AI EXECUTION & RUNTIME SECURITY

The execution
runtime for
AI agents.

Give agents a better environment to work in. Run concurrent sessions, carry forward compact context, and recover without rebuilding execution history—with anomaly detection and containment built into the runtime.

Request a pilot Explore the platform
Efficient executionContinuityRuntime security
J/ EXECUTION CONTROLIllustrative scenario
build-agent-14healthy
JoyMux boundaryjm-8F32A
POLICY ENGINEAgent identity bound
CPU 24% Memory 1.8 GB Network Observed Filesystem Brokered
✓ Read workspace· Privileged action
CPUMemoryProcessesInfrastructure
EVENT 01

Agent starts with an attributable execution session.

Identify

Example policy flow. Enforcement depends on surface and configuration.

BUILT FOR AI THAT OPERATES

Coding agentsEnterprise automationPrivate AIAutonomous workloads
A BETTER ENVIRONMENT FOR AI

Less repeated work.
More useful execution.

Agents need more than a terminal. JoyMux gives them persistent execution, compact context, and shared workspace facts—so they spend less effort recovering state and repeating work.

Keep execution alive

Durable sessions separate the work from the client. Reconnect to the same execution history instead of rebuilding it from terminal output.

Persistent sessions · PTY workers · Event cursors

Run independent work in parallel

Concurrent process and PTY sessions keep input, output, and lifecycle state separate so independent jobs can progress together.

Concurrent sessions · Isolated streams · Structured state

Send the context that changed

Bounded delta views and explicit Context Capsules give agents compact catch-up information instead of repeatedly copying full histories.

Workspace deltas · Bounded context · Per-agent cursors

Avoid duplicate effort

Shared workspace facts and advisory warnings reveal overlapping edits, repeated tests, duplicate processes, and stale file state.

Workspace state · Handoff packets · Overlap warnings

Keep output moving

Disk-backed output and resumable reads let slow clients catch up without blocking process output or keeping the full history in memory.

Bounded memory · Durable output · Resumable reads

Give every harness the same foundation

Connect agents to a shared local execution protocol through hooks, CLI, or SDKs. Each harness keeps its own reasoning and planning.

Vendor-neutral runtime · Local APIs · Rust, Python & TypeScript

Where the efficiency comes from: less history to resend, fewer tasks to reconstruct, and independent work that can run concurrently. Measure the actual effect on your workload.

Explore the runtime
THE EXECUTION GAP

AI agents are
becoming operators.

They execute commands, modify files, call APIs, and run continuously. Model safeguards are one part of the system. Control over execution is another.

01
MODEL LAYER

What AI thinks and says

Prompts · Reasoning · Model outputs

02
EXECUTION LAYER JOYMUX

What AI actually does

Processes · Actions · Authority · Evidence

ONE RUNTIME. SIX RESPONSIBILITIES.

Govern AI where
execution happens.

Meet the platform
01

Observe

See processes, resource use, execution events, and failure signals in one runtime context.

Structured events · Process visibility · Runtime telemetry
02

Attribute

Connect execution to an agent, workload, session, and authorization scope.

Agent identity · Session binding · Audit correlation
03

Control

Evaluate brokered actions against explicit policy before granting authority.

Scoped grants · Action mediation · Policy decisions
04

Detect

Detect abnormal resource use, repeated failures, and unexpected execution activity. Attribute the signal to the affected agent and workload.

Resource anomalies · Failure signals · Abnormal activity
05

Contain

Contain abnormal workloads by stopping managed execution, cleaning up its process group, and revoking brokered authority.

Cancellation · Process cleanup · Authority revocation
06

Recover

Reconnect to durable sessions and replay execution evidence after interruption.

Persistent events · Resumable reads · Recovery evidence
IDENTITY BEFORE AUTHORITY

Every agent needs
an identity.

Anonymous processes are a poor foundation for autonomous work. Connect actions to the agent, workload, session, and scope behind them.

Understand the security model
build-agent-14Example workload identity
Bound
Workload
backend-migration
Environment
enterprise-vm-03
Session
jm-8F32A
Authorization
Workspace read / write
Privileged actions
Approval required

Illustrative configuration; not a live deployment.

WHEN BEHAVIOR CHANGES

Control the consequence.
Investigate the cause.

A bug, an unexpected model response, or a malicious tool output can lead to the same runtime problem. Identify the workload, evaluate authority, and retain the evidence needed to investigate.

  1. 01 Detect abnormal execution
  2. 02 Attribute the agent and session
  3. 03 Deny unsupported brokered authority
  4. 04 Stop managed execution and preserve evidence
Explore runtime security
J/ EXECUTION CONTROLIllustrative scenario
build-agent-14healthy
JoyMux boundaryjm-8F32A
POLICY ENGINEAgent identity bound
CPU 24% Memory 1.8 GB Network Observed Filesystem Brokered
✓ Read workspace· Privileged action
CPUMemoryProcessesInfrastructure
EVENT 01

Agent starts with an attributable execution session.

Identify

Example policy flow. Enforcement depends on surface and configuration.

BENEATH THE REASONING LAYER

A runtime, inside
your infrastructure.

Keep your agent framework. Connect its execution to a shared session and policy layer, with clear boundaries around what the runtime can enforce.

01 / AGENT LAYER
Coding agentsAutonomous workersAI workflows
02 / JOYMUX RUNTIME
IdentityConcurrent sessionsWorkspace stateCompact contextDurable outputPolicy & grantsAction mediationTelemetryAnomaly detectionContainmentAudit & recovery

Resource and network enforcement require supported deployment controls.

03 / INFRASTRUCTURE
Operating systemProcesses & filesCompute & network

Conceptual architecture. Confirm platform and integration support for your environment.

CONTINUITY IS PART OF CONTROL

Security and reliability
meet at the runtime.

Explore recovery
01Healthy
02Fault detected
03Execution stopped
04Evidence preserved
05Recovery reviewed
06Work resumed
WHERE AUTONOMY MEETS INFRASTRUCTURE

Built around the work agents do.

Coding agents

Give coding tools scoped, attributable execution.

Enterprise automation

Govern agents inside operational workflows.

AI infrastructure

Observe managed workloads across runtime environments.

Private AI

Keep execution under customer infrastructure controls.

AI operations

Investigate failures and abnormal resource behavior.

Secure agent deployment

Evaluate identity, policy, and auditability together.

EVIDENCE BEFORE CLAIMS

Test it against
a real workload.

Evaluate deployment effort, runtime behavior, and the controls that matter to your infrastructure. Agree on success criteria before the pilot begins.

Discuss a 14-day technical pilot
CONTROL WHAT COMES NEXT

AI agents are getting more authority.
Your infrastructure needs more control.

Request a technical pilot Explore the architecture