Inference Optimization

• Drive TTFT below 400ms for multi-step agent pipelines
• Streaming optimization: first token to user while sub-agents are still running
• KV cache strategy, prompt compression, dynamic context window management
• Multi-provider routing: model selection by latency, cost, and task type across OpenAI, Anthropic, Gemini, and open-weight models

Agent Architecture

• Design and implement Plan-Execute-Synthesize pipelines that run sub-agents in parallel DAGs, not sequential chains
• Build reliable orchestration on top of Temporal: retries, timeouts, partial failure recovery, idempotency
• Structured output enforcement: JSON schema validation, retry loops on malformed LLM output, graceful degradation
• Tool call design: schema design that LLMs actually follow reliably across providers

Evaluation & Harness

• Own the eval framework end to end: ground truth dataset...