Execution control for AI agents
Let AI agents act within limits you control.
Phalanx sits between your AI agents and the business systems they can change. It checks each protected action against the task, permissions, current system state, and limits you set before a connector can use the credentials to carry it out.
Current demo: Meridian baseline, before Phalanx enforcement.
The agent proposes
Send a follow-up to the customer on this support case.
Phalanx checks
- Authorized task
- Allowed recipient
- Current case state
- Remaining task limits
The protected connector may act.
Resolve the missing condition.
This action is outside the rules.
Each protected action gets its own decision and recorded outcome.
Once an agent can act, mistakes can leave the conversation.
An AI agent can use software tools to do work. That might mean replying to a customer, changing a record, or starting the next step in a workflow. A wrong recipient, stale record, or repeated action can turn a plausible plan into a business problem.
Phalanx gives those actions a separate control point. Your application defines the authority. Your rules determine what can proceed.
Keep the task in scope
Define the resources, destinations, and actions this task is allowed to use.
Keep limits across the workflow
Apply shared counts and budgets across protected tool calls in the same task.
Know what happened
Inspect the decision, execution status, and verification outcome for a protected action.
Every protected action has to earn permission.
Phalanx controls the route from an agent's proposal to an action in your systems.
- 1. Set the taskYour application supplies the authenticated task and the maximum authority available to it.
- 2. Check the proposalPhalanx evaluates the requested action against your rules, current state, evidence requirements, and remaining limits.
- 3. Control executionA permitted action receives a one-use permit. A protected connector rechecks the conditions before using its credentials.
- 4. Record the resultPhalanx records the outcome and verifies the effect where the connected system supports it. Uncertain outcomes remain visible.
Start with the action you need to control.
The right starting point is a real workflow with clear permissions and an owner who can change how its tools execute.
Customer support
Keep updates and follow-up messages within the assigned case, customer, and communication limits.
Business operations
Control which records an agent may read or change, and check the relevant state before an operation runs.
Approvals and exceptions
Let eligible work continue while actions that need approval or stronger evidence wait for resolution.
Workflow examples illustrate the control model. Connector support and the protected routes are confirmed during evaluation.
Explore Phalanx in a business you can understand.
Meridian Cloud is a synthetic software business built for the Phalanx demonstration. Its customer portal gives you a concrete setting for exploring an AI agent's work and the controls around consequential actions.
Start with the guided introduction. It explains your role, the workflow, and the current demonstration status before you enter Meridian.
The Meridian environment is open for exploration with synthetic business data. Phalanx enforcement is not active in the public demo yet.
Keep the execution boundary in your environment.
Phalanx's core execution controls run inside the customer boundary. You choose the connected systems and any external model or evidence services. The agent does not hold the production credentials for the actions protected by the gateway.
An action is protected when its credentialed route is enrolled behind Phalanx and equivalent routes around that control have been closed.
Which action would you trust your agent to take next?
Bring one workflow, the tools it uses, and the limits that matter. We can work through where Phalanx fits and what an evaluation should prove.