Docker, Adobe & tldraw: Where Should Your Agent Run?
Summary
The discussion explores the critical architectural question of where AI coding agents should execute, presenting four distinct models: Docker advocates for secure microVM sandboxes; Helix ML proposes centralized, dedicated computing resources for each agent; Adobe demonstrates running the agent loop entirely within the browser tab; and tldraw visualizes agents collaborating as characters on an infinite canvas. The consensus highlights the trade-offs between isolation, centralized control, and environmental fidelity.
Key takeaways
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MicroVMs for Agent Sandboxes
0:09
Docker recommends using micro VMs instead of traditional containers for agent sandboxes because enterprise security teams view shared kernels as an unacceptable isolation boundary.
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Centralized Agent Infrastructure
3:03
Helix ML argues for giving every agent its own dedicated computer on centralized infrastructure (e.g., Kubernetes) to facilitate seamless handoffs of work across global time zones.
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Browser-Native Agent Loops
7:33
Adobe demonstrated an agent that runs its entire loop and controls the browser from within the browser tab, showcasing the concept of the 'self-licking ice cream cone' (SLICC).
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Collaborative Canvas Agents
6:13
tldraw presents agents as interactive characters on a canvas that can coordinate, plan, and execute tasks as a team, allowing for simultaneous, visible collaboration.
Technical details
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Docker Sandboxing
143s
Docker's sandboxes use micro VMs to ensure hardware-level isolation, preventing access to the host file system and controlling all outbound requests via a networking proxy. Secrets injection mechanisms are used to provide necessary data without exposing private credentials.
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Centralized Agent Architecture
213s
The centralized approach suggests running agents on an organization's dedicated infrastructure (e.g., Kubernetes) rather than relying on individual developer machines, enabling global team handoffs.
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Browser-Based Agent Control
453s
Adobe's agent runs the entire agent loop within the browser, allowing it to control the displayed browser environment directly, rather than merely displaying results from a cloud runtime.
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Agent State Visualization
603s
tldraw allows agents to exist as instances on a canvas, visualizing their state (e.g., thinking, reviewing, working) and coordinating complex tasks like playing a game as a unit.
Mentioned resources
- Docker
- Helix ML
- Adobe
- tldraw
- SLICC
Channel & topics
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