Top 9 Agentic Workflows | Rakesh Gohel

If AI Agents are complicated, then you can start with LLM workflows Here are a few of them you can try with code samples... Most theoretical AI Agent concepts are either too difficult to implement or something you don't exactly need right now. So, I collected 6+ Agentic workflows that are easier to build and solve a particular problem 📌 Prompt Chaining - Prompt chaining decomposes a task into a sequence of steps, where each LLM call processes the output of the previous one. 📌 Parallelization - Parallelization in LLMs involves sectioning tasks or running them multiple times simultaneously for aggregated outputs. 📌 Orchestrator-Worker - A central LLM dynamically breaks down tasks, delegates them to worker LLMs to synthesizes results. 📌 Evaluator-Optimizer - In this workflow, one LLM call generates a response while another provides evaluation and feedback in a loop. 📌 Routing - It classifies an input and directs it to a specialized followup task. This workflow allows for the separation of concerns. 📌 Autonomous Workflow - Autonomous workflow or Agents are typically implemented as an LLM performing actions based on environment/tools feedback in a loop. Note: For Prompt Chaining, Parallelization, Orchestrator-Worker, Evaluator-Optimizer, Routing, and Autonomous Workflow, you can find their code samples here: https://lnkd.in/gscuZ978 📌 Reflexion (Improved Reflection) - This architecture learns via feedback and self-reflection, reviewing task responses to improve the final response quality. - Use case: Full-Stacking App building agent (Eg; AI Agents like Lovable or Bolt new) 🔗 Langgraph Implementation: https://lnkd.in/g6zTCT86 📌 Rewoo (Reasoning Without Observation) - Agent enhances ReACT with planning and substitution, reducing tokens and simplifying fine-tuning. 🔗 Langgraph Implementation: https://lnkd.in/gy3wHusD 📌 Plan and Execute - An Architecture to create a multi-step plan, execute sequentially, review and adjust after each task.