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1Scan for outdated or missing drivers - takes under a minute2Clear out junk files and repair common Windows errors3Fix the driver behind crashes, sound loss and screen glitchesBuild and test the agent in ServiceNow AI Agent Studio, then govern it with two separate controls: an access control list (ACL) for who may discover or invoke it, and a run-as identity that determines what data and actions it can use. For production, add least-privilege role masking, supervised approval for consequential tools, representative security testing, domain-separation decisions, and a documented owner and rollback path. External-agent connections require additional patch, credential and ACL checks, while larger portfolios may need AI Control Tower for centralized oversight.
What you need before creating an agent
An AI agent is not simply a prompt connected to a chatbot. In ServiceNow, it can call tools that read data, update records or trigger workflows, so its identity and permissions are as important as its instructions. ServiceNow says responsible AI deployment requires more than configuring individual features; readiness, security, testing and ongoing governance belong in the same change process as the agent build.
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Confirm licensing and instance readiness
Check that your subscription includes the AI Agent Studio capabilities you intend to use and that the target instance release supports them. Documentation and release prerequisites change, so verify the release, patch level, licensing terms, AI Acceptable Use Policy and data-processing terms for the instance you will deploy. If you operate multiple business units or tenants in one instance, decide whether domain separation is required before you design tools or data queries.
Assign the build role and supporting roles
The documented creation flow requires sn_aia.admin. The administrator also needs whatever roles are required by the selected tools and channels. Do not assume that the administrator role should be granted to every person who will maintain an agent; separate builders, approvers, operators and auditors where your change-management model allows it.
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Define the agent’s purpose and boundaries
Write a short purpose statement before opening the studio. Name the records and data domains it may use, the actions it may take, the actions that always require a human, and the requests it must refuse or escalate. A narrow purpose makes ACLs, role masking, tests and incident review measurable.
Create an agent in AI Agent Studio
AI Agent Studio is the main build and test surface. The sequence below follows the documented user interface; labels can vary slightly by release.
- Open the agent workspace. In ServiceNow, choose Create and manage > AI agents > New. Confirm that you are working in the intended application scope and domain.
- Define the specialty. Give the agent a specific name and describe the job it performs. State the records it can work with, the information it should request when a task is ambiguous, and the conditions under which it must stop and escalate.
- Write operating instructions. Tell the agent how to verify identity and context, how to handle missing or conflicting data, which tools it may call, and what it must never infer. Instructions should require confirmation before irreversible actions even if a tool is technically available.
- Configure user-access ACLs. Decide who can discover or invoke the agent. The available choices include specified roles, authenticated users, or public access. Select specified roles for internal work by default. Use authenticated access only when every signed-in user is an appropriate audience, and use public access only for a deliberately public, low-risk use case with a reviewed data path.
- Choose the data-access identity. Select Dynamic user when the agent should act in the invoking user’s context, then apply role masking to limit that context to approved roles. Select a dedicated AI user only when a controlled service identity is genuinely required and its roles, ownership and credential lifecycle are documented.
- Add only necessary tools. Start with read-only tools and add write or action tools one at a time. For each tool, record the tables, fields, APIs, workflow actions and side effects it can reach. Remove unused tools rather than relying on the model to ignore them.
- Set supervision for sensitive actions. Use supervised mode for tools that change records, disclose sensitive information, send communications, approve transactions, alter configuration or otherwise create material consequences. The tool call should pause for a human approval decision before execution.
- Add an event trigger only for intended automation. If the agent should respond to a record event or schedule, configure the trigger and its conditions. If users should call it interactively, expose it through the appropriate chat channels instead of adding an automatic trigger.
- Select channels and access, activate, and save. Choose the channels in which the agent is available, activate it according to your change process, then select Save and test. The documented test flow requires the administrator and the roles needed by the tools being exercised.
- Register the agent. Store the owner, purpose, data domains, tools, run-as identity, ACL, approved roles, test prompts, expected outcomes, escalation route and disable or rollback procedure in the normal change and risk records.
Separate invocation access from execution permissions
Two decisions are often confused. An ACL answers “who may find or invoke this agent?” The run-as identity answers “what can the agent read or do after it has been invoked?” An invocation ACL does not grant the agent permission to bypass table, field or application security.
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| Decision | What it controls | Typical choices | Practical default |
|---|---|---|---|
| Invocation ACL | Discovery and ability to start a conversation or trigger | Specified roles; authenticated users; public | Specified roles tied to the use case |
| Run-as identity | Data and actions available during execution | Dynamic user; dedicated AI user | Dynamic user with approved-role masking |
| Role masking | Which roles are retained when a dynamic user invokes the agent | Explicit approved-role set | Mask to the minimum roles needed by the tools |
| Human supervision | Whether a person must approve a tool call before it runs | Required for selected tools; not required for low-risk reads | Require approval for sensitive or irreversible actions |
Dynamic user with role masking
Dynamic user execution inherits the invoking user’s context. Role masking narrows that context to a reviewed list, which supports least privilege while preserving user-specific access where it is needed. ServiceNow describes role masking as a security best practice and recommends adherence to least privilege. Do not override the role-masking requirement as a shortcut; any exception should have a documented business reason, owner, compensating control and expiry review.
