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Give an AI agent the smallest, clearest set of information it needs for the next decision, then restrict what it can do with that information. Put durable rules and role boundaries in high-authority instructions; pass the current task and user facts in the run input; fetch changing details with retrieval or tools; treat everything fetched from outside the trusted policy as untrusted data; validate tool calls at the action boundary; and require human approval before consequential side effects.
This design does not make prompt injection or tool misuse impossible. It reduces the damage a misleading document, email, web page, or malformed argument can cause by separating policy from data, narrowing permissions, checking actions, and giving a person a chance to stop sensitive operations.
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What context should an AI agent receive before using tools?
An agent should receive a specific task, the desired outcome, the facts needed for that task, explicit boundaries, and the conditions that require it to stop or ask. The model can use only information your application exposes in its context, whether that arrives through instructions, run input, conversation history, retrieval, web search, or a tool result.
A useful design question is: Will the model see this fact at the moment it must decide? If not, the fact is not operational context, even if it exists somewhere in your database. Expose stable rules continuously and retrieve volatile information only when the workflow needs it.
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Define the task narrowly
State one outcome in concrete terms, such as “identify duplicate invoices and prepare a review list,” rather than “manage accounts.” Include what a successful result contains and what the agent must not attempt. Broad delegation gives unrelated or malicious content more opportunities to influence behavior.
State stop conditions
Tell the agent when to pause: uncertainty about identity, missing required fields, a conflict between policy and source material, a request to disclose restricted data, or any operation that changes external state. A stop condition is more useful than a general instruction to “be careful” because it gives the application a testable branch.
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Keep user goals, organization policy, reference material, and tool results distinguishable. A retrieved paragraph can support an answer, but it should not silently become a new system rule. Preserve provenance in your internal data model so later stages know whether a value came from an approved policy store, a user, or an untrusted page.
Where should persistent rules and changing information go?
Place durable behavior and authority rules in instructions, current task details in the run input or conversation, and changing or task-specific facts behind retrieval or tools. This split follows the context sources described in the OpenAI Agents SDK context guidance.
| Context type | Best location | Typical contents | Design caution |
|---|---|---|---|
| Persistent policy | High-authority instructions | Role, allowed objectives, prohibited actions, escalation rules, output style | Keep it concise and versioned; do not let retrieved text overwrite it |
| Current request | Run input or conversation history | User goal, selected account, date range, constraints, confirmed preferences | Validate identity and scope before treating user claims as authorization |
| Task-specific reference | Retrieval or read-only tools | Policies, records, inventory, documentation, status data | Mark as data and retain source and timestamp |
| External live information | Web search or specialized tools | Current public pages, rates, availability, notices | Assume it may contain instructions aimed at the agent |
| Downstream hand-off | Validated structured output | Only fields the next agent or tool actually needs | Reject extra free-form fields that could carry injected instructions |
Keep the context legible
Use explicit labels such as “trusted policy,” “user request,” “untrusted reference,” and “tool result.” Delimit sections consistently and include a source identifier, retrieval time, and confidence or validation status where those values affect a decision. Formatting is not a security boundary by itself, but it helps the model and your logging system distinguish channels.
Control context growth
Do not dump an entire mailbox, database table, or website into every prompt. Retrieve the smallest relevant set, summarize only after preserving facts needed for audit, and discard fields the next step cannot use. If a long conversation is compressed, retain the active permissions, unresolved questions, source references, and approval state rather than only a prose summary.
How do I stop prompt injection from triggering an agent action?
Assume that any third-party content entering context may contain instructions intended to redirect the agent. OpenAI defines prompt injection as a situation in which a third party misleads the model by injecting malicious instructions into the conversation context. The risk becomes serious when that content can reach a powerful “sink,” such as sending data externally, opening a link, or calling a side-effecting tool.
Separate data from policy
Keep retrieved pages, emails, uploaded files, and tool-returned text outside the trusted instruction channel. Tell the agent that these sources are reference data and cannot change its role, permissions, or approval requirements. Do not concatenate arbitrary text into a system or developer instruction field.
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Extract fields instead of forwarding prose
When a later step needs a value from untrusted material, extract only the required fields into a schema. For example, a document-review stage might return invoice_id, amount, currency, and due_date; it should not pass the document’s free-form “instructions” field to a payment tool. Validate types, ranges, and identifiers before the value crosses into a higher-impact stage.
Constrain the source-to-sink path
Map which sources can influence which actions. A public web page may be allowed to supply a product description for a draft, but not a recipient address for an outbound message without independent verification. The more direct the path from untrusted text to an irreversible action, the more checks and human review it needs.
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- Give the agent a narrow, explicit task and deny unrelated actions.
- Use least-necessary data and tool permissions.
- Return structured fields rather than free-form instructions between stages.
- Validate tool arguments and results at the tool boundary.
- Require confirmation for sensitive side effects.
- Log the source text, extracted fields, proposed action, and reviewer decision.
These measures reduce impact; they are not proof that every attack will be detected. OpenAI’s security guidance frames prompt injection as a security-engineering problem in which limiting consequences matters even when detection is imperfect.
