arangate
Stateful runtime securityONE TASK / MANY STEPS / ONE STATE

One task.
Many steps.
One state.

Take your agents to production safely.
Keep real-time control over how they operate.

Every step informs the next decision.
Security that follows the work from start to finish.AI WORKFLOW / REAL-TIME ENFORCEMENT

01 / Why state matters

A safe action can
become unsafe.

The next decision depends on
what this task already did.

EXPLORE ONE WORKFLOW
Illustrative example
AGENT A / EARLIER STEP

Customer record read

Sensitive customer data entered the task.

AGENT B / SAME NEXT ACTION

Send an external request

A new agent continues the task.

STATEFUL DECISION

Block the action

Earlier sensitive read. External destination.

EXAMPLE POLICY

The same action is allowed or blocked based on earlier steps—even when another agent took them.

Sensitive path selected. Block the action.

The missing context

Remember the work.
Not just the worker.

A SINGLE-STEP CHECK

“Can this agent use this tool?”

A STATEFUL DECISION

“Should it act now,
given the whole task?”

State persists across steps and agent handoffs.

02 / Temporal policies

Policy for what
happened before.

Apply rules to the sequence of actions,
not one tool call in isolation.

01 / DATA MOVEMENTBLOCK
Read customer recordSend to external service

Earlier data changes the next permission.

Rule: If this task has touched sensitive data, block an outbound send to an unapproved destination.

APPLIED ATOutbound tool call, before execution
02 / AGENT HANDOFFBLOCK
Branch A reads secretBranch B tries export

A new agent does not reset the boundary.

Rule: Bind both branches to the same task state. Block export even when the acting agent did not read the secret.

APPLIED ATBranch B’s export request
03 / SHARED BUDGETLIMIT
Two branches request spendOne task budget

Parallel work shares one limit.

Rule: Reserve budget atomically before each action. A second branch cannot reuse what the first has reserved.

APPLIED ATEach spend attempt, before execution
ILLUSTRATIVE POLICY EXAMPLESExplore the Aran platform

03 / How it works

State carried.
Policy enforced.

Track the workflow across agents.
Decide at each action.

YOUR APPLICATION

AI workflow

Agent A → Agent B
Tool action

Arangate

Task state + policy
Runtime decision
Permitted action
YOUR ENVIRONMENT

Tools & data

APIs · services · systems
01 / CONNECTBring the action to a checkpoint.

Place a policy check before the tool executes.

02 / CARRY STATEKeep one task in view.

Include earlier steps and agent handoffs.

03 / ENFORCEAct on the decision.

Allow or block at the point of execution.

PROPOSED INTEGRATION MODEL

Use a checkpoint in your workflow to bring the proposed action, task state, and policy together.

Map your architecture with us

04 / Across the workflow

Three boundaries.
Real consequences.

Illustrative cases spanning
steps and agents.

01 / DATA MOVEMENT
Private dataExternal service

Keep sensitive data
where it belongs.

Remember a sensitive read, even when another agent sends the request.

BOUNDARY / INFORMATION
02 / TASK DRIFT
Read a reportDelete records

Keep the agent
inside the task.

Keep the task goal intact as work passes between agents.

BOUNDARY / INTENT
03 / PRIVILEGED TOOLS
Support taskAdmin action

Put powerful tools
behind clear limits.

Apply limits using the task’s earlier actions and current state.

BOUNDARY / AUTHORITY

05 / Built around your team

Build. Deploy.
Stay in control.

BUILDERS

Let agents share the work.

Keep guardrails with the task.

PLATFORM TEAMS

Carry state across handoffs.

Enforce at each action point.

SECURITY TEAMS

Set policy for the full task.

Make prior steps count.

Let’s talk agent security

Putting agents
into production?

Show us your agent workflow.
Let’s map state across the task.

Talk to the team 30 minutes · Book on Calendly