Agentic Coding

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Based on the user's interest in agentic coding, Claude Code workflows, PRD documentation patterns, and enterprise AI adoption best practices, here are the relevant summaries: Best practices for AI agent instructions focus on separating static facts from dynamic inputs, making agents call out missing information, forcing planning before action, and using deterministic branching rules. Testing with 10-20 sample evaluations before shipping and iterating based on failures rather than feelings can improve success rates from 30% to 80%. Better instructions beat complex architecture. For enterprise adoption, the framework distinguishes between tasks to automate (fully describable with clear right answers), augment (clear process needing judgment), consult (too nuanced to delegate), and own (requiring expertise and relationships). Start with one agent and scale based on learning and confidence. Cost per correct outcome, not token usage, should drive ROI conversations with CFOs. Design fast, cheap lanes for routine decisions and slow, expensive lanes with deep reasoning for high-stakes cases, treating agent objectives like compensation plans for digital workers. On-demand tool loading and progressive disclosure patterns significantly improve agent performance. Load only the tools and instructions an agent needs at each moment rather than preloading everything. Refactoring skill files from 200-400 lines to 20-line checklists with externalized detailed documentation in PATTERNS.md and CONTENT_TYPES.md files reduces cognitive load. Modular, contextually conditional guidelines with conditions and actions load only when relevant, keeping the LLM focused on 3-4 pertinent rules instead of juggling 50, dramatically improving instruction-following. Knowledge graphs need runtime context assembly. Most enterprise failures come from handing agents the entire knowledge graph. Instead, build dynamic context graphs at runtime scoped by the decision moment—mixing permanent facts with live state like seat counts, weather, and timers. A dozen well-selected nodes beats thousands the agent must search.