Building Agentic AI Systems That Actually Work in Production

AI agent demos are impressive. They chain LLM calls, use tools, and produce results that look magical. Then you deploy them and everything breaks — hallucinated function calls, infinite loops, cost explosions, and unpredictable behavior.

After building agentic systems for multiple clients, we've identified the patterns that separate demos from production-grade systems.

Every agentic system needs these guardrails from day one:

• Token budgets — hard limits on total tokens per agent run, not just per call
• Step limits — maximum number of tool calls before forced termination
• Output validation — structured output parsing with fallback strategies
• Human-in-the-loop — approval gates for high-stakes actions (payments, deletions, external communications)

Without these, your agent will eventually do something expensive, embarrassing, or both.

The most reliable pattern we've found is orchestrator + specialists. A lightweight orchestrator LLM routes tasks to specialized agents, each with a narrow tool set and constrained context. This prevents the "god agent" anti-pattern where one agent has access to everything.

Key principles:

• Each agent should have access to 3-5 tools maximum
• Use cheaper, faster models for routing; expensive models for reasoning
• Implement retry with exponential backoff at the agent level, not the API level
• Log every decision point for debugging — agent systems are notoriously hard to debug without traces

Agentic systems can burn through API credits fast. The practices that keep costs predictable:

• Caching — cache tool results aggressively; most tools return the same data within short windows
• Model tiering — use Haiku for classification, Sonnet for most tasks, Opus only for complex reasoning
• Early termination — if the agent has enough information, stop it from "exploring" further
• Batch processing — aggregate similar requests and process them together where possible