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Give an AI agent enough information to make the decision, but no more authority than the task requires. Put durable rules in high-priority instructions, pass the current request and user facts as run input, retrieve changing information only when needed, and treat every external document as untrusted data. Before a tool can change external state, validate its arguments, restrict its permissions, and pause for human approval when the consequence is sensitive.
This design does not make prompt injection or other failures impossible. It creates several controlled points where bad instructions can be separated, detected, limited, or rejected before an email is sent, a record is edited, money moves, or production systems change.
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What does “context” mean for an AI agent?
Context is everything the model can see at the moment it chooses its next response or tool call. In an agent application, that can include high-priority instructions, the current run input, conversation history, tool definitions, retrieved passages, web results, and previous tool outputs. If a fact is not exposed through one of those channels, the model cannot reliably use it.
The OpenAI Agents SDK context guidance identifies instructions, run input, function tools, retrieval, and web search as ways to make information model-visible. The practical test is simple: can you point to the exact place in the assembled context where the agent sees the rule or fact immediately before it must act?
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Context is not permission. A model may read a customer record without being allowed to edit it, or receive a policy excerpt without being allowed to send that policy to a third party. Treat information exposure and operational authority as separate design decisions.
Which information belongs in instructions, input, retrieval, or tools?
Use each channel for the kind of information it can carry reliably. Stable behavior belongs in instructions; request-specific facts belong in the run; changing or large information should be fetched on demand; and actions should be exposed as narrowly defined tools.
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| Context channel | Best use | Design caution |
|---|---|---|
| High-priority instructions | Role, durable policies, prohibited actions, stop conditions, output format | Do not insert arbitrary retrieved text here; it would gain authority it has not earned. |
| Run input or conversation | Current user goal, verified user facts, choices made during this run | Validate identity, scope, and sensitive values before passing them through. |
| Retrieval | Current policies, account data, documentation, or other task-specific reference material | Retrieved text is data, not a command. Mark its origin and treat it as potentially hostile. |
| Function or API tools | Read information or perform a narrowly defined operation | Tool schemas and server-side authorization must enforce limits; a description alone is not a security boundary. |
| Web search | Fresh public information when the task needs it | Pages can contain instructions aimed at the agent. Constrain what can flow from results into actions. |
Keep the assembled context legible. Label sections such as policy, user request, reference material, and tool result so the model and your logs show which source supplied each statement. When one stage hands data to another, pass only the fields the next stage needs.
How should you write the agent’s task and boundaries?
Start with a specific outcome and explicit limits. Broad delegation gives unrelated or malicious content more opportunity to influence behavior, while a narrow task reduces the decisions the agent is allowed to make.
State the objective
Describe the result in operational terms, such as “classify this support request and draft a reply,” rather than “handle support.” Include the audience, the records or systems in scope, and what counts as completion.
List allowed and forbidden actions
Say which tools the agent may call and which it must never call. For example, an agent may read an order and prepare a cancellation request, but it may not cancel the order or issue a refund without approval. Add stop conditions for missing identity checks, conflicting records, ambiguous instructions, or requests outside the assigned account.
Specify escalation behavior
Tell the agent when to ask a clarifying question, return a structured refusal, or request human review. Do not rely on a general “be safe” sentence to produce these branches consistently. OpenAI’s prompt guidance describes the instructions parameter as the place for high-level behavior, goals, tone, and examples of correct responses.
Pin the operating contract
Keep durable rules versioned in application code or configuration. When you change the prompt or model, run the same evaluation set and compare tool calls, refusals, and escalation decisions. OpenAI recommends pinning production applications to model snapshots and measuring behavior as prompts or model versions change.
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How do you keep retrieved content from becoming an instruction?
Assume that email, web pages, uploaded files, tickets, and database text may contain instructions written by someone other than the user or your application. Prompt injection occurs when a third party places such instructions in content that enters the model’s context and tries to change what the system does. OpenAI defines it as a third party misleading the model by injecting malicious instructions into the conversation context.
Separate source types
Represent external material as a clearly delimited data object with fields such as source, timestamp, and text. Tell the agent that the object is reference material and cannot change its operating rules. Keep it outside the higher-authority instruction channel.
Extract facts instead of forwarding prose
Use a schema that names only the fields needed downstream. For an invoice, that might be invoice_id, amount, currency, and due_date; do not pass an entire note field to a payment tool when it is not required. Validate types, ranges, identifiers, and allowed enumerations before the next stage receives the values.
Design for the source-and-sink combination
An injected sentence is most dangerous when it can travel from an untrusted source to a powerful sink, such as sending data to a third party, opening a link, executing code, or changing a record. OpenAI’s security guidance recommends limiting the consequences of manipulation through ordinary security engineering: reduce permissions, narrow data flow, validate arguments, and require confirmation for sensitive effects.
