Choose an agent identity tool by asking whether it can show which AI agents exist, what each can access and at what level, who approved that access, and what each did. Then test whether it can enforce limits, not just report them. This guide is vendor neutral and ranks no products.
What is agent identity?
AI agents act on behalf of people or systems. They call tools, read data and sometimes change things. Agent identity is the practice of giving each agent a distinct, trackable identity with defined access, instead of letting agents share a person's login or a generic key. It answers a basic question: which agent did this, and was it allowed to?
Why is it different from user identity?
Agents are created quickly, may run unattended, and can spawn other agents. They often need short lived, narrowly scoped access rather than standing permissions. A tool built only for employees' accounts may not fit that pattern, so evaluate agent specific behaviors.
Criteria that matter
Discovery. Can the tool find agents already running, including ones teams set up without telling security? Ask what sources it uses: code repositories, gateways, cloud accounts, tool servers. An inventory you must fill by hand will be out of date.
Distinct identities. Does each agent get its own identity, rather than reusing a human token or shared service account?
Scoped, short lived credentials. Can access be limited to specific tools and actions, and expire quickly? Ask how credentials are issued and rotated.
Access levels and approvals. Can you see and set what an agent may do (read, write, delete, deploy) and require a human approval for sensitive actions? Check for a record of who approved.
Enforcement point. Where are limits applied: in the agent code, at a proxy in front of tools, or at the target system? Enforcement outside the agent is stronger because the agent cannot bypass it. Ask what happens when the tool is unreachable: does it fail open or closed?
Audit trail. Is every tool call logged with agent identity, input summary, outcome and time? Can logs be exported to your log system and kept for the period your auditors expect?
Protocol coverage. Does it work with the way your agents reach tools, such as the Model Context Protocol, and with your cloud and SaaS targets?
Lifecycle. Can you disable one agent quickly, and see what it had access to? Can you retire agents that have gone unused?
Integrations. Identity provider, secrets store, ticketing and monitoring.
Exit. Can you export identities, policies and logs?
How to test
Set up a constructed scenario with a test agent that can read a repository and open pull requests. Ask the vendor to show: the agent appearing in inventory, its access list, a denied action (such as trying to merge), an approval flow for a sensitive action and the log trail for the whole session. Then disable the agent and confirm it loses access immediately.
Red flags
- Only a spreadsheet style register with no enforcement.
- Agents sharing credentials with humans.
- Logs that cannot be exported.
- No answer on fail open or fail closed.
Where Safeguard fits
Safeguard tracks agent identity, meaning which agent holds what access at what level, and offers MCP Guardrail, a proxy for MCP servers, along with policies enforceable at build, CI and release. Its autonomous remediation is off by default and categories start on manual approval. An AI Gateway or LLM firewall is in early access. Compare any option, including ours, against the criteria above.
What does a good rollout look like?
Resist enforcing everywhere on day one. Start in observe mode: connect discovery sources and build the inventory of agents, tools and access, without blocking anything. The first output is usually a surprise list of agents nobody planned for.
Next, pick two or three high value agents, such as one that touches production data, and move them to distinct identities with scoped credentials and approvals on destructive actions. Keep a rollback path in case a limit breaks a legitimate workflow.
Then widen. Set a rule that new agents must register before getting credentials, and review the inventory monthly for agents unused for a set period, which you can retire. Track simple numbers: share of agents with a named owner, share using scoped credentials, and approvals granted versus denied.
Involve engineers early. If limits feel arbitrary, people will route around them with personal tokens, and your inventory becomes fiction.
What to do
- List the agents and tools in use today, including unofficial ones.
- Decide which actions need human approval.
- Write your top five criteria from the list and weight them.
- Run the constructed scenario with each shortlisted option.
- Check log export and exit terms before signing.
Quick check
Why not let agents use the developer's own credentials?
Because you lose attribution and least privilege. Actions look like the person's, and the agent inherits all their access.
What is the strongest enforcement point?
A control outside the agent, such as a proxy or the target system, since the agent cannot simply skip it.
Sources
This guide reflects general identity and access practice. No external standard was fetched for it, so check your own policies and any current protocol specification before relying on specifics.
Last reviewed: 18 October 2026.
