Common agentic patterns: ReAct (Reasoning + Acting — the agent alternates between thinking about what to do and taking actions), Plan-Execute (create a plan upfront, then execute each step), and Reflection (generate output, critique it, then improve it). More complex patterns include hierarchical agents (a planner agent delegates to specialist agents) and multi-agent debate (agents argue different perspectives to reach better conclusions).
Agentic workflows depend on tools: web search, code execution, file operations, API calls, database queries. Without tools, an agent is just a model talking to itself. The quality of tool definitions (clear descriptions, well-typed parameters, good error messages) directly affects agent performance. Poorly defined tools lead to wrong tool choices, incorrect parameters, and cascading errors.
The biggest challenge with agentic workflows is reliability. Each step has some failure probability, and failures compound across steps. Production agentic systems need: error handling (what happens when a tool call fails?), guardrails (what actions require human approval?), observability (logging every step for debugging), budget limits (maximum tokens/cost per workflow), and graceful degradation (return partial results rather than failing completely). The gap between impressive demos and reliable production systems is large.