Do not promise perfect detection
Classifiers and pattern checks can catch some suspicious content, but they cannot prove that a document is safe. Treat these controls as layered risk reduction. The action boundary still needs authorization, validation, logging, and—when warranted—human approval.
What is the difference between context and permission?
Context answers “what can the model know?” Permission answers “what can the workflow do?” A safe agent can receive enough information to recommend an action while lacking the credentials or tool route needed to perform it.
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Expose only the records needed for the current job. Use a logged-out or read-only view when that is sufficient. Filter fields before retrieval so secrets, unrelated accounts, and private notes never enter the context in the first place.
Scope tools by capability
Prefer separate read and write tools over one general-purpose tool. Define resource identifiers, permitted operations, maximum amounts, and valid destinations in the tool contract. Enforce those rules on the server or service that executes the call; the model’s compliance with a prompt is not authorization.
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Set explicit action limits
Financial transfers, legal submissions, production changes, account deletion, outbound communications, and sensitive MCP actions deserve explicit limits. Google Cloud’s Agent AI Use Guidelines, last modified October 12, 2026, place responsibility for governance and appropriate agent permissions on the deploying organization and call for oversight where legal, economic, or physical consequences may be material.
| Tool category | Typical exposure | Required control |
|---|---|---|
| Read-only lookup | Fetch a record, policy, or status | Resource-level authorization, field filtering, audit log |
| Drafting | Prepare a message, form, or change set without submitting | Schema validation and clear “draft” status |
| Side effect | Send, edit, cancel, purchase, deploy, or execute | Argument checks at the tool boundary, scoped credentials, and approval when consequential |
Where should validation happen before an agent acts?
Put validation as close as possible to the side effect. Validate the input entering the workflow, every value crossing a stage boundary, the arguments sent to a tool, and the result returned by that tool. A check only at the outer agent boundary may miss a dangerous call made by a downstream agent or tool.
Validate inputs
- Confirm the user and account are in scope.
- Normalize identifiers, dates, currencies, and amounts before reasoning over them.
- Reject missing, contradictory, or out-of-range values rather than asking the model to guess.
Constrain intermediate outputs
Use structured outputs with an explicit schema instead of free-form text between agents. OpenAI’s agent safety guidance recommends this approach because it reduces channels through which injected instructions can travel. A parser should reject unknown fields, invalid enum values, excessive lengths, and nested content that the next stage does not expect.
Validate tool arguments
Check authorization, destination, amount, resource ownership, and idempotency at execution time. Recheck facts that may have changed since retrieval, such as a balance, order status, or deployment version. Return a machine-readable error when a call fails so the agent cannot reinterpret a denial as success.
Validate tool results
Verify that the result matches the requested resource and operation. Mark partial, stale, or unconfirmed results explicitly. Never let a free-form error message from an external service become a new policy instruction without parsing and review.
When should a human approve an agent action?
Pause before a consequential side effect when a person should inspect the exact operation. Approval is a decision point, not a decorative notice: the reviewer must see what will happen, to whom, with which arguments, and what evidence supports it.
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Actions that commonly warrant approval
- Sending an external message or publishing content.
- Editing or deleting a customer, financial, legal, or production record.
- Issuing a refund, placing an order, transferring funds, or changing access.
- Running a shell command, deploying code, or invoking a sensitive MCP action.
- Sharing personal, confidential, or regulated information with a third party.
Show the executable proposal
Display the actual tool name, arguments, target, amount, and relevant source records. A summary such as “the agent wants to update the account” is not enough to detect a wrong identifier or an injected destination. Let the reviewer approve, reject, or send the run back for clarification.
Resume the same run safely
The OpenAI Agents SDK describes approval as an interruption that the application can approve or reject before resuming the same run. Preserve the validated proposal and approval identity in the audit trail. If any material argument changes after approval, require a new approval rather than silently continuing.
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Combine people and automation
Automated guardrails can validate inputs, outputs, and tool behavior; human review decides whether a sensitive operation should proceed. Attach automated checks to the tool where the side effect occurs. The SDK documentation notes that agent-level input checks run only for the first agent and output checks only for the final agent, whereas tool checks apply where they are attached.
How can you implement a context-and-action pipeline?
The following sequence keeps information flow and authority visible in code and in logs.
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- Authenticate and authorize the run. Resolve the user, tenant, role, and resource scope before retrieval. Issue the narrowest credentials the task can use.
- Assemble stable instructions. Put role, policy, output schema, and escalation rules in the high-priority instruction layer. Version this text.
