Agentic Workflows: How to Chain AI for Success

Definition: A dynamic process where an agent autonomously plans and executes actions to achieve goals, involving reasoning, invoking external tools, and self-correction.

The “Plan-Act-Observe” Loop

An Agentic Workflow is the specific pattern of behavior an AI follows to solve a problem. It’s not just “one shot” generation; it’s a cycle.

1. Plan: The AI breaks the user’s request into steps (e.g., “1. Read file, 2. Import library, 3. Write function”).
2. Act: It executes the first step (writes the code).
3. Observe: It runs the linter or compiler to see if it worked.
4. Correct: If there’s an error, it self-corrects before showing you the result.

Why This Matters for Vibe Coding

Most “bad” AI code happens because the model didn’t check its work. Agentic workflows force the model to “think before it speaks” and “test before it commits.”

• Reflection: The workflow includes a step where the AI critiques its own code.
• Tool Use: The workflow allows the AI to “go look up” the documentation if it forgets a syntax.

Designing Your Own Workflows

Advanced vibe coders use tools like LangGraph or CrewAI to build custom workflows.

• Example: “When I push a commit, have Agent A review it for security, Agent B generate unit tests, and Agent C update the documentation.”

The “Vibe” Implication

This allows you to stay in the “flow.” You don’t stop to write tests; you have a workflow that does it in the background. You don’t stop to check for unused imports; the workflow cleans them up.

Key Takeaway

Don’t settle for “raw” LLM output. Use tools that wrap the LLM in a workflow. The difference between GPT-4 (raw) and GPT-4 (in a workflow) is the difference between a dictionary and a writer.