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Give an AI agent the information it needs to decide, but do not let information alone authorize consequential actions. Put durable rules in high-authority instructions, provide the current task and relevant facts in the run input, fetch changing information when needed, and treat retrieved pages, files, and messages as untrusted data. Then restrict tools, validate proposed actions at the point they can change external state, and require human approval for sensitive side effects. These controls reduce risk; no prompt or context template guarantees that an agent will behave safely.
What context does an AI agent need before it uses a tool?
An agent needs enough model-visible information to understand its task, the relevant facts, the boundaries on its behavior, and when to stop or seek approval. “Available somewhere in the application” is not enough: the model must receive the information through instructions, run input, conversation history, tools, retrieval, or web search at the moment it makes a decision.
Context is not a single prompt. It is the information and constraints that reach the model throughout a run. OpenAI’s Agents SDK context-management guide identifies instructions, run input, function tools, retrieval, and web search as ways to make information available. A useful design question is therefore not simply “What should we put in the prompt?” but “What must the model see before this decision, and through which channel should it arrive?”
Separate rules, task details, and reference material
- Rules: Put stable behavior requirements, role boundaries, and action limits in high-authority instructions. These should remain applicable across runs.
- Task details: Put the user’s current goal and relevant facts for this run in the run input or conversation.
- Reference material: Fetch changing or task-specific facts through retrieval or tools when needed. Treat their contents as data to assess, not new policy.
- Tool results: Make results available to the model in a form it can interpret, while checking that untrusted results do not silently become instructions.
For example, a support agent may have a persistent rule to draft a response rather than send it without approval. The current request might say which customer issue to address, while a retrieved account record supplies the latest facts. The record can inform the draft, but it should not override the sending rule.
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Make the boundaries explicit
State the requested outcome, what the agent may do, what it must not do, and what conditions require it to stop or ask. A narrowly defined task gives the model less latitude to reinterpret irrelevant material as a reason to act. This is especially important when the agent can access external content or tools that change data outside the conversation.
Do not assume that a rule was followed simply because it appears in an instruction. Check whether it is available in the relevant agent’s context at the point of decision, and whether the application independently enforces the boundary.
Where should persistent rules and changing information go?
Put stable rules in instructions and supply information that changes by task through run input, retrieval, or tools. This distinction keeps enduring policy separate from facts that may be incomplete, outdated, or supplied by an external source.
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| Information | Preferred channel | Why |
|---|---|---|
| Role, durable behavior, and action limits | High-authority instructions | These are intended to govern behavior across relevant runs. |
| Current user goal and run-specific details | Run input or conversation history | They describe the present task and may differ each time. |
| Changing or task-specific facts | Retrieval or tools, when needed | The agent can obtain relevant information without treating it as permanent policy. |
| Facts returned by an external source | Tool or retrieval results, clearly distinguished from instructions | Their content can inform a decision but may be misleading or malicious. |
The table is a design guide, not a claim that every system has identical context channels. Keep the key distinction intact even if your framework names or combines them differently: trusted policy must remain distinguishable from task input and external evidence.
Pass only what the next decision needs
More context is not automatically better. When a workflow has multiple stages or agents, pass only the fields required for the next step, and validate those fields against a defined structure. OpenAI’s agent safety guidance recommends structured outputs to constrain data flow and reduce free-form channels through which injected instructions might travel.
A conceptual handoff might contain a customer identifier, a requested operation, and extracted facts relevant to that operation. It should not forward an entire retrieved email thread as if every sentence were a trusted instruction. Structure helps define what downstream components can use; it does not establish that the extracted values are true or safe.
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How do I keep prompt injection from triggering an agent action?
Treat retrieved pages, emails, files, and other third-party content as untrusted data, and limit how that content can influence tools. Prompt injection is an attempt by a third party to place malicious instructions in material the model receives. It becomes a practical security risk when that material can influence a powerful action, such as sending information externally or changing a record.
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OpenAI’s “Understanding prompt injections” describes prompt injection as third-party content that misleads the model by inserting malicious instructions into the conversation context. Its later security guidance frames the risk in terms of an untrusted source influencing a consequential sink. That framing is useful in implementation: identify which inputs are untrusted, which tools can cause harm, and what barriers stand between them.
Keep external text out of trusted instruction channels
- Do not paste retrieved content into a high-authority instruction field or label it as policy.
- Mark external material as reference data and tell the agent to use it only to answer the current task, not to change its rules.
- Extract specific fields needed for the next step rather than passing unrestricted content forward.
- Validate extracted values and tool arguments before allowing them to reach a side-effecting tool.
These are layered protections, not a guarantee that the model will identify every attack. An attacker may phrase hostile text as a system notice, a request from a supposed authority, or an instruction to reveal information. The application should therefore limit the consequences of mistaken interpretation rather than rely on wording alone.
Think in sources, sinks, and paths
For each workflow, document the path from external input to possible action. A retrieved page may be harmless when it is used to summarize a fact, but risky if its text can directly influence a tool that sends a message or edits a production record. Ask what data the model can read, what the model can propose, and what the application allows to happen next.
Where the answer depends on extracted information, constrain the handoff to expected fields and check the result before using it. Where the next step is consequential, require a separate check or approval. This source-and-sink approach, reflected in OpenAI’s March 11, 2026 article on resisting prompt injection, emphasizes limiting impact rather than assuming that one prompt can prevent manipulation.
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Give the agent only the data and tool access its assigned task requires. A clear task description cannot compensate for permissions that let the agent reach unrelated records or perform broader actions than the task needs.
