AI analyst multi-agent system
Project summary
A modular multi-agent system that turns natural-text questions into direct business answers in almost no time. Task routing, data-driven reasoning, SQL generation, and concise, executive-ready narratives – all built into one.
Now, weeks of effort (and expenses) are compressed into days by using multi-agent capabilities.
Services:
Project overview
Our client – a global advisory company that works with private equity investors and their portfolio companies. The kind of work where ambiguity is expensive.
- Their services: due diligence, technology and exit consulting, digital transformation, interim leadership
- Their expertise: data analysis, technology ecosystems, business-aligned strategy, high-stakes evaluation
The client didn’t need a chatbot – they needed a solution that wouldn’t get lost in complex internal datasets. That’s where we got into it.
We joined to turn a sprawling data environment into something people could actually use with confidence.
Main goals
- Reliable analytics by turning natural-language queries into accurate business insights
- Low-risk update to expand business-critical capabilities without disruption
- Increased reliability – all grounded in extracted schema metadata & relationships
- Reduced workload – no more wasted resources
The problem
In the advisory landscape, data access isn’t just about convenience – it is about trust and speed under pressure. In this particular scenario, every response must stay tightly controlled – no guesswork, no room for risk.
At the same moment, the system in place was causing extra complexity:
- Ambiguous queries often required additional clarification, contextual understanding, or decomposition
- Data spread across sources and connected through external foreign keys
- Mixed modalities (JSON and relational tables) was complicating SQL generation
- A large and domain-specific schema surface was impeding manual processing
The solution
Delivered framework
We implemented a modular multi-agent system that turns natural-language queries into clear business insight. The architecture is separating the responsibilities across specialized agent systems.
We covered:
- Task orchestration: to analyze user intent and coordinate agents end-to-end
- Schema navigation: to ground every response in extracted schema metadata
- SQL drafting: to translate user intent into elaborated SQL queries
- SQL guardrails: to detect unsafe patterns and enforce safety rules
- And summarization: to package the results into clear business narratives (with numbers & assumptions)
Ongoing improvement & scaling
Moving further, to support existing capabilities and harden overall reliability, we are now extending the system. That includes both additional control layers and specialized agent roles.
In particular:
- Methodology support: to select the approach for queries going beyond data retrieval
- Business-domain alignment: to translate the terms to canonical business metrics & dimensions
- Clarification handling: to resolve ambiguous inputs
- Quality testing: to validate obtained outputs
- And observability: to monitor agent behavior and improve its quality
Forget about the sifting through tables and validating the joins, forget rewriting and adding additional context. What’s left is analyzing the findings and forming a conclusion – no routines.
Talking about value delivered, the results now arrive in days, not weeks.
Tools & technology stack
AI agents:
- Amazon Bedrock AgentCore
Vector search:
- Amazon OpenSearch Service
Data querying:
- Amazon Athena
- AWS Glue
API layer:
- Amazon API Gateway
Application & orchestration:
- Python-based services (AWS Lambda)
Observability & audit:
- Amazon CloudWatch
- AWS X-Ray
- AWS CloudTrail
Timeline
- Foundation & multi-agent skeleton: wrapped up
- Stabilization & query reliability: wrapped up
- Further expansion: still ongoing
Team
- Solution architect
- LLM/Agent engineers
- Data engineer
- QA/AQA engineer
- Delivery lead
Value delivered to business
We provided a way to turn data complexity into efficient everyday workflows without losing execution control. No digging to get the insight standing behind a strategic, defensible decision.
Less guesswork, more impact.
What does this mean?
- Data-backed decision-making
- Reduced time, reduced cost
- More trust
- Higher scalability