I am focused on the operating layer around AI agents: execution reliability, audit trails, tooling distribution, observability, and AI-native development workflows.

• Building durable execution systems for production AI agents.
• Designing AgentOps infrastructure: traces, metrics, logs, replay, governance, and human approval.
• Turning team workflows into skills, prompts, pipelines, and reusable agent systems.
• Shipping practical developer tools that make agents easier to inspect, trust, and operate.
• Bringing 10+ years of backend architecture and reliability experience into AI-native products.

Before moving deeply into AI-native systems, I spent years building and operating backend platforms where reliability was not optional: payments, booking, marketing campaigns, observability platforms, business middle platforms, and high-concurrency services.

That history shapes how I build AI systems now:

• I treat agents as production systems, not just prompts.
• I care about audit trails, rollback paths, monitoring, and data safety.
• I prefer workflows that teams can repeat, review, and improve.
• I still enjoy hard backend problems: concurrency, consistency, storage, release safety, and operational visibility.

Durable execution and reliability runtime for production AI agents.

• Tracks agent execution as auditable ledgers.
• Makes long-running agent workflows easier to replay, inspect, and recover.
• Focuses on reliability, governance, and operational visibility instead of demo-only agent behavior.

A distribution layer for prompts, agent scripts, and MCP servers.

• Treats agent capabilities as versioned, installable assets.
• Supports local runtime setup, metadata indexing, and repeatable installation.
• Designed for teams that need shared AI capabilities without copy-paste drift.

A CLI that gives agents web reach across public sources such as GitHub, Reddit, YouTube, Bilibili, XiaoHongShu, and more.

• Built for agents that need external context without bespoke API integrations.
• Optimized for practical search and reading workflows.

Interactive knowledge graphs for codebases.

• Turns repositories into explorable graphs.
• Helps developers and agents understand unfamiliar systems faster.
• Built around the idea that graphs should teach, not just impress.

An AI coding agent environment for the terminal.

• Focuses on local harnesses, tools, LSP, subagents, and safer edit workflows.
• Explores what a practical AI-native developer environment should feel like.

• Backend architecture: 10+ years designing distributed systems, business platforms, and high-availability services.
• Reliability engineering: observability, alerting, release gates, graceful startup/shutdown, backup and recovery.
• Team leadership: 7+ years across technical governance, engineering workflow design, and cross-functional delivery.
• Production scale: hands-on experience with high-concurrency systems, large traffic events, and multi-team platforms.
• AI engineering: agents, RAG, MCP, LangGraph-style workflows, tool orchestration, and AI-assisted development pipelines.

• Orange Digital Technology: led architecture work and built an observability stack with Prometheus, SLS, Grafana, and N9E; pushed monitoring coverage, alerting, and reliability governance.
• Huanxin Network: built a business middle platform from scratch, supporting multiple business lines and large-scale daily traffic.
• JD Technology: worked on large-scale marketing and coupon systems for major promotional campaigns.
• VIPKID: led booking and class-management architecture, coordinating with multiple teams around high-traffic scheduling scenarios.
• Duolabao: designed core payment systems, OAuth2 open platform capabilities, data synchronization middleware, and data platform work.

• Scalable architecture: distributed systems, high-concurrency services, and business platform design.
• Reliability first: observability, incident response, graceful deploys, release gates, and backup/recovery.
• AI infrastructure: agents, RAG, MCP, AgentOps, skill distribution, and AI coding pipelines.
• Product-minded engineering: turning fuzzy product requirements into systems that can ship and be operated.
• Team enablement: making engineering workflows explicit through docs, checklists, automation, and reusable tools.

• GitHub: github.com/yaogdu
• Email: yaogdu@gmail.com
• Open to discussing: AI agents, AgentOps, developer tooling, backend reliability, and AI-native engineering workflows.