AI agent governance
Give each agent a governed path to tools and models
Halo connects a distinct agent identity to brokered model and tool access, then adds policy decisions, human approvals, budget checks, runtime boundaries, and records for operators to review.
The challenge
Agent access crosses several control points
An agent can request a model, call tools, and take actions inside a runtime, so teams need identity and controls that follow the workflow.
Shared identity hides the actor
When an agent acts through a user or shared service account, reviewers can have less context about which workflow made a request.
Tool access can outlast the task
MCP servers and app connections can expose actions beyond the specific capability a workflow needs.
Decisions need a usable record
Policy outcomes, approvals, runtime events, and costs are difficult to review when they sit apart from the agent identity.
How Halo solves it
Tie identity to agent activity
The workflow names the agent, governs its model and tool paths, adds human review, and keeps the resulting records available to operators.
Govern
Agent Identity and Access
Give each agent a distinct identity before it reaches a gateway. Model and tool decisions can then carry the identity of the workflow that needs review.
Secure
MCP and Tool Gateway
Route configured MCP and tool calls through the separate MCP and tool gateway, where identity and tool-level policy can govern the action path. This establishes the tool route alongside model access.
Secure
Model Gateway
Route model requests through Halo’s separate model gateway, where identity, policy, budget checks, and evidence apply to model traffic. With both routes defined, teams can choose actions that need human review.
Secure
Human Approvals
Hold higher-impact actions for a human decision and record the approval or denial. Approved activity continues within the configured runtime boundaries.
Secure
Secure Agent Runtime
Run agents in sandboxed, non-root workloads with default-deny network egress and explicit file, network, process, credential, and model boundaries. Operators can quarantine or release a runtime when needed.
Govern
Audit and Evidence
Bring gateway decisions, approvals, costs, and configuration changes into reviewable records. Export audit logs and gateway decisions in CSV or JSON for operational review.
Who's involved
Coordinate the people behind each agent
Builders define the workflow, while security and platform owners set its limits and operating responsibilities.
AI builders
Often starts here
Name the agent owner, choose model and tool paths, define the actions the workflow needs, and test the configured capabilities before launch.
Security leaders
Set structured policy outcomes, identify higher-impact actions for review, and inspect gateway decisions and runtime events.
IT and platform teams
Connect the model and tool routes, coordinate runtime configuration, and document who can quarantine or release an agent.
Suggested pilot
Suggested agent governance pilot
This is a suggested plan, and timing depends on your environment, agent paths, credentials, and app connections in scope.
- Week 1
Choose an agent workflow
Set up the pilot scope with one agent, its owner, model route, tool calls, and intended actions. At the end of the week, teams can show the agreed workflow and the access questions it raises.
- Weeks 2 and 3
Configure identity and gateway paths
Set up a distinct agent identity, model and tool gateway routes, and scoped capabilities with credentials bound at the gateway. At the end, teams can show the configured identity, routes, and selected permissions.
- Week 4
Add policy, approval, and runtime controls
Set up structured policy, human approval for selected higher-impact actions, and the secure runtime boundaries in scope. At the end, teams can show which decisions require review and how operators can contain the runtime.
- Week 5
Review records from test activity
Set up a review of a test run, then examine gateway decisions, approvals, costs, and configuration changes. At the end, teams can show a sample of recorded activity and the evidence export used for follow-up.
Outcomes
What you can show after the pilot
An agent tied to a named identity
Show the pilot agent, its owner, and the model and tool routes configured for its work.
Access with defined review points
Show scoped capabilities, selected approval gates, and the runtime boundaries configured for the workflow.
Records from a test run
Show gateway decisions and approvals from test activity, plus any related cost and configuration records available for export.
FAQ
Questions, answered
Halo keeps agent identity separate from user identity. Gateways can apply policy and record decisions in the context of the agent making a model or tool request.
Halo has separate model and MCP or tool gateways. The MCP and tool gateway governs configured tool traffic, while model requests use the separate model gateway.
Provider and app credentials stay bound at the gateway. Agents receive scoped capabilities and do not see raw keys.
Yes. Halo supports human approval gates for higher-impact actions, and the resulting approval or denial is recorded as evidence alongside gateway activity.
MCP is an open protocol introduced by Anthropic in November 2024 for connecting AI applications to tools and data. Halo’s MCP and tool gateway governs configured tool traffic with identity, policy, approvals, budgets, and evidence.
The Halo connection catalog lists 1,000+ apps, and available actions vary by app. Review a connection’s setup and scope before assigning it to an agent workflow.
Plan your AI agent governance pilot
Bring one real use case to a walkthrough. We will map what you need to see, the decisions you need to make, and the evidence your team will review.