The Tool Desk
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What should stay in an AI agent’s context?
Keep information in the active context when it can change the agent’s next decision. A useful working set usually includes:
- The goal and constraints, including safety-critical instructions.
- The current plan and the information needed for the next step.
- Decisions already made, unresolved issues, and dependencies.
- Relevant results from recent tool calls, rather than entire histories of output.
Separate this working set from large or frequently changing material. Documents, databases, codebases, and other external collections can remain outside the prompt if the agent has a way to find and retrieve relevant portions. OpenAI’s Agents SDK describes instructions, run input, tools, and retrieval or web search as ways to make information available to a model. Anthropic’s engineering guidance likewise describes just-in-time retrieval using lightweight references and tools.
How much context should you give an AI agent?
There is no universal token threshold that makes an agent reliable. The right boundary depends on the selected model, the task, the amount of information that must be available at once, and the room needed for the model’s response and tool cycle.
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Budget for the complete request, not just the text you wrote. Depending on the model, the context allocation may account for input and output tokens and may also include reasoning tokens. A large prompt can exceed the allocation and lead to truncated output, as OpenAI explains in its API documentation, “Conversation state.” Token limits and accounting differ by model, so check the current documentation for the model you actually use and leave headroom for its response.
- Estimate the active load: include stable instructions, task input, retrieved passages, tool responses, expected output, and any reasoning tokens counted by the model.
- Remove low-value material: omit repeated instructions, irrelevant history, and tool output that is no longer needed.
- Keep exact source material available: if a summary could lose a detail that matters later, store the original externally and retrieve it when needed.
Which context strategy should you use?
These approaches can be combined. A practical design often uses a small stable instruction core, task-specific input, retrieval for external detail, and notes or compaction for continuity.
| Strategy | Good fit | Main advantage | Main risk or cost |
|---|---|---|---|
| Selective upfront context | A small, known, stable working set | Information is available without a retrieval step. | Irrelevant material and token costs can grow with the set. (Anthropic, “Effective context engineering for AI agents”) |
| Just-in-time tools and retrieval | A large, changing, or uncertain corpus | The agent can load relevant portions as needed. | Exploration adds latency and depends on capable tools and navigation. (Anthropic, “Effective context engineering for AI agents”) |
| Compaction | Long, continuous conversations or tasks | A shorter state can carry work forward. | A summary can lose subtle details. (Anthropic, “Effective context engineering for AI agents”; OpenAI API, “Compaction”) |
| Structured external notes | Milestone work and context resets | Progress and dependencies persist outside the active window. | Notes can become stale or omit details if they are not maintained. (Anthropic, “Effective context engineering for AI agents”) |
| Larger context window | Large but coherent inputs or multimodal material | More material can fit in one request. | Cost, latency, and relevance limits remain. (Google AI for Developers, “Long context”) |
When should an agent retrieve information instead of receiving it upfront?
Preload a small set when it is stable
If you already know which information the task needs and that information is unlikely to change, supplying a carefully selected working set can avoid repeated searches. Keep the set narrow enough that unrelated material does not crowd out the task.
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Retrieve on demand when relevance is uncertain
For a large or changing corpus, give the agent tools to search and fetch specific slices. Lightweight references such as file paths, stored queries, or links can point to material without loading it all at the start. This can reduce irrelevant context, but the agent will spend time exploring, and the result depends on the quality of its tools and navigation.
Use a hybrid boundary for mixed tasks
Keep essential rules and a compact task brief present from the outset, then retrieve deeper details only when a decision calls for them. This balances direct access to known requirements with access to material whose relevance is harder to predict.
How do you preserve an agent’s memory across a long task?
Compact the conversation deliberately
Compaction summarizes a growing conversation so the work can continue with a shorter context. Do it at a deliberate threshold that leaves room for the next model response and tool cycle. Preserve the goal, constraints, decisions and their rationale, completed work, open issues, important references, and the next action. Remove redundant tool output only when it is safe to do so.
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Do not assume that a fluent summary retained every important detail. Anthropic cautions that aggressive compaction can lose subtle context whose value becomes apparent only later. Keep exact values or source material outside the summary when fidelity matters.
Write structured notes for milestones and resets
For work that pauses, crosses context resets, or proceeds through milestones, save a concise handoff outside the active transcript. Record what has been completed and what depends on it, alongside the current state and next step. Retrieve the notes when work resumes, and update them when decisions change.
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OpenAI’s Responses API documentation describes server-side compaction through context_management and compact_threshold, as well as a standalone compact endpoint. Its example uses a compact_threshold value of 200,000; that is an example request value, not a universal recommendation or a statement of current model limits. The documentation describes compaction items as opaque rather than human-interpretable, so follow the current API reference’s chaining behavior instead of treating those items as editable notes.
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Does a larger context window make an AI agent more reliable?
No—not by itself. A larger window lets a request contain more material, but the agent still has to identify the relevant information among it. Google’s “Long context” guide describes many Gemini models with windows of 1 million tokens or more, but that figure is specific to the models and page updated June 22, 2026; check Google’s current Models page for a particular model’s limit.
Google characterizes extraction from large chunks as approximately 99% accurate in many cases, while also warning that performance can vary, especially when a request contains multiple retrieval targets. That is Google’s description, not a universal or independent benchmark. The guide also notes that longer requests generally increase time to first token and that caching may help with repeated inputs.
Anthropic’s guidance highlights another issue: irrelevant context can still interfere even when the window is large. More capacity is useful when the additional information is coherent and likely to matter; it is not a substitute for selection, retrieval, or state management.
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How can you test whether your context boundary works?
Test the boundary against failures the agent could encounter in its actual task, rather than choosing a threshold by guesswork. Include cases that require the agent to:
- Find important facts near the beginning, middle, and end of a long history.
- Retrieve multiple independent pieces of information.
- Resolve conflicting or stale notes.
- Continue correctly after compaction or a context reset.
- Retain constraints while using tools and producing a final response.
Track task success, retrieval precision, missed constraints, token use, latency, and cost. The vendor guidance cited here offers implementation advice and examples; it does not establish a universal numeric threshold or an independent benchmark proving one context strategy best for every agent task.
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