Streaming Tool Interception & JSON Extraction
Real-Time Tool Invocation in Low-Latency Streams
When an agent model calls a tool, it outputs a specialized JSON payload. In production harnesses, waiting for the model to finish generating the entire JSON block before executing the tool introduces unacceptable latency.
Stream Interception Architecture
- The harness monitors the incoming token stream for tool signature triggers (e.g.
<tool_call>or OpenAI function call blocks). - A streaming JSON parser validates argument keys as tokens arrive.
- If the tool requires an external network request, the harness can initiate speculative network pre-connects based on parsed arguments.
- Upon tool completion, results are injected back into the message sequence as a role-delimited observation block.
Per-Rollout RL Sandbox Isolation (September 2026)
When training or serving autonomous agent workflows via Reinforcement Learning (RL / GRPO), multiple rollouts run concurrently. Production harnesses now enforce single sandbox per rollout isolation (Firecracker microVMs or lightweight gVisor sandboxes). Each rollout runs inside a strictly isolated, ephemeral environment, completely preventing cross-rollout file contamination, race conditions, or host privilege escalation.
Configure real-time stream interception to parse tool arguments on-the-fly and fire speculative pre-connects.