Building Agentic AI Systems at Scale

Building AI agents that actually work in production is fundamentally different from building a chatbot demo. At Stellar One, we've been shipping agentic AI systems for enterprise accounting — and I want to share some of the patterns that have worked.

The Agent Architecture

Most AI agent tutorials show you a simple loop: prompt → action → observation → repeat. In production, you need something more robust:

1. State management — Agents need to remember what they've done and why
2. Error recovery — When an action fails, the agent needs graceful fallbacks
3. Human escalation — Know when to hand off to a human

RAG Done Right

Our chat agents use RAG (Retrieval Augmented Generation) with a vector database. The key insight: your retrieval quality matters more than your model choice.

The best LLM with bad retrieval will be outperformed by a mediocre model with great retrieval.

We spent weeks tuning our chunking strategy and embedding model before touching the generation side.

Enterprise ETL Challenges

Building ETL-as-a-service for ERP data migration taught us:

• Schema inference is hard — legacy systems have undocumented quirks
• Data validation must happen at every step
• Incremental updates beat full refreshes for large datasets

What's Next

We're exploring multi-agent architectures where specialized agents collaborate on complex accounting tasks. More on that soon.

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If you're building similar systems, I'd love to chat — reach out on X.