Proposal: Agent-Native Compute Marketplace
Compute should become a discoverable resource for AI agents
Today’s AI agents are increasingly capable, but their practical capabilities are still constrained by the tools and compute resources explicitly attached to them.
I propose an Agent-Native Compute Marketplace that allows agents to dynamically discover, evaluate, acquire, and orchestrate authorized compute resources when a task requires capabilities beyond their currently available infrastructure.
The core idea
Think of compute as a resource that an agent can reason about and acquire, rather than infrastructure permanently attached to the agent.
Instead of:
Agent → Fixed Tools → Fixed Compute
we could have:
Agent → Resource Discovery → Evaluate → Acquire → Orchestrate → Verify → Release
For example:
User: “Generate this image.”
If the agent determines that additional GPU inference capacity is required, it could discover authorized GPU providers, compare them by latency, cost, capability and reliability, acquire capacity within the user’s permissions/budget, execute the workload, verify the result, and return it.
USER
│
▼
AI AGENT
│
▼
RESOURCE DISCOVERY
│
▼
COMPUTE MARKETPLACE
/ | \
/ | \
GPU Node A GPU Node B GPU Node C
\ | /
\ | /
EXECUTION LAYER
│
VERIFICATION
│
▼
AGENT
│
▼
USER
What makes this different from ordinary cloud computing?
The important change is who makes the resource decision.
Today, humans typically select infrastructure and configure it.
In this model, the agent could reason:
“I need 8 GPUs for 20 minutes.
My budget is $5.
Minimize latency while maintaining the required model capability.”
The agent could then select and orchestrate the appropriate authorized resources.
Security must be fundamental
This should never mean unauthorized access to third-party systems.
Participating compute providers would explicitly opt in and expose controlled execution environments.
Possible mechanisms:
- Cryptographic node identity
- Sandboxed workloads
- Capability-based permissions
- User-defined spending limits
- Per-task CPU/GPU/time limits
- Remote attestation where available
- Result verification
- Provider reputation
- Automatic isolation of anomalous nodes
Why this could matter
This architecture could eventually enable agents to dynamically obtain resources for:
- Image and video generation
- Large-scale inference
- Scientific simulations
- Data processing
- Rendering
- Fine-tuning
- Code compilation
- Parallel research
- Specialized hardware workloads
The long-term vision is an economy of compute for AI agents.
The agent does not need to own the infrastructure.
It simply needs permission to discover, acquire and orchestrate compute.
The question
I’d be very interested to know:
Is OpenAI already exploring an architecture where agents can dynamically discover and acquire authorized compute resources, rather than being limited to their permanently attached compute?
If so, what are considered the biggest technical, economic, and safety constraints?
If not, I believe this could be an interesting direction for the next generation of agent infrastructure.
TL;DR:
Compute should become an API-like resource that agents can discover, evaluate, acquire and orchestrate—within explicit security, permission and budget boundaries.