Sandbase

Agent infrastructure for building production AI agents. Sandboxed runtime, tools, models, and APIs for developers.

[Sandbase @SandbaseAI]

We mapped 500 projects in the production AI agent infrastructure stack.

Runtimes, sandboxes, browser automation, MCP/tool protocols, memory, safety/evals, observability, model gateways, and deployment.

Corrections welcome:
https://t.co/vmX3RKy8BK

[Sandbase @SandbaseAI]

I run a small preflight before wiring in a public MCP server- endpoint reachable- tools/list returns usable schema- auth/quota assumptions clear
- read-only vs mutating calls separated- tool calls logged
- failure/denial behavior known
Discovery is easy. Runtime trust needs checks.

[Sandbase @SandbaseAI]

MCP servers make tools easier for agents to discover.Production agents still need a runtime preflight endpoint health schema validity auth/quota scope mutation boundaries logs and failure behavior.
Discovery is step one. Runtime trust comes next.

[Sandbase @SandbaseAI]

MCP makes tool access legible. Production agents also need runtime infrastructure: sandboxes, browser automation, memory, evals, observability, deployment.

We mapped 500 agent infra projects:
https://t.co/vmX3RKy8BK

[Sandbase @SandbaseAI]

@Cryptoakins99 Thanks! We are especially interested in real agent workflows that need stronger runtime boundaries: browser sessions, file/tool access, long-running jobs, logs, and replay after failure.

[Sandbase @SandbaseAI]

Published a practical checklist for AI agent sandbox runtimes.

The core question:

What can the runtime prove before execution, enforce during execution, and explain after execution?

Capability discovery, filesystem boundaries, network modes, lifecycle, audit, and integration surface all matter.

https://t.co/VNCeWKYNJV

[Sandbase @SandbaseAI]

@BatsouElef Exactly. For agent runtimes, the boundary has to be something the caller can interrogate.

The useful evidence is usually boring but decisive: declared capabilities before execution, policy version during execution, and an audit trail after denial or failure.

[Sandbase @SandbaseAI]

Day 15 build note:

We are turning agent sandbox research into a small runtime compatibility probe.

The questions are simple:
- what boundaries can the runtime prove?
- what capabilities can agents inspect before execution?
- what audit trail exists after tool use?

[Sandbase @SandbaseAI]

This matches what we see in runtime design too.

Less context helps, but long-running agents also need better runtime evidence: which tools ran, which policy version was active, what got denied, and what can be replayed after failure.

Otherwise the agent may use fewer tokens but still be hard to operate.

[Sandbase @SandbaseAI]

Production agents need a runtime control plane, not just a sandbox flag.

Useful questions:

- did the latest tool policy reach the executor?
- can the runtime prove the active policy version?
- are denied actions visible in audit logs?
- can a failed sandbox be cleaned up or replayed?

Sandboxing is useful when it is observable.

[Sandbase @SandbaseAI]

@rishflips Strong checklist. I’d add two preflight gates before the first run:

- capability discovery: what FS/network/process boundaries are actually supported?
- audit shape: what trace exists after a tool call fails or gets denied?

Sandbox safety gets much easier when it is testable.

[Sandbase @SandbaseAI]

@zatdex55 Mostly three kinds:

1. where builders want permission boundaries
2. which tool/runtime failures are hardest to debug
3. what traces or approvals would make them trust an agent in prod

Bug reports help, but repeated workflow friction is usually the roadmap signal.

[Sandbase @SandbaseAI]

The future agent runtime may look like this:

Cloud Brain, Local Hands.

The cloud agent handles reasoning, planning, knowledge, and memory.

Execution agents handle the real world:
web pages, desktop apps, and 3D environments.

The important layer is the protocol boundary between thinking and acting.

[Sandbase @SandbaseAI]

Strong take. I think the hard part is that a skill store cannot stay purely as a document registry. For agents that actually execute work, skill verification needs to connect the instruction, allowed tools, tool results, failures, and replay traces. Otherwise the skill compounds, but the operational evidence does not.

[Sandbase @SandbaseAI]

Great question. The most useful alpha for us is not feature requests in isolation, but concrete agent workflows that break in production: browser sessions that need isolation, tool calls that need permission boundaries, long-running tasks that need logs/replay, and places where model reasoning meets real execution. That is the roadmap signal we care about.

[Sandbase @SandbaseAI]

@iamlukethedev This is exactly the direction that makes computer-use agents feel practical: background execution without hijacking the user's machine.

The next layer will probably be runtime controls around permissions, session state, logs, and safe rollback when an action goes wrong.

[Sandbase @SandbaseAI]

@DolphinRoadster Cross-platform computer use feels like a key step toward the "local hands" layer for agents.

The interesting question is where the runtime boundary sits: cloud planner, local desktop runtime, browser extension, or a protocol layer between them.

[Sandbase @SandbaseAI]

@_shubhankar @lennysan @browserbase This is a great example of why browser agents are becoming useful: the agent found an external transcript path that was hidden in the workflow.

The next hard part is making that execution reliable: permissions, session state, logs, and replay when something goes wrong.

[Sandbase @SandbaseAI]

Most agent demos fail in production because they have a framework, but not a runtime.

I wrote down the runtime layer I think production agents need:

- durable state
- sandboxed tool execution
- resource limits
- lifecycle control

https://t.co/cwt2d7S45S

[Sandbase @SandbaseAI]

Production agents do not only need model inference.

They need two kinds of compute:

- GPU compute for reasoning
- isolated CPU/runtime compute for tools, browser sessions, shell commands, and files

The agent runtime is where those two worlds meet.