AI agents are becoming the interface to our digital lives - but they’re still context-blind. By connecting ContextSDK to OpenClaw, we built an adaptive AI that understands how you’re moving through the world and adjusts execution in real time. Walking? Digest. Driving? Urgent only. Desk? Full interaction.

AI agents are getting smarter every week.
But they still behave as if you are always sitting calmly at a desk.
They don’t know if you're walking with your phone in your pocket, lying on the couch or actively focused at your desk. They treat every moment the same way.
We wanted to change that.
So Felix built a working prototype that connects ContextSDK (real-world mobile context - motion, attention, environment) to OpenClaw (a personal AI assistant running on your own computer).
The result: an AI agent that automatically adapts how it communicates and executes tasks based on what you're physically doing.
Walking? Voice messages and batched digests.
At your desk? Detailed text and interactive mode.
Driving? Urgent alerts only.
No manual toggling. It just works.

OpenClaw is a personal AI assistant that runs locally on your computer. It handles tasks, reminders, communications and workflows on your behalf.
The limitation of any AI assistant today is simple: it has no awareness of your physical world.
It doesn’t know if you are:
So it defaults to a single interaction model.
ContextSDK changes that.
Running as a companion app on iPhone with background processing enabled, ContextSDK continuously understands real-world context: mobility state, attention level and environment signals.
Through a native plugin architecture, this structured context is injected directly into OpenClaw’s runtime.
The integration means OpenClaw doesn’t just know what to do for you.
It knows how and when to reach you.

Throughout the day, interaction expectations shift dramatically. An AI agent should shift with them.


The integration is implemented as an OpenClaw plugin that subscribes to context changes from ContextSDK.
The flow works as follows:
The system message provides three layers of context-driven configuration:

When ContextSDK detects the user is walking (phone likely in pocket, screen brightness at 0%, low attention), the injected context triggers the following behavior:
Detected Context: Mobility: walking | Attention: low | Interaction Mode: hands_free Delivery Profile: text_plus_voice
Resulting Agent Behavior: Execution Mode: async_motion | Urgency: urgent_dual_channel_non_urgent_digest Sub-agent Scheduling: background_digest
In practice this means:
When the user returns to a desk environment, OpenClaw automatically switches back to full interactive mode with detailed text-based communication.
The agent doesn’t just detect context.It adapts execution.
Every AI assistant today is context-blind about the physical world. They don’t know if you’re walking with your phone in your pocket, lying in bed or actively focused at your desk. As a result, they treat every moment the same way.
That creates a disconnect:
This is more than a UX annoyance.
AI agents are quickly becoming the primary interface to our digital lives. Instead of opening apps, we increasingly delegate tasks directly to agents.
Peter Steinberger recently predicted that most apps will disappear in the age of AI agents. His argument is that many apps simply manage data and agents can handle that more naturally.
If that shift happens, agents don’t just assist apps. They replace them.
And if agents replace apps, they must understand timing.
An interface without awareness of physical context will always feel misaligned.
Without context:
With context: agents adapt.
This prototype shows that real-world context can become a native input to AI systems - not as a feature, but as infrastructure.
We built the integration as a native OpenClaw plugin that subscribes to ContextSDK context change events.
When a context change occurs, the plugin constructs a structured system message and injects it into the OpenClaw runtime.
From that point forward:
Every cron job, heartbeat, user prompt and sub-agent has access to the current context automatically.
The prototype runs on:
Walk away from your desk and the agent shifts behavior within seconds.
This is not theoretical.
It works end-to-end today.
We believe this is only the beginning of what context-adaptive AI can enable.
→ If you’d like to see it in action, watch our live demo of the OpenClaw × ContextSDK integration.
We're looking for ideas, collaborators and early adopters. If you're building with AI agents and want context-awareness baked in, or if you see applications for this technology that we haven't thought of yet, we'd love to hear from you via dieter@contextsdk.com.
Reach out - let's explore what's possible.