Dedicated AI user
A dedicated AI user provides a stable service identity. This can simplify controlled integrations or scheduled work, but every permission on that account becomes part of the agent’s effective reach. Keep the role set small, assign a named owner, protect credentials, review the account regularly and disable it through the same process used to disable the agent.
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Why both controls are necessary
An agent can have a narrow invocation ACL yet still be overpowered if its run-as identity has broad roles. Conversely, a tightly masked identity does not make a public ACL appropriate if the agent handles internal information. Review the two controls independently during design, testing and every material change.
Design tools and approvals for safe action
Start with read-only capability
Read-only retrieval is easier to test and usually easier to reverse than updates or workflow execution. Add a write tool only when the purpose requires it, and document the exact tables, fields, records and conditions it can affect. Avoid bundling unrelated administrative tools into a general-purpose agent.
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ServiceNow provides supervised mode to enhance security for agents capable of sensitive or critical actions. Configure approval before execution when a call could modify a record, disclose regulated information, change an entitlement, approve spending, affect employment or healthcare decisions, alter security controls, or disrupt infrastructure. The approval request should show the proposed action, target record, material parameters and expected side effects so the reviewer can make an informed decision.
Make refusal and escalation explicit
Instructions should tell the agent to stop when identity, authorization, required fields or source data are uncertain. Define a human queue or named team for escalation, the information that must accompany the handoff, and the response the user receives while the request is pending. A graceful refusal is safer than an improvised workaround.
Choose channels and triggers deliberately
Interactive channels
Use chat channels when a person should initiate the request and see the conversation. Limit the channel audience with the invocation ACL and make sure the channel’s authentication context is preserved when the agent calls tools.
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Event triggers
Add an event trigger only when automation is an intentional outcome. Define the event, filter, frequency, retry behavior and stop condition. Test duplicate events and partial failures; an agent that runs automatically on every update can create loops or repeated changes if its trigger is too broad.
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Public access
Public exposure is a special case, not a convenience setting. Use it only when the agent’s purpose, data and tools are safe for unauthenticated users and when rate, abuse and privacy controls are in place. A public ACL cannot compensate for a tool that exposes internal records.
Test before production
The Save and test flow confirms that the configuration can run, but it is not a universal quality or security score. ServiceNow does not publish a standard test corpus or success-rate target for every agent. Define local acceptance criteria tied to the agent’s purpose and risk.
Build a representative prompt set
- Normal requests covering each supported task and channel.
- Ambiguous requests that should trigger a clarifying question.
- Requests with missing, conflicting or stale record data.
- Requests from each permitted role and from a role that should be denied.
- Requests for records in another domain or business unit.
- Requests that require a write tool and should pause for approval.
Run security and failure tests
- Permission-boundary tests: verify that an allowed user can perform only the documented actions and that masked roles do not regain broader access through a different tool or channel.
- Denied-access tests: attempt discovery and invocation with unauthorized roles, then attempt access to records, fields and domains outside the approved set.
- Tool-failure tests: simulate timeouts, malformed parameters, unavailable records and downstream errors. Confirm that the agent reports the failure without inventing a result or retrying an unsafe action indefinitely.
- Prompt-injection tests: place instructions in user text and retrieved content that conflict with the agent’s policy. Confirm that the agent treats untrusted content as data and does not reveal hidden instructions, credentials or unrelated records.
- Unsafe-action tests: ask for irreversible or high-impact changes. Confirm that the agent refuses, escalates or requests the configured human approval rather than executing directly.
- Human-escalation tests: verify the handoff destination, context supplied to the reviewer, user notification and recovery when no reviewer is available.
Capture evidence
Keep traces, approval decisions, prompts and outputs, tool results, denied attempts and incidents in the organization’s normal change, risk and incident records. Record the instance release, agent version or configuration revision, tester, date and expected outcome. Evidence should allow an auditor or incident responder to reconstruct what the agent was asked to do and which identity and roles were active.
Govern data domains and business-unit isolation
Use domain separation when multiple business units or tenants share an instance and require data or process isolation. Map each agent tool to the domains it may query or change, and test cross-domain requests explicitly. Do not treat a conversational instruction such as “only use my department’s records” as a substitute for platform enforcement. Domain rules, ACLs, table and field security, and role masking should agree; a gap in any one layer can expose data.
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Apply heightened review to consequential domains
Healthcare, finance, legal, employment, security and infrastructure use cases warrant additional review because an error can affect rights, safety, money or essential services. Apply the ServiceNow AI Acceptable Use Policy and your organization’s legal, privacy, security and records-retention requirements. Define prohibited decisions, required human reviewers, evidence retention and incident notification before enabling the agent. If the use case cannot provide accountable human oversight, narrow the scope or do not deploy it.
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For Agent2Agent connectivity, verify that the target instance is on the required Zurich patch 4 or later before enabling the connection. Confirm the release and patch in every participating instance rather than assuming that a family-level name is sufficient.
