Agentic workflows represent a shift from single-shot LLM prompts to iterative, self-correcting, and multi-step reasoning processes where an AI agent uses tools to achieve a goal.
Key Components
- Planning: Breaking down complex tasks into sub-goals.
- Memory: Maintaining state and context across interactions (Short-term vs Long-term).
- Tool Use: Interaction with external APIs, databases, or code execution environments.
- Reflection/Self-Correction: Evaluating outputs and refining steps.
Frameworks & Patterns
- Spec-Driven Development (SDD): A methodology using formal specifications as a contract for agentic execution.
- LangGraph: A graph-based approach to defining stateful, multi-agent workflows.
- ReAct Pattern: Synergizing reasoning and acting in LLMs.
- Claude Code: Anthropic’s state-of-the-art agentic coding tool for repo-level modifications.
- SubAgent Delegation: Spawning isolated child agents to perform bounded tasks, solving context window exhaustion and token cost explosion at scale.
- Custom AI Workers: Authoring
.claude/agents/Markdown files to define reusable, version-controlled, specialist agents with their own system prompts, tool surfaces, model tiers, and persistent memory. See Claude SubAgents.