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AI Systems Engineer Current
Robynn AI
At Robynn AI, I work across the production agent stack: orchestration services, reusable MCP workflows, structured report generation, memory infrastructure, and the observability needed to run long-lived AI systems reliably in production.
Key Achievements
- Architected a production-grade agentic AI platform combining a SvelteKit application with a Python/LangGraph backend, a multi-agent registry, 60+ tools, and deployed LangGraph workflows for multi-step reasoning, research, and execution.
- Standardized platform intelligence behind typed MCP / guided-workflow APIs, exposing reusable services for brand analysis, AI-search visibility, competitive intelligence, and website auditing.
- Redesigned multi-LLM report generation across OpenAI, Anthropic, Gemini, and Perplexity from direct HTML output to structured JSON + templates, improving runtime from roughly 320 seconds to 46 seconds, reducing run cost by roughly 40%, and pushing validation reliability near 100%.
- Productionized Graphlit-backed memory and brand-context infrastructure so downstream agents could operate on reusable organizational knowledge instead of stateless prompts, then shipped a LangGraph-backed conversational runtime for long-running execution, streaming, and artifact generation.
- Instrumented platform observability by normalizing app, widget, and LangSmith telemetry into admin dashboards for run tracing, model and tool usage, latency, and cost while reinforcing org-scoped auth, RLS, and connector security boundaries.