How much access should an agent have?
Give an agent only the data and tools required for its assigned task, and make the permission scope explicit. Context is not permission: showing a record to the model does not mean the model should be able to edit it, and a tool being available does not mean every task should call it.
Prefer read-only access first
Use logged-out or read-only access when it satisfies the task. A research agent may need public documents but no account credentials. A reconciliation agent may need to read invoices while lacking the ability to approve payments. Separate read and write credentials so a prompt mistake cannot automatically escalate into a state change.
Define action limits
For every side-effecting tool, specify allowed objects, fields, destinations, amounts, frequency, and operating environment. Examples include “update only the ticket status,” “send only to addresses already verified in the request,” or “run commands only in a disposable test environment.” Enforce these constraints in application code, not only in prose instructions.
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Permissions can change during a long run. Before executing a consequential call, verify the current user, resource ownership, policy version, and approval state. Do not rely on an earlier model statement that the action is authorized.
What should be validated before and after a tool call?
Put checks as close as possible to the operation that can change external state. Structured arguments, schema validation, policy checks, and result validation should run where the tool is invoked, because a guardrail attached only to an outer agent may not cover every downstream agent or tool.
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Validate arguments
- Require a strict schema with enumerated actions and typed fields.
- Reject unknown fields and free-form command strings when a structured alternative exists.
- Check identifiers against the user’s authorized scope.
- Apply numeric limits, destination allowlists, and rate limits.
- Normalize values before comparison so alternate encodings cannot bypass a rule.
Validate results
Check that the tool actually affected the intended resource and returned data in the expected shape. A successful HTTP response is not proof that the correct account was updated. Treat unexpected fields, redirects, partial completion, and stale versions as reasons to stop or escalate.
Constrain inter-agent hand-offs
If one agent calls another, pass a typed object containing only necessary fields. The receiving agent should not inherit the sender’s entire transcript or arbitrary retrieved text. Validate the hand-off against a schema and preserve the original user and authorization context separately.
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When should a human approve an agent action?
Pause for a person before an action with financial, legal, production-data, privacy, security, or physical consequences. Show the reviewer the exact operation and arguments, not merely a summary such as “send the invoice.” The OpenAI Agents SDK describes approval as an interruption that the application can accept or reject before resuming the same run.
Design the approval screen around the real call
- Display the tool name, target resource, destination, and every material argument.
- Show which source supplied each important value.
- Identify the user, permission scope, and policy version in effect.
- Explain what will happen and whether the operation is reversible.
- Offer approve, reject, and request-changes paths.
Do not treat approval as a substitute for controls
Automated guardrails and human review have different jobs. Guardrails reject malformed or unauthorized calls consistently; a reviewer handles ambiguity and accepts or rejects a valid proposal. Keep both, and attach automated checks to each side-effecting tool.
Use approval for sensitive MCP or shell operations
Actions such as sending a message, editing a record, cancelling an order, running a shell command, or invoking a sensitive Model Context Protocol operation should default to a pause unless the risk has been explicitly bounded and approved in advance. A pre-approved policy can narrow friction, but it should still specify exact tools, targets, and limits.
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How do I build a context-to-action workflow?
Use a staged pipeline in which each step adds only the context required for the next decision and no step silently upgrades authority.
- Capture the request. Record the user’s goal, identity, selected resources, and desired outcome. Reject ambiguity that would change the target or consequence.
- Load trusted instructions. Apply the agent role, organization policy, prohibited actions, output schema, and stop conditions from a versioned instruction source.
- Resolve scope. Determine which records and tools are permitted for this user and task. Prefer read-only credentials until a write is demonstrably needed.
- Retrieve narrowly. Fetch only relevant records or pages. Label every result as trusted policy, user-provided data, or untrusted external content, and retain provenance.
- Extract and validate. Convert external material into a minimal typed object. Check identifiers, ranges, dates, and allowed values before passing them onward.
- Plan without executing. Ask the model for a proposed action in a strict schema. At this stage, the output is a plan, not permission.
- Run policy checks. Validate the proposed tool, target, arguments, authorization, and current state in application code.
- Request approval when required. Interrupt the run and show the exact call and consequences to a reviewer.
- Execute the tool. Re-check authorization and limits immediately before the call, then invoke the narrowest available operation.
- Validate and record the result. Confirm the intended state change, capture errors or partial completion, and log the context, checks, approval, and result for evaluation.
What is the difference between read-only and side-effecting tools?
Read-only tools primarily supply context; side-effecting tools alter external state and therefore require tighter scope, validation, and often approval.
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| Characteristic | Read-only tool | Side-effecting tool |
|---|---|---|
| Purpose | Fetch or inspect information | Send, edit, delete, purchase, execute, or otherwise change state |
| Typical risk | Wrong, stale, or malicious information influences a later decision | Immediate external consequence, data loss, disclosure, or cost |
| Minimum control | Source labeling, relevance filtering, and result validation | All read-only controls plus strict arguments, authorization, limits, and an approval policy |
| Credential strategy | Logged-out or least-privilege access where possible | Separate write credentials, narrow resource scope, and short-lived authorization where practical |
| Recovery | Discard or re-fetch incorrect data | Provide rollback, idempotency, or a human recovery path |
How should guardrails be placed in a multi-agent system?
