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.
MCP-Enabled Agentic Workflows
The Model Context Protocol transforms agentic workflows by giving agents direct access to external systems without custom integration code:
| Workflow | MCP Server | Automation |
|---|---|---|
| Design-to-Code | Figma | Paste Figma URL → Claude reads design → generates matching HTML/CSS/JS |
| PR Automation | GitHub | Commit → push → create PR → squash merge → delete branch (all from Claude) |
| Database Exploration | SQLite/PostgreSQL | Natural language queries replace manual SQL writing |
| Ticket-Driven Dev | Jira | ”Read this ticket and implement the feature” pulls specs directly |
| Incident Response | Slack | Read production errors from incident channels → find bug → fix |
| Live Documentation | Context7 | Always-current library docs, bypassing LLM training cutoff |
Context bloat warning: Each active MCP server loads tool descriptions into context at session start. Keep servers minimal to preserve model performance. See Claude + MCP Explained.