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1.Landing Page ARIA: Gemini-powered enterprise intelligence agent with LangGraph orchestration and Arize Phoenix MCP evaluation.
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2. Query Execution Natural language business query routed through specialized agents for enterprise intelligence generation.
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3. Agent Execution Summary Confidence scoring, self-correction, validation, telemetry, and MCP traceability for every agent decision.
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4. Executive Report Header Generate executive-ready intelligence reports with confidence metrics, evaluations, and strategic insights.
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5. Executive Summary & Evidence Structured executive briefing with evidence quality assessment and knowledge-backed recommendations.
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6. Risk & Confidence Transparent confidence evolution showing self-correction impact and Arize MCP evaluation results.
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7. Technical Appendix Complete agent trace documenting orchestration flow, validation steps, telemetry, and confidence changes.
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8. Analytics Dashboard (Top) Interactive business intelligence dashboard visualizing revenue growth and sector-wise funding trends.
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9. Analytics Dashboard (Bottom) Sales pipeline analytics enabling executives to monitor deal value across business stages.
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10. Live Agent Trace Real-time observability showing routing, confidence shifts, latency, annotations, and MCP tool calls.
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11. Observability Dashboard (Top) Arize Phoenix MCP monitors latency, confidence timelines, and self-correction behavior in real time.
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12. Confidence Analytics Track confidence evolution and evidence quality across queries to improve agent reliability.
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13. Historical Evaluation Analytics Historical agent evaluation trends and execution telemetry powered by Arize Phoenix MCP.
Inspiration
Modern enterprises generate large volumes of structured and unstructured data, yet decision-makers often spend significant time gathering information from multiple systems before making strategic decisions. We wanted to build an AI-powered enterprise intelligence platform that can act as a team of specialized analysts, automatically retrieve evidence, validate findings, explain reasoning, and provide executive-ready insights.
Our goal was to combine Gemini's reasoning capabilities with multi-agent orchestration, retrieval-augmented generation, observability, and evaluation tooling to create a trustworthy enterprise decision-support system.
What it does
ARIA (Gemini Enterprise Intelligence Agent) is a multi-agent enterprise intelligence platform that transforms natural language questions into actionable business insights.
The system:
- Routes queries to specialized agents using intelligent orchestration.
- Supports enterprise analytics, strategic forecasting, knowledge intelligence, and anomaly detection.
- Uses Retrieval-Augmented Generation (RAG) to ground responses in enterprise knowledge.
- Generates executive intelligence reports with evidence-backed recommendations.
- Tracks confidence scores dynamically instead of relying on static confidence values.
- Applies self-correction when evidence quality suggests additional validation is required.
- Integrates Arize Phoenix MCP for evaluation, tracing, annotation, and observability.
- Provides live dashboards, agent traces, and confidence monitoring for transparency.
Example questions include:
- Which startup raised the most funding?
- What are the best practices for RAG architecture?
- Forecast revenue trends for our products.
- Detect anomalies in operational metrics.
How we built it
ARIA was built using a multi-agent architecture powered by Google Gemini.
Core components include:
- Gemini 2.0 Flash for reasoning and agent execution
- LangGraph for agent orchestration and routing
- Retrieval-Augmented Generation (RAG)
- FAISS vector retrieval
- BM25 lexical retrieval
- Reciprocal Rank Fusion (RRF)
- SQLite enterprise data layer
- Arize Phoenix MCP integration
- Plotly interactive analytics dashboards
- OpenTelemetry-based observability
The orchestration layer classifies incoming requests and routes them to specialized agents:
- Enterprise Analytics Agent
- Knowledge Intelligence Agent
- Strategic Forecasting Agent
- Risk Detection Agent
- Evidence Validation Agent
Results are validated, confidence-scored, traced, and visualized through executive reports and observability dashboards.
Challenges we ran into
Building trustworthy AI systems required more than generating answers.
Key challenges included:
- Designing reliable multi-agent routing.
- Combining structured analytics and RAG workflows.
- Developing dynamic confidence scoring mechanisms.
- Preventing unsupported conclusions through evidence validation.
- Integrating evaluation and observability workflows through Arize Phoenix MCP.
- Creating a transparent self-correction mechanism that improves confidence only when supported by additional evidence.
Accomplishments that we're proud of
- Built a complete multi-agent enterprise intelligence platform.
- Implemented dynamic confidence scoring rather than fixed confidence values.
- Added self-correction workflows driven by evidence quality.
- Integrated Arize Phoenix MCP for evaluation and traceability.
- Created executive-ready intelligence reports.
- Built real-time observability dashboards for agent monitoring.
- Developed an explainable decision-support experience with full trace visibility.
What we learned
This project reinforced that enterprise AI requires much more than model quality alone.
We learned the importance of:
- Agent orchestration.
- Evaluation-driven development.
- Observability and tracing.
- Retrieval quality.
- Confidence calibration.
- Human-readable explainability.
Building trustworthy AI systems requires strong validation, monitoring, and evidence-based reasoning in addition to powerful foundation models.
What's next for ARIA – Gemini Enterprise Intelligence Agent
Future improvements include:
- Integration with enterprise knowledge platforms and data warehouses.
- Support for multimodal enterprise intelligence workflows.
- Advanced agent memory and long-term context management.
- Real-time streaming enterprise analytics.
- Expanded evaluation pipelines through Arize Phoenix.
- Automated executive briefing generation and distribution.
- Deployment as a scalable enterprise intelligence platform on Google Cloud.
Our vision is to evolve ARIA into a fully autonomous enterprise intelligence copilot that continuously monitors business signals, validates evidence, and helps organizations make faster, more informed decisions.
