AI Agents for Research Perspective

The Challenge

Research analysts needed to query massive datasets, build visualizations, and detect patterns without depending on specialist engineering teams for every request.

System Architecture

Architecture & Approach

LangGraph supervisor pattern orchestrating specialized agents for NLP-to-SQL, semantic search, knowledge graph construction, Superset dashboard generation, RAG Q&A, and anomaly detection.

Implemented intent routing through a supervisor agent, introduced strict guardrails and retries, and deployed each capability as independently testable modules for reliability.

My Role & Contributions

Lead AI Agent Engineer designing the multi-agent architecture, orchestration policies, and production deployment flow through MLFlow.

Key Technical Decisions

• Used supervisor-based orchestration to isolate failures in a single sub-agent without collapsing the full workflow.
• Adopted graph-state execution so each user task could persist context across chained agent actions.
• Split retrieval, generation, and visualization steps to control latency and make debugging production traces easier.

Results & Impact

• Agents deployed to production and used by research analysts.
• Time-to-insight dropped from manual multi-step workflows to near real-time responses.
• New capabilities can be added by introducing new sub-agents without rewriting the core orchestration layer.

The platform moved from a static analytics workflow to an AI-assisted insight engine, increasing analyst throughput while preserving enterprise reliability requirements.

Lessons Learned

Production AI systems require strong orchestration semantics, observability, and fallback strategy. Demo-style prompt chaining is not enough at enterprise scale.