Connection prerequisites
- Use an authenticated connection and a credential alias managed through the platform’s approved secret process.
- Grant ACLs that control which agents or users may discover the external agent and which may execute it.
- Limit the exchanged data and tools to the documented use case; do not expose a broad administrative identity for convenience.
- Test authentication failure, expired credentials, denied discovery, denied execution, timeout and partial-response behavior.
- Assign an owner for the connection, credential rotation, patch review and incident response.
External connectivity creates a second governance boundary. Review both the local agent’s run-as permissions and the remote agent’s identity, ACLs, domain restrictions and approval policy. A local approval step does not automatically approve a remote action unless the integration contract explicitly preserves that control.
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Maintain an agent register
For each agent, keep a current record of purpose, owner, business sponsor, release and patch assumptions, data domains, tools, channels, triggers, invocation ACL, run-as identity, masked roles, approval requirements, external connections, test evidence, retention rules and disable procedure. Link material changes to a change request and record the date of the next review.
Control the lifecycle
- Design: classify data and actions, choose the least-privileged identity and define the human decision points.
- Build: implement the narrow purpose, tools, ACLs, role masking and channel configuration.
- Validate: run functional, boundary, injection, failure and escalation tests in a non-production environment where possible.
- Approve: obtain business, security, privacy and compliance sign-off appropriate to the risk.
- Release: activate through normal change control and monitor traces, approvals, incidents and unexpected denials.
- Review: reassess permissions, prompts, tools, model behavior, domain rules, credentials and business need after changes or on a defined schedule.
- Retire: disable triggers and channels, revoke credentials and roles, preserve required records, and communicate the replacement or end of service.
Use AI Control Tower at portfolio scale
When many agents, models and workflows must be governed together, evaluate AI Control Tower as ServiceNow’s centralized layer for governance, security, compliance and value tracking. Confirm what capabilities and packaging are available for your release and subscription. A central view is useful only when agents have complete ownership, risk classification, permission, approval and performance records; it does not replace least-privilege design or local testing.
Troubleshoot common setup failures
The New option or Save and test is unavailable
Confirm that the account has sn_aia.admin, that the feature is licensed and enabled in the target instance, and that you are in the correct scope and domain. If a selected tool requires another role, add that role only in the test or execution context where it is justified.
The agent appears in chat but cannot read a record
Check the run-as identity first, then the masked role set, table and field ACLs, domain restrictions and channel authentication. An invocation ACL can allow the conversation to start without granting data access.
A user can invoke the agent but an action is denied
Verify the tool’s required roles and conditions, the effective dynamic-user context or AI-user roles, and any approval requirement. Do not solve the error by granting a broad administrator role; identify the exact missing permission and document the least-privileged fix.
The agent performs an action without a reviewer
Inspect the tool’s supervision setting and the path by which it was called, including event triggers and external-agent calls. Sensitive tools should require approval in every path, and tests should include direct, triggered and remote invocation.
An external agent cannot be discovered or authenticated
Check that every participating instance meets the Zurich patch 4 or later prerequisite, then verify the credential alias, connection status, discovery ACL and execution ACL. Test with a deliberately unauthorized identity to ensure a successful connection has not created excessive reach.
Production-readiness checklist
- Licensing, release, patch and data-processing terms are verified for the target instance.
- An accountable owner and business sponsor are recorded.
- The purpose, prohibited uses, data domains and escalation path are documented.
sn_aia.adminand tool roles are limited to the people and execution contexts that need them.- The invocation ACL and run-as identity have been reviewed as separate decisions.
- Dynamic-user role masking or dedicated AI-user roles implement least privilege.
- Write, disclosure and critical-action tools require human approval where appropriate.
- Triggers, channels, retries and stop conditions are tested.
- Representative, denied-access, boundary, injection, failure and escalation tests pass with evidence retained.
- Domain separation and cross-domain behavior are verified.
- External-agent credentials, ACLs and Zurich patch 4-or-later prerequisites are verified when applicable.
- Monitoring, incident handling, change control, credential rotation and rollback or disable procedures are active.
- Portfolio reporting and compliance evidence are consolidated when the number of agents justifies a central governance layer.
Measure the agent with local evidence
There is no universal ServiceNow-published accuracy, adoption, productivity or return-on-investment figure that applies to every agent. Define instance-specific measures instead: task completion under approved conditions, correct refusal rate, unauthorized-access blocks, human-approval frequency and outcome, tool failure rate, escalation time, incident count, domain-boundary violations and the cost of operating the agent. Review these measures alongside qualitative feedback and trace samples, and change or disable the agent when its risk or behavior no longer matches its approved purpose.
Bottom line
Use AI Agent Studio to build the agent, but treat the build as the beginning of governance. Restrict invocation with an ACL, restrict execution with a carefully chosen run-as identity and role masking, supervise consequential tools, test hostile and failure cases, isolate domains where required, and keep ownership and evidence current. For external agents, enforce the Zurich patch 4-or-later, credential and ACL prerequisites. For a growing estate, centralize visibility with the governance capabilities available in AI Control Tower while preserving least-privilege controls in every individual agent.
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