Multi-Agent Orchestration: When One LLM Isn't Enough
Single-agent systems hit a ceiling. Here’s how we architect multi-agent systems that break complex operations workflows into coordinated sub-tasks — and what can go wrong.
AI Agent Engineering
We Build AI Agents
That Operate Your Business
Move beyond dashboards and alerts. We engineer autonomous AI agents that handle your operational workflows — order exceptions, supply chain decisions, financial reconciliation — with full auditability.
Order Exception Agent
847
exceptions resolved today
82% automated0 human touches
Cost Savings
$2.4M
annualized this month
What We Build
Production AI Agents for Enterprise Operations
Not POCs, not playbooks, not slide decks.
AI Agent Development
Custom AI agents built for your specific operational workflows. From order processing to supply chain optimization.
- Custom LLM integration
- Multi-agent orchestration
- Human-in-the-loop design
- Production deployment
Intelligent Data Pipelines
Transform raw operational data into structured, AI-ready formats with automated validation and enrichment.
- Real-time data processing
- Automated quality checks
- Schema evolution handling
- Monitoring & alerting
RAG Knowledge Systems
Retrieval-augmented generation systems that give your AI agents access to your institutional knowledge.
- Document ingestion pipelines
- Semantic search infrastructure
- Context-aware retrieval
- Continuous updates
AgentOps & Monitoring
Production observability for AI agents. Track decisions, costs, latency, and business outcomes in real time.
- Decision audit trails
- Cost optimization
- Performance dashboards
- Anomaly detection
AI Strategy & Roadmap
Executive-level AI strategy tailored to your operational context, competitive landscape, and technical constraints.
- Opportunity assessment
- Build vs. buy analysis
- Team capability planning
- Implementation roadmap
Our Methodology
Engineering Methodology
Every agent we build follows a four-layer architecture designed for production reliability.
01
Data Foundation
Clean, structured, real-time data pipelines that feed your agents accurate operational context.
02
Decision Engine
LLM-powered reasoning with tool use, retrieval, and multi-step planning tailored to your domain.
03
Action Layer
Agents that write back to your systems — ERP, WMS, TMS — not just surface recommendations.
04
Observability
Full audit trails, cost tracking, anomaly detection, and human override at every decision point.
From the Field
What Clients Say After Production
Real outcomes from AI agents deployed in enterprise operations.
Sow Analytics built us an order exception agent that handles 80% of our daily exceptions without human intervention. ROI was positive within 90 days.
We had tried two other AI vendors before Sow. The difference is they actually understand operations, not just machine learning.
The agent they built for our reconciliation process cut our close time from 5 days to 8 hours. That's real money.
The Ops Intelligence Brief
From the Blog
Practical writing on AI agents in enterprise operations.
Why Your RAG System Fails in Production (And How to Fix It)
RAG demos are easy. Production RAG is hard. Here are the five failure modes we see repeatedly when enterprise teams try to ship retrieval-augmented generation to production.
Cutting Agent Token Costs by 60% Without Hurting Quality
When your agent starts handling thousands of decisions per day, token costs add up fast. Here are the optimization strategies that work in production.
Ready to Move Beyond Dashboards?
Let's talk about what AI agents could automate in your operations — in 30 minutes, we can scope it.
47+
Agents Deployed
94%
Client Retention
3.2x
Avg ROI