Building a Multi‑Expert Data Agent with OpenClaw: From Single‑Agent Answers to Collaborative Analysis

Enterprises face exploding data volumes while the speed of extracting insights remains stagnant; analysts waste hours locating tables and understanding fields before they can define analysis frameworks. A Data Agent aims to compress the "problem‑to‑insight" timeline from days to minutes by letting AI agents understand business semantics, autonomously explore data, and produce conclusions.

Why a Custom Data Agent?

Typical analysis requires identifying where user‑behavior data resides (data lake), where product data lives (data warehouse), and tracing attribution logic across teams. The manual effort of finding the right tables and interpreting schemas can consume half a day, pushing valuable work—hypothesis testing and decision making—to the end.

Multi‑Expert Collaboration Needs

Enterprise data is scattered across systems with differing tech stacks and business vocabularies. Complex analyses often need multiple domain experts: one to split tasks, others to query their respective data sources, and a coordinator to synthesize results. The proposed solution builds a customizable multi‑expert Data Agent team on top of OpenClaw, enabling cross‑domain collaboration.

Demo Scenario

A user asks, "@pm why is ROI dropping—user behavior change or product issue?" The processing flow is:

PM Agent (Alex) receives the question, decomposes it, and routes sub‑tasks to an Amazon expert (Nova) for user‑behavior data and a Snowflake expert (凌) for product data.

Both expert agents run in parallel, query their data lakes/warehouses, and return analysis results.

PM Agent aggregates the findings: roughly 75% of the ROI decline stems from product factors (channel traffic drop, localization gaps, low conversion), and about 25% from user‑behavior changes, each annotated with data evidence and impact ratings.

The user experiences a single natural‑language query while multiple specialist agents collaborate behind the scenes.

Architecture Overview

The solution runs OpenClaw on Amazon EC2 as a multi‑agent runtime, with each expert bound to an independent session. TeamAI serves as the front‑end, providing a multi‑agent chat interface and integrating with Amazon OpenSearch and Amazon Bedrock Knowledge Base.

Each agent is configured with a specific Amazon Bedrock model and skill set, such as Amazon Athena + Amazon S3 for data‑lake queries or Snowflake Cortex AI for warehouse analytics.

Core Configuration (Markdown‑Based)

# agents/pm/SOUL_PRIVATE.md
# Alex (PM) — only coordinates and manages tasks
Core rules:
 - On receiving a business question → return JSON command to assign to team, do not answer directly
 - On receiving "[team feedback]" → summarize based on message content only, no history lookup
Routing rules:
 - Use team member description + Neptune semantic judgment to assign to amazon‑expert or snowflake‑expert
 - If data location is uncertain → assign to both experts

# agents/amazon-expert/SOUL_PRIVATE.md
# Nova — senior Amazon solutions architect
Expertise: Amazon architecture, cost optimization, security compliance, data analysis
Tools: datalake_query (Athena + Glue)

# agents/snowflake-expert/SOUL_PRIVATE.md
# 凌 — senior Snowflake data architect
Expertise: Cortex AI / Cortex Agent / Semantic View, SQL optimization, data modeling
Tools: cortex-code

Two‑layer SOUL merging is supported at runtime: a global GLOBAL_SOUL.md defines shared behavior, while each agent’s SOUL_PRIVATE.md captures domain‑specific knowledge.

Continuous Operation Design

Adaptive Memory : Agents extract structured rules and query snapshots from tool‑call logs, storing them with weighted decay (rules never decay, snapshots decay after 30 days). Retrieval ranks items by weight × decay, ensuring high‑value knowledge persists.

Unified Semantic Layer : A Neptune‑backed knowledge graph synchronizes table schemas from Glue Catalog (data lake) and Snowflake Semantic Views (warehouse). Agents perform vector search to locate relevant tables, traverse join paths, and generate up‑to‑date SQL, supporting both YAML and graphical editing.

Human‑in‑the‑Loop : Before execution, the agent presents a structured understanding of the request with source citations; the user must confirm, preventing mis‑queries.

Future Work

Code‑Level Data Understanding : Current agents rely on metadata; the next step is to automatically parse data‑pipeline code to capture table generation logic, update frequency, and hidden constraints, enabling the agent to answer both "what is in the table" and "how the data was produced".

Systematic Evaluation : Presently answer quality is manually sampled. A standardized evaluation suite—question‑answer test sets for core business scenarios, automated regression checks after each iteration—will be built to benchmark agent performance.

Conclusion

The article demonstrates an OpenClaw‑based enterprise Data Agent that orchestrates multiple domain experts, leverages adaptive memory, and maintains a unified semantic layer, allowing natural‑language, cross‑domain data analysis. It is not intended to replace standardized products like Amazon Quick Suite, but to provide a customizable path for enterprises with deeper, bespoke requirements.