Continue with Google
See how the steps fit together.
Build a visible sequence of actions and decisions. Use a defined path for routine tasks and add AI reasoning where the work needs it.
Agent OS
Give your agents a clear path to follow.
Connect tools, reusable skills, and review points in one workspace. Turn a request into a workflow your team can understand and guide.
Agent OS
The pieces that keep your work connected.
Start with the way your team works. Connect the right tools, keep useful context close, and give every handoff a clear next step.
Graph-based execution
Build a visible sequence of actions and decisions. Use a defined path for routine tasks and add AI reasoning where the work needs it.
Unified building blocks
Bring reusable skills, tools, and triggers into the same workflow. Keep useful steps close so your team can adapt them to another process.
Multi-agent orchestration
Break a larger process into focused tasks. Connect agents through explicit handoffs so each one has the context for its part of the work.
Review and iteration
Review what happened, capture feedback, and update the instructions or supporting knowledge. Test the change before using it more widely.
Made for your workflowMore ways to move
More ways to move
your work forward.
Build once. Use it again.
Bring reusable skills, tools, and triggers into the same workflow. Keep useful steps close so your team can adapt them to another process.
Give each agent a clear role.
Break a larger process into focused tasks. Connect agents through explicit handoffs so each one has the context for its part of the work.
Make the next run better.
Review what happened, capture feedback, and update the instructions or supporting knowledge. Test the change before using it more widely.
Define a good result.
Set criteria for the task and compare outputs against them. Use examples and team feedback to identify where the workflow needs attention.
Keep people in the decisions.
Pause at the moments that need judgment. Give the reviewer the proposed action and its context before the workflow continues.
Choose the model for the task.
Discuss the supported model options and your data requirements. Compare results on representative tasks before choosing what fits your workflow.
Keep useful context close.
Attach the documents and working records a task depends on. Give agents a clear source to retrieve from and keep that information up to date.
Agent OS in focus
The pieces that keep your work connected.
Workspace / WorkflowsIllustrative preview
YOUR OPERATIONS, CONNECTED
Example workspaceEvery handoff. One clear flow.
Resident request · Maintenance coordination
01 · TRIGGERResident request received
A maintenance request enters the workflow.
Attach property & resident context
02 · PREPAREFind the right next step
Check the procedure. Prepare a vendor handoff.
Keep a person in control
03 · HUMAN REVIEWReady for your approval
Review the details before the workflow continues.
Context attached Every step recorded
Connected contextInnflow · Product illustration
Graph-based execution
See how the steps fit together.
Build a visible sequence of actions and decisions. Use a defined path for routine tasks and add AI reasoning where the work needs it.
- Connect steps
- Set conditions
- Review the path
Unified building blocks
Build once. Use it again.
Bring reusable skills, tools, and triggers into the same workflow. Keep useful steps close so your team can adapt them to another process.
- Reusable skills
- Connected tools
- Clear triggers
Multi-agent orchestration
Give each agent a clear role.
Break a larger process into focused tasks. Connect agents through explicit handoffs so each one has the context for its part of the work.
- Focused responsibilities
- Shared context
- Visible handoffs
Review and iteration
Make the next run better.
Review what happened, capture feedback, and update the instructions or supporting knowledge. Test the change before using it more widely.
- Review the result
- Record feedback
- Test improvements
Evaluation framework
Define a good result.
Set criteria for the task and compare outputs against them. Use examples and team feedback to identify where the workflow needs attention.
- Set expectations
- Compare outputs
- Review exceptions
Human-in-the-loop
Keep people in the decisions.
Pause at the moments that need judgment. Give the reviewer the proposed action and its context before the workflow continues.
- Choose review points
- Prepare the context
- Approve the next action
Model flexibility
Choose the model for the task.
Discuss the supported model options and your data requirements. Compare results on representative tasks before choosing what fits your workflow.
- Review requirements
- Compare results
- Choose a suitable model
Memory system
Keep useful context close.
Attach the documents and working records a task depends on. Give agents a clear source to retrieve from and keep that information up to date.
- Attach records
- Retrieve relevant context
- Keep knowledge current
Trusted by 1,260+ real estate professionals
Elena Morales
More focus on people, less on admin.
Elena Morales
Property manager28 properties“The people are my favorite part of this work, and I want the organization around it to support that. More focus on people, less on admin. The everyday details feel easier to approach with a clear head.”
Meera Shah
The next step always feels clearer.
Meera Shah
Leasing coordinator6 properties“There are days when every conversation seems to come with another follow-up. The next step always feels clearer. I like feeling prepared to pick things back up, even after the day takes a different turn.”
Daniel Cho
Rental admin feels much less overwhelming.
Daniel Cho
Rental property owner4 properties“I handle my rentals around the rest of my life, so I appreciate anything that makes the routine feel simpler. Rental admin feels much less overwhelming. It gives me a more comfortable way to approach the everyday work.”
Marcus Reed
I start each day with direction.
Marcus Reed
Property business owner46 properties“Running a property business means balancing the immediate work with the bigger picture. I start each day with direction. I value that feeling of being organized enough to give the right things my attention.”
Frequently asked questions
Direct answers on agents, memory, human review, deployment, and security, and fitting innflow into your operation.
What is Agent OS?
Agent OS is the execution layer behind innflow agents: graph-based workflows, reusable skills, tools, triggers, and review points in one system. It is how work is planned, run, and inspected.
How does graph-based execution work?
A flow is a graph. Nodes call tools, models, or data; branches follow conditions you define; merge points bring paths back together. Predictable steps stay explicit. Model reasoning is used only where you place it.
What memory does an agent keep?
Agents can use short-lived task context plus the knowledge, files, and records you connect. You choose the operational information to retain and review how it is used.
Can we choose the model?
Model choice is configured per agent and workflow. Switching models does not remove the need to validate outputs and keep humans on consequential steps.
How do humans stay in the loop?
Consequential steps can pause for approval. Agents prepare the next action with the attached context; a person reviews, edits, or rejects it before it continues.
How is innflow deployed?
Teams start in the innflow workspace, connect the systems involved in one operation, and expand from there. Talk with our team about the setup, timing, and hosting requirements for a private or custom deployment.
How does innflow handle security?
Access follows workspace permissions, connected-account scopes, and the approval points you place in a workflow. Our team can help you review the security documentation and hosting requirements for your proposed setup.
The Innflow platformKeep your tools, context,
Keep your tools, context,
and next steps connected.
Graph-based execution for innflow agents
Agent OS is the execution layer behind innflow agents: graph-based workflows, reusable skills, tools, triggers, and review points in one system. Predictable steps stay explicit. Model reasoning is used only where you place it.