Pure autonomous agents are unreliable in production. Agentic workflows codify the steps as a directed graph with deterministic edges; the LLM makes local decisions (extraction, classification, routing) at each node. Result: 80% of agent benefit at 10% of the unpredictability.
Conditional workflow with retry and human-in-the-loop escalation.
Anthropic's research: workflows with explicit steps beat autonomous agents on reliability and cost in 90% of business tasks.
Use Temporal, Inngest, or LangGraph checkpointer. Lets you resume mid-workflow after crashes — critical for long jobs.
Auto-escalate to humans when LLM confidence < threshold. Captures edge cases without crashing the workflow.
LLM calls fail. Wrap each LLM node with exponential backoff + circuit breaker; treat them like any flaky API.
Each step must be safe to re-run. Use idempotency keys for external calls (Stripe, send_email).
Track latency, cost, error rate per node. Hot spots reveal where to swap LLMs (cheaper) or add caching.
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