Build Autonomous Agents Using Prompt Chaining with AI Primitives

Core Idea

Autonomous agents don't need orchestration engines or heavyweight frameworks. Basic LLM calls chained together with conditional logic create scalable agentic systems.

Prompt Chaining Pattern

Break complex tasks into sequential prompts where each output feeds into the next. The three-stage pipeline example:

1. Summarize — Extract key points from raw input
2. Transform — Convert summary into target format (e.g., marketing copy)
3. Validate — Quality gate checks output meets criteria

Quality gates halt processing when outputs fall below thresholds (e.g., summary under 10 words). This prevents garbage from propagating downstream.

Key Takeaways

• Start simple: Basic LLM API calls plus if statements beat complex agent frameworks for most use cases
• Add gates: Validation checkpoints between stages catch bad outputs early
• Chain deliberately: Each prompt should do one thing well; let composition handle complexity

Connections

Complements agentic-design-patterns with a framework-free approach. Shares the "simplicity first" philosophy with 12-factor-agents.