Building Reliable AI Agents

During shipping several LLM-powered agents to production, here's what I've learned about reliability.

Deterministic Guardrails

The biggest risk with LLM agents is non-determinism. The same input can produce different outputs — and different tool calls — on successive runs. This is unacceptable for production systems.

My approach: wrap every LLM decision point with a validation layer:

Structured output parsing (Pydantic schemas, not free-form JSON)

Parameter validation before tool execution

Confidence thresholds with fallback paths

State Management

LangGraph's stateful graphs are the right abstraction for agent workflows. The key insight is to design your state schema before writing any agent logic. Each step in the graph should have explicit input/output types.

I use a three-layer state model:

1. **Workflow state**: Current step, progress, errors

2. **Context state**: Accumulated context from previous steps

3. **Output state**: Final results and intermediate artifacts

Error Recovery

Agents will fail. The question is whether they fail gracefully. Every agent I build now has:

Retry policies with exponential backoff (max 3 retries)

Circuit breakers that escalate to human operators after N failures

Complete audit logging of every LLM call, tool execution, and state transition

The Right Abstraction Level

Not every task needs an agent. If a deterministic script can solve the problem, use that. Agents add complexity, latency, and cost. Reserve them for tasks that genuinely need reasoning, tool use, and multi-step planning.