Agentic Workflows: Building Iterative AI Systems
The paradigm of AI interaction is shifting from simple, one-off prompts to “Agentic Workflows.” Instead of expecting a Large Language Model (LLM) to generate a perfect answer in a single try, agentic workflows use iterative loops to refine outputs.
Why Agentic Workflows Matter
In a zero-shot or single-shot scenario, the model has one chance to get it right. If it hallucinates or misses a detail, the process ends. Agentic workflows introduce:
- Reflection: The model reviews its own work and identifies errors.
- Tool Use: The model can search the web, run code, or query a database to ground its answers in fact.
- Planning: The model breaks a complex goal into smaller, manageable sub-tasks.
- Multi-Agent Collaboration: Different specialized agents work together (e.g., a “Coder” agent and a “Reviewer” agent).
Key Patterns
- Self-Correction: “Here is your draft. Review it for technical accuracy and rewrite the sections that are unclear.”
- Iterative Refinement: Generating a draft, gathering feedback, and revising multiple times.
- Hierarchical Planning: A manager agent delegates tasks to worker agents and aggregates the results.
Impact on Accuracy
Research has shown that an agentic workflow using a smaller model can often outperform a much larger model used in a simple zero-shot fashion. By allowing the model to “think” and “revise,” we unlock significantly higher reliability.