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Review access at both the data and action levels. If a task can be completed with logged-out or narrower access, prefer that over a broader session. Separate read-only capabilities from tools that send, delete, purchase, publish, or otherwise change external state.
| Tool capability | Typical risk distinction | Design focus |
|---|---|---|
| Read-only lookup | Provides information without changing external state | Limit which sources and records are accessible; treat returned content as untrusted. |
| Side-effecting action | Changes data or communicates outside the run | Validate arguments and scope, and add approval where the consequences warrant it. |
This distinction is about capability, not a claim that read access is risk-free: sensitive information can still be exposed or misused. Match access to the task and consider the possible impact of both reading and acting.
Define stop conditions before the agent runs
Specify the conditions under which the agent must not proceed on its own. Examples include a missing required fact, conflicting records, an action outside the assigned scope, or a request that could have material financial, legal, production-data, or physical consequences. These are useful policy patterns, not a universal checklist; teams should select stop conditions appropriate to their workflow.
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When should an action require human approval?
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OpenAI’s Agents SDK guidance describes approval as an interruption: the application can approve or reject the proposed action before resuming the same run. This is different from asking the model to “be careful.” The application creates a decision point outside the model’s own interpretation.
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Show the action, not just a summary
A useful approval screen presents enough detail to judge what will happen: the operation, target, relevant arguments, and any consequential content that will be sent or changed. A vague prompt such as “The agent wants to continue” gives the reviewer little basis to catch a wrong recipient, record, or operation.
Approval does not replace automated controls. Validate inputs, outputs, and tool behavior as appropriate, then ask a human to make the decision when the consequences call for it. A person may still miss a problem, so retain scope limits and checks at the tool boundary.
Attach checks where the side effect occurs
Put validation and approval close to the tool that changes external state. A check attached only to the overall agent boundary may not cover every downstream step or tool. OpenAI’s SDK documentation notes that agent-level input checks run only for the first agent and output checks only for the final agent, while tool checks apply to the specific tools where they are attached.
This matters in workflows with multiple agents or chained tools. Map each side effect to its actual execution point and confirm that the relevant check runs there. Do not infer that a check on one agent automatically protects every later tool call.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How can I test whether the context and action boundaries work?
Test the complete workflow against both ordinary tasks and cases that should fail, pause, or ask for clarification. Inspect traces and tool calls to confirm that the agent saw the right information, kept untrusted content in its proper role, and respected the action boundary.
Build cases around realistic failure modes
- Ordinary request: The model has the required facts and should complete an allowed task.
- Ambiguous request: A required detail is missing or conflicting, so the expected behavior is to ask or stop.
- Hostile or misleading retrieved content: External text attempts to override policy or redirect the task.
- Malformed tool arguments: A proposed action contains an invalid or out-of-scope value.
- Sensitive action: The agent should pause for approval rather than execute immediately.
For each case, record the expected outcome: proceed, refuse, request clarification, or interrupt for approval. Review not only the final response but also which context the model used and which tools it called. A correct-sounding answer can still conceal an unsafe tool path.
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Re-evaluate after meaningful changes
OpenAI’s safety guidance recommends trace grading and evaluations. Its prompt-engineering guidance recommends pinning production applications to model snapshots and measuring behavior as prompts or model versions change. Use those practices to compare behavior over time rather than assuming that a previously acceptable workflow remains acceptable after a change.
There is no universal threshold for “enough context” established by these sources. Define acceptance criteria for the task: what facts must be available, which actions are permitted, which errors require a stop, and what evidence in a trace shows the system respected those requirements. Criteria should reflect the workflow’s consequences, not a generic context-length target.
What is a practical pre-action checklist?
Before enabling an agent to act, walk through the full decision path from information to effect. The checklist below turns the design principles into review questions; a “no” on a consequential item should lead to a fix or a narrower deployment.
- Task: Is the requested outcome specific, and are allowed actions and stop conditions explicit?
- Context: Will the model see the necessary current facts before deciding, through a suitable channel?
- Trust: Can the system distinguish policy from user input and retrieved or tool-returned content?
- Data flow: Are handoffs restricted to necessary, validated fields rather than unrestricted external text?
- Access: Does the agent have only the data and tools required for this task?
- Action boundary: Are arguments validated and checks attached to the actual tool that changes state?
- Approval: Can a reviewer inspect the proposed action before sensitive consequences occur?
- Evaluation: Have normal, ambiguous, adversarial, malformed, and approval-required cases been exercised and inspected?
Keep the checklist tied to a concrete workflow and revisit it when the task, permissions, tools, prompt, or model snapshot changes. The central engineering aim is not to make the model memorize every possible fact or attack; it is to expose relevant information, preserve trust boundaries, and constrain what can happen when the model is wrong.
Frequently Asked Questions
Can an AI agent use information that is not in its prompt?
Yes, if the application exposes it through a tool, retrieval system, web search, conversation history, or another context channel at the decision point. Information stored elsewhere but never made available to the model cannot guide that decision.
Should an AI agent remember everything from previous tasks?
Not by default. Carrying all prior material forward can mix stale facts or untrusted text into a new task; retain or retrieve only what the current task requires, and keep durable rules separate from remembered reference data.
Do human approvals make an AI agent safe?
No. Approval is one checkpoint, and its usefulness depends on whether the reviewer can see the actual action and judge its consequences. Narrow permissions, argument validation, and evaluation remain important even when approval is enabled.
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How do I know whether an agent has too much context?
Look for irrelevant or sensitive material that the task does not require, especially if it can affect tool choices or be passed to downstream stages. A task-specific evaluation should test whether removing that material changes the agent’s decision or reduces unnecessary exposure.
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