Runtime and Orchestration
Agentic RAG, parallel multi-agent orchestration, unified context, and dynamic Skill routing, with about 7s end-to-end latency under 7k+ average conversation-token load.
Platform Focus
End-to-end ownership across architecture evolution, core runtime, shared platform capabilities, production quality operations, and team delivery, accountable for reliability, efficiency, cost, and business outcomes.
Platform capability mapLed the evolution from code-guided Agent 1.0 to model-driven 2.0, defining the boundary between model decisions and Runtime control.
Built Agentic RAG, parallel multi-agent orchestration, unified context, dynamic Skill routing, and MCP/API tool systems.
Established multi-tenant isolation, tool permissions, audit, human handoff, idempotent retries, and high-risk action controls.
Connected production traces, replay evaluation, release gates, and data feedback to turn production issues into knowledge, Skill, and policy improvements.
Led the Agent squad in core platform delivery and used an AI development harness to establish consistent engineering governance across 40 repositories.
Connecting architecture decisions, platform capabilities, and team delivery through production outcomes.
Agentic RAG, parallel multi-agent orchestration, unified context, and dynamic Skill routing, with about 7s end-to-end latency under 7k+ average conversation-token load.
MCP/API, tool gating, multi-tenant permissions, audit, side-effect controls, human handoff, and an observable execution ledger.
Continuous optimization through replay evaluation, content gaps, and usage auditing, reducing token consumption by 60% and improving auto-resolution by about 30%.
Led a 3-person Agent squad within a 20-person product-engineering team; expanded the AI development harness across 40 repositories.
Agent platforms, model serving, and large-scale platform engineering.
Owned a cross-industry AI Agent customer-service SaaS platform from architecture evolution through production operations and expanded the team AI development harness across 40 repositories.
Owned web engineering, microservice decomposition, model-serving call chains, and platform operations for 0-to-1 generative AI SaaS delivery.
Owned high-traffic cross-platform web engineering, micro-frontends, ad attribution, logging SDKs, JsBridge, and performance optimization across Alibaba Taobao and Local Services.
Connects production traces, issue cases, evaluation samples, and preference data so online behavior becomes a continuous quality improvement mechanism.
Turning production traces into replay evaluation and release gates.
Built reproducible Agent regression evaluation from production traces for prompt, tool, and model changes.
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Focused on Agent Runtime, tool governance, evaluation release gates, and core workflow integration.