models,” published July 17, 2025, describes a field spanning more than 1,400 reviewed research papers. That is the authors’ stated scope of the literature, not a count of papers proving one context strategy is best. Vendor guidance and frameworks are useful starting points, but architecture choices should be tested on the agent’s own workload.
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