- Pass the current request. Add the user’s goal and verified facts as run input. Keep unverified claims labeled as such.
- Retrieve selectively. Query only the sources needed for this task. Attach provenance and timestamps, and delimit the returned material as untrusted reference data.
- Plan with read-only tools first. Let the agent gather status and produce a proposed operation before exposing a write path.
- Extract and validate. Convert relevant facts into a strict schema. Reject unexpected fields, invalid values, and instructions embedded in text fields.
- Present the proposed side effect. Show the exact tool and arguments to automated checks and, when required, a human reviewer.
- Execute with server-side enforcement. Reauthorize the request, enforce limits, use idempotency where appropriate, and record the result.
- Verify and report. Confirm the external state change, distinguish success from partial completion, and return a concise record of what happened.
How should you test whether the agent had the right context?
Test the workflow, not just the final prose response. A correct-sounding answer can conceal a bad retrieval choice or an unsafe tool call.
Build a scenario matrix
- Ordinary requests: the expected record is available and the permitted action succeeds.
- Ambiguous requests: a missing identifier or conflicting instruction should trigger a question or stop.
- Hostile content: a web page, email, or file contains instructions to reveal secrets, ignore policy, or call an unrelated tool.
- Malformed arguments: amounts, identifiers, destinations, or enum values are invalid or out of scope.
- Sensitive actions: the agent must pause and present an accurate proposal for approval.
- Changed state: a record becomes stale between retrieval and execution, requiring a recheck.
Inspect traces and tool calls
Review which context sources were exposed, what the agent relied on, which tools it selected, and whether arguments changed between planning and execution. Trace grading and evaluations are recommended in OpenAI’s agent safety guidance. Store enough metadata to reproduce a decision without retaining unnecessary sensitive content.
Define acceptance criteria per workflow
There is no universal metric for “enough context.” Set measurable criteria for your task, such as correct resource selection, refusal of out-of-scope requests, zero execution of unapproved writes, or a required clarification rate for ambiguous cases. Re-run the suite when prompts, retrieval indexes, tool schemas, permissions, or model snapshots change.
What are common failure modes and fixes?
The agent cites a policy it never received
Cause: the policy was assumed to be implicit or was retrieved after the decision point. Fix: expose the current policy before planning, include its version or effective date, and require a stop when no applicable policy is found.
A document tells the agent to ignore its rules
Cause: untrusted text entered an instruction-like channel or was passed wholesale to a downstream tool. Fix: keep it in a labeled data field, extract only required values, and validate the resulting schema.
The agent can read more than it needs
Cause: a broad search endpoint or shared credential. Fix: filter by tenant, resource, and field before retrieval; issue read-only or logged-out access where sufficient.
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Cause: the check was attached only to an agent boundary. Fix: attach authorization, argument validation, and approval logic to the side-effecting tool itself.
The reviewer approves a vague summary
Cause: the interface hides the executable arguments. Fix: show the exact target, operation, values, and supporting records; invalidate approval if any material field changes.
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How do you maintain context quality over time?
Context changes as policies, data, tools, and models change. Treat the assembled prompt, retrieval results, tool schemas, permissions, and approval rules as one versioned workflow.
- Record the instruction version, model snapshot, retrieved source identifiers, and tool schema version for each consequential run.
- Expire or refresh retrieved facts whose validity period has passed.
- Review permissions when a task expands; do not grant broad access merely to avoid a retrieval failure.
- Use canary evaluations for prompt and model changes before production rollout.
- Sample traces for both safe refusals and successful actions; failures often hide in apparently normal runs.
- Remove obsolete tools and credentials so an old instruction cannot reach a still-powerful endpoint.
Governance remains a deployment responsibility. Google Cloud’s Agent AI Use Guidelines state that organizations must determine their governance obligations and whether tools are needed to meet them. The right controls depend on the data, jurisdiction, users, and consequences in your workflow.
Frequently Asked Questions
How much conversation history should an AI agent receive?
Include the turns that establish the current goal, commitments, and unresolved decisions, then summarize or omit irrelevant history. Preserve authoritative facts in structured fields so a long or contradictory conversation cannot silently override the task contract.
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Can a second AI agent safely review the first agent’s plan?
A second agent can provide another check, but it does not replace authorization or human approval. Give the reviewer a narrow schema and independent access to the proposed arguments, then enforce the final decision at the side-effecting tool.
What should be logged when an agent is denied?
Record the run identifier, policy or validation rule that blocked the call, the tool and normalized arguments, and whether a person was asked to intervene. Redact secrets and unnecessary personal data while retaining enough provenance to reproduce the decision.
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