Built a unified knowledge and memory layer for multi-tenant business AI Agents, injecting trusted knowledge, conversation facts, user preferences, and task state on demand.

RAG

Unified ingestion, chunking, embeddings, retrieval, and reranking for documents, QA, websites, attachments, and product data; added model-driven Agentic RAG for retrieval planning, query rewriting, source selection, and iterative evidence gathering.

Memory

Built tenant-isolated session and user Memory with fact extraction, confidence filtering, write/update, and retrieval injection for persistent conclusions, preferences, and task state.

Led model-driven AI Agent 1.0 from 0 to 1 in a SaaS business, evolving a Dify workflow into a controllable multi-tenant AI Agent platform for marketing, ecommerce shopping assistance, lead identification, data collection, qualification, business queries, and human handoff, with repeatable delivery across industries.

Configuration and controlled reasoning

Built a configuration-driven Agent factory with tenant- and Agent-level customization across Instructions, Actions, knowledge bases, Skills, MCP, language style, and human handoff; used Guidelines, Journeys, and ARQs to govern rules, multi-turn workflows, and critical decisions.

Engineering controls and runtime assurance

Used an Agent Harness to standardize engineering rules, tool boundaries, recovery, and release validation, backed by an observability console for conversation flows, tool calls, quality, latency, and cost.

Quality operations

Established pre-release evaluation, production replay, issue attribution, and cost analysis, comparing task success, tool accuracy, latency, and token cost by tenant, scenario, and version to support continuous optimization and 70%+ auto-resolution.

Led an internal AI engineering delivery harness and CLI platform across 40 repositories, using shared context to connect development, testing, release, deployment, verification, and operations while consolidating a long multi-role workflow into a developer-led AI delivery loop.

Engineering and testing convergence

Unified requirements, implementation, unit and integration tests, case reproduction, and pre-acceptance checks through a shared CLI, reusable Skills, and engineering checks; engineers owned and automated most test execution while QA focused on high-risk scenarios and quality gates.

End-to-end AI delivery loop

Connected build, release, deployment, environment checks, regression verification, production observability, and incident diagnosis through AI-driven commands and Skills that execute and write results back into shared context.

Collaboration efficiency

Shared repository rules, service dependencies, environment configuration, and Spec/EPIC context through Skills, MCP, and CLI, reducing handoffs, verbal synchronization, and wait states; standardized delivery and production issue resolution reached roughly 10x the prior collaboration model's efficiency.

Built a multilingual unknown-question clustering and knowledge-gap learning platform that turned unresolved questions into actionable themes and candidate knowledge across discovery, synthesis, tenant adoption, and write-back.

Algorithm pipeline

Unified multilingual cleaning, embeddings, UMAP/HDBSCAN, BERTopic/c-TF-IDF, and LLM clustering with SentenceTransformer and OpenAI-compatible interfaces.

Engineering platform

Built an async Python, FastAPI, Pydantic, and MongoDB platform with concurrency controls, dynamic configuration, webhook retries, experiment tracking, and quality metrics.

Knowledge evolution and outcomes

Evolved HDBSCAN into LLM clustering to identify knowledge gaps and learn candidate answers from high-quality human conversations; about 50% of clusters entered production optimization, high-frequency coverage reached 70%, and auto-resolution improved 30%.

Designed a controlled conversational Agent framework from 0 to 1 for enterprise consultation and complex tasks, separating business rules, workflow state, tool execution, and response generation into governable layers for reliable long-running conversations.

Rules and workflow model

Modeled local business rules as condition-action Guidelines selected by context, while Journeys carried branching and backtracking multi-turn flows between free-form conversation and deterministic SOPs.

Attentive reasoning

Applied Attentive Reasoning Queries (ARQs) as domain reasoning blueprints that reinstate constraints around intent, rule applicability, tool choice, parameter provenance, and response compliance.

Production execution framework

Built a configuration-driven Agent factory, session runtime, RAG and tool integration, async execution, evaluation, and observability, bringing rules, tools, and model versions into release governance and replay.