Prompt engineering for LLMs remains complex, with existing frameworks either hiding complexity behind restrictive APIs or providing inflexible canned patterns that resist customization —making sophisticated agentic programming challenging. We present the Prompt Declaration Language~(PDL), a novel approach to prompt representation that tackles this fundamental complexity by bringing prompts to the forefront, enabling manual and automatic prompt tuning while capturing the composition of LLM calls together with rule-based code and external tools. By abstracting away the plumbing necessary for such compositions, PDL aims at improving programmer productivity while providing a declarative representation that is amenable to optimization. Through a real-world case study, we demonstrate PDL's expressivity and practical utility for implementing complex agentic prompting patterns, showing substantial performance improvement (4x) over conventional approaches.