Enterprise AI Agent Architecture by SLPR Labs
Architecture Philosophy
Monolithic AI assistants try to do everything and do nothing well. Five Claws takes the opposite approach: specialized agents that each own a domain, coordinated by an intelligent orchestrator.
Each claw is purpose-built for a single domain. Narrow scope means deeper expertise, cleaner failure modes, and independent scaling.
Agents coordinate through a central orchestrator — sharing context, respecting priority, and avoiding conflicting actions. No rogue agents.
Every agent produces drafts, recommendations, and alerts — not final actions. Humans approve, agents accelerate.
Agent framework from NVIDIA's OpenClaw initiative (GTC26). Provides tool-use orchestration, memory management, and multi-agent coordination primitives.
NEMOCLAW ORCHESTRATOR
Central Agent Coordinator
Priority routing · Context sharing · Conflict resolution
C-01
Email Triage
C-02
Competitive Intel
C-03
BigQuery Reports
C-04
Digital Twin
C-05
Research & Portfolio
Automated executive inbox intelligence
Classifies, prioritizes, and drafts responses for executive inboxes. Uses NLP to understand intent, urgency, and required action — then generates contextually appropriate draft responses with tone matching.
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Always-on market surveillance
Continuously monitors competitor positioning, pricing changes, campaign launches, and strategic moves across ISP/telecom, crypto, and fintech verticals. Synthesizes signals into actionable briefs.
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Natural language to marketing insights
Translates natural language questions into optimized BigQuery SQL, executes queries, detects anomalies, and generates narrative insights. Democratizes data access for non-technical marketing teams.
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Simulated customer journey modeling
Creates probabilistic digital twins of customer segments and simulates campaign scenarios before deployment. Models "what-if" outcomes for budget allocation, channel mix, and messaging changes.
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Multi-source research aggregation — DEPLOYED
Aggregates research from multiple sources with full citation tracking. Currently running on MSI GE76 Raider via WSL2, migrating to Keebmon HX 370 (shipping end of April). The first claw to reach production.
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STATUS: LIVE
CURRENT_HOST: MSI GE76 Raider / WSL2
MIGRATION: Keebmon HX 370 (ETA: April 2026)
RUNTIME: Python + ChromaDB + n8n
UPTIME: Continuous since March 2026
Build Log
Documenting the process of building a multi-agent AI system from scratch.
Attended NVIDIA GTC26 and got hands-on with the OpenClaw/NemoClaw agent framework. The multi-agent orchestration primitives solve the exact coordination problems I've been working around for months. Decided to rebuild Five Claws on this foundation.
Research & Portfolio Intelligence Agent is the first claw to reach production. Running on MSI GE76 Raider via WSL2 with ChromaDB for vector storage and n8n for orchestration. Daily research digests are now automated.
Ordered the Keebmon HX 370 to replace the MSI GE76 Raider as the primary local inference and agent hosting machine. Shipping end of April. The AMD HX 370 APU should handle multi-agent workloads more efficiently than the current setup.
Documenting the orchestration patterns emerging from Five Claws development: priority-based routing, shared context windows, conflict resolution between competing agent recommendations, and human-in-the-loop approval workflows.
With the NemoClaw foundation proven on Claw 05, development begins on the remaining four agents. Email Triage and BigQuery Reporting are next in the pipeline, followed by Competitive Intelligence and Digital Twin.
Technical Paper
We present Five Claws, a multi-agent AI architecture designed for enterprise marketing operations. Built on NVIDIA's NemoClaw framework, the system decomposes the broad challenge of marketing intelligence into five specialized agents: email triage, competitive intelligence, data reporting, customer journey simulation, and research aggregation. Each agent operates within a narrow domain with deep expertise, coordinated by a central orchestrator that manages priority routing, context sharing, and human-in-the-loop approval workflows.
This paper describes the architecture decisions, orchestration patterns, and deployment considerations that emerged from building and deploying the first production agent (Claw 05: Research & Portfolio Intelligence) on consumer hardware via WSL2. We discuss the tradeoffs between monolithic AI assistants and specialized agent ensembles, and propose design patterns for enterprise adoption of multi-agent systems.
Keywords: multi-agent systems, NVIDIA NemoClaw, marketing AI, agent orchestration, enterprise AI architecture