Production AI Agents for Research Analysts
Production AI agents that perform NLP-to-SQL conversion, NLP-based searches, Neo4j knowledge graph construction, auto-build dashboards on Apache Superset, RAG-based Q&A on chart data, and anomaly and pattern detection.
Confidentiality Note: Details have been generalized to respect employer confidentiality and non-disclosure agreements, while preserving the core system architectures, trade-offs, and business impacts.
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
Production
Deployment Status
Multi-Agent
Architecture Pattern
Hours -> Seconds
Insight Latency Shift
- 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.
Related Technical Writing
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