Enterprise AI Re-Skilling & Internals
Prompt Chaining vs Agent Swarms
Prompt chains give you predictable, auditable LLM workflows; agent swarms give you adaptive, parallel problem solving at the cost of control. The right choice depends on how much ambiguity your task has and how much you can afford to debug when things go wrong.
Points clés
- Use prompt chains when the steps are known and the order is fixed.
- Use agent swarms when the problem decomposes into independent subtasks that need different tools.
- Keep chains observable: log each step's input, output, and token cost.
- Cap swarm fan-out and depth to prevent runaway cost and infinite loops.
- Give every agent a narrow tool set and a clear stopping condition.
- Prefer deterministic routing between steps over free-form agent handoffs.
- Measure end-to-end accuracy and cost per task, not just per call.
- Start with a chain and promote parts to agents only where adaptivity pays.
Questions fréquentes
When should I use a single prompt instead?
When the task fits in one context window and needs no tools; chains and swarms add latency and failure modes you may not need.
Are agent swarms production-ready?
Yes for bounded tasks with strong evals; no for open-ended goals without supervision and cost guards.
How do I debug a swarm?
Trace each agent's messages, tool calls, and decisions, and replay with fixed seeds or recorded tool outputs.
Which is cheaper?
Chains are usually cheaper and more predictable; swarms spend tokens on coordination and retries.
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