Place input, output, and tool checks according to the actual workflow boundary. The OpenAI Agents SDK guidance 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 tools where they are attached.
That means a check on the outer orchestrator is not enough if an inner agent can call a sensitive tool directly. Attach authorization, argument validation, and approval requirements to every tool that can create a consequential side effect. Use agent-level checks for broad workflow invariants, then repeat critical constraints at downstream boundaries.
How do I evaluate whether the agent received enough context?
There is no universal metric for “enough context.” Define acceptance criteria for each workflow and test whether the agent had the right information, ignored hostile material, selected the correct tool, and stopped when required.
Build a representative test set
- Ordinary requests with complete information.
- Ambiguous requests that should trigger a clarification.
- Missing, stale, or contradictory records.
- Retrieved pages, emails, and files containing misleading instructions.
- Malformed, out-of-range, or unauthorized tool arguments.
- Sensitive actions that must pause for approval.
- Partial failures, retries, duplicate requests, and changed permissions during a run.
Inspect traces, not just final text
Review which context was retrieved, how it was labeled, what the model extracted, which tool arguments it proposed, which checks ran, and whether approval occurred before execution. A fluent final answer can hide an unsafe tool path, so trace grading and tool-call evaluation are essential.
Pin versions and measure changes
For production systems, pin model snapshots where your platform supports it and re-run evaluations when prompts, models, tools, retrieval indexes, or policies change. OpenAI’s prompt guidance recommends measuring behavior as prompts or model versions change rather than assuming a wording change is harmless.
What are common context-design failures and their fixes?
The agent sees policy only after it acts
Symptom: A tool call is proposed before the relevant restriction is loaded. Fix: Put durable policy in the initial instructions and enforce the same rule at the tool boundary.
Retrieved text is treated as an instruction
Symptom: A web page or email tells the agent to reveal data or skip a check. Fix: Label the content untrusted, extract only typed fields, and prevent arbitrary text from entering the instruction channel.
The agent has more access than the task needs
Symptom: A research task can write records or access unrelated accounts. Fix: Split read and write tools, narrow credentials, and specify resource and action limits in code.
Approval describes a plan instead of the call
Symptom: A reviewer sees “process the order” but not the amount, destination, or account. Fix: Render the exact tool name and arguments, with provenance and reversibility information.
Only the outer agent is guarded
Symptom: A downstream agent invokes a powerful tool without the orchestrator’s checks. Fix: Attach validation and approval logic where each side effect occurs, then test the complete call graph.
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Long context hides the important constraint
Symptom: The model overlooks a limit buried in a transcript or document. Fix: Retrieve narrowly, summarize with preserved constraints and provenance, and place active boundaries in high-authority instructions as well as application checks.
What should a production readiness checklist include?
- The task, success condition, prohibited actions, and stop conditions are explicit.
- Persistent rules are versioned and separated from user or external content.
- Every retrieved item carries a source and trust classification.
- Only necessary fields cross each workflow or agent boundary.
- Read-only and side-effecting tools are distinct.
- Tool arguments and results are schema-validated at the tool boundary.
- Permissions are least-privilege and rechecked at execution time.
- Sensitive operations pause for a reviewer who sees the exact call.
- Logs capture context sources, extracted values, checks, approvals, and outcomes.
- Evaluations include normal, ambiguous, hostile, malformed, and high-impact cases.
- Model and prompt changes are tested against pinned production behavior.
- There is a rollback or recovery path for every consequential operation.
Google Cloud’s Agent AI Use Guidelines, last modified October 12, 2026, place responsibility for governance and appropriate agent permissions on the deploying organization. That responsibility cannot be delegated to a prompt: the surrounding application must enforce access, oversight, and recovery.
Frequently Asked Questions
Can an AI agent safely use information from email attachments?
Yes, if attachments are handled as untrusted reference data. Scan and parse them in an isolated step, extract only the fields required for the task, retain provenance, and prevent embedded instructions from directly selecting recipients, tools, or commands.
Should I give every agent the full conversation history?
No. Pass the smallest history needed to resolve the current task, along with active permissions, unresolved questions, and source references. Omitting irrelevant or sensitive turns reduces accidental disclosure and leaves less room for a malicious passage to influence a later action.
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How can I make an agent’s actions idempotent?
Include an application-generated operation key or request identifier in the tool schema and have the service reject duplicate executions. Pair that with a precondition, such as an expected record version, so a retry cannot overwrite a change made by someone else.
What evidence should I retain when a reviewer approves an action?
Store the exact tool arguments, the relevant source values, the policy and permission versions, the identity of the reviewer, the decision time, and the resulting tool response. This record supports incident review and helps distinguish a bad plan from a later execution failure.
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