Building Production-Grade AI Agents: Lessons from the Field

At Broadwing Labs, we've shipped AI agent systems across retail, healthcare, and financial services. Here's what we've learned about taking these systems from prototype to production.

1. Start with the Workflow, Not the Model

The biggest mistake teams make is choosing a model first. Instead, map the business workflow end-to-end. Understand the inputs, decision points, and outputs. The model is a component — the workflow is the product.

2. Design for Failure

Every AI agent will hallucinate, timeout, or produce unexpected output. Build guardrails:

• Input validation — Verify data quality before the agent processes it
• Output validation — Check the agent's response against expected schemas
• Fallback paths — Always have a human-in-the-loop escape hatch
• Retry logic — Implement exponential backoff for transient failures

3. Observability is Non-Negotiable

You can't debug what you can't see. Every production AI system needs:

• Request/response logging with trace IDs
• Token usage tracking for cost management
• Latency percentiles (p50, p95, p99)
• Error rate monitoring with alerting
• Human feedback collection loops

4. RAG Done Right

Retrieval-Augmented Generation is powerful but tricky to get right:

• Chunk size matters — Too small loses context, too large adds noise
• Hybrid search — Combine vector similarity with keyword matching
• Re-ranking — Don't trust the first retrieval pass; add a re-ranker
• Freshness — Stale embeddings = stale answers. Automate re-indexing.

5. Cost Control

LLM costs can spiral fast. Our approach:

• Use smaller models (GPT-4o-mini, Claude Haiku) for classification and routing
• Reserve larger models for complex reasoning steps
• Cache frequent queries
• Implement token budgets per request
• Monitor cost per user/transaction

6. Testing AI Systems

Traditional unit tests aren't enough. You need:

• Golden dataset testing — Curate 100+ input/expected-output pairs
• Regression testing — Ensure new model versions don't break existing behavior
• Adversarial testing — Test edge cases, prompt injection, and malicious inputs
• Human evaluation — Regular sampling and scoring by domain experts

Conclusion

Building production AI agents is 20% model selection and 80% engineering. The teams that ship successfully focus on reliability, observability, and cost control — not just accuracy benchmarks.

If you're building an AI system and want to get it to production faster, reach out to us. We've done this across multiple industries and can help you avoid the